First spin-resolved electron distributions in crystals from combined polarized neutron and X-ray diffraction experiments.

PubMed

Deutsch, Maxime; Gillon, Béatrice; Claiser, Nicolas; Gillet, Jean-Michel; Lecomte, Claude; Souhassou, Mohamed

2014-05-01

Since the 1980s it has been possible to probe crystallized matter, thanks to X-ray or neutron scattering techniques, to obtain an accurate charge density or spin distribution at the atomic scale. Despite the description of the same physical quantity (electron density) and tremendous development of sources, detectors, data treatment software etc., these different techniques evolved separately with one model per experiment. However, a breakthrough was recently made by the development of a common model in order to combine information coming from all these different experiments. Here we report the first experimental determination of spin-resolved electron density obtained by a combined treatment of X-ray, neutron and polarized neutron diffraction data. These experimental spin up and spin down densities compare very well with density functional theory (DFT) calculations and also confirm a theoretical prediction made in 1985 which claims that majority spin electrons should have a more contracted distribution around the nucleus than minority spin electrons. Topological analysis of the resulting experimental spin-resolved electron density is also briefly discussed.

Measuring the Density of States of the Inner and Outer Wall of Double-Walled Carbon Nanotubes.

PubMed

Chambers, Benjamin A; Shearer, Cameron J; Yu, LePing; Gibson, Christopher T; Andersson, Gunther G

2018-06-19

The combination of ultraviolet photoelectron spectroscopy and metastable helium induced electron spectroscopy is used to determine the density of states of the inner and outer coaxial carbon nanotubes. Ultraviolet photoelectron spectroscopy typically measures the density of states across the entire carbon nanotube, while metastable helium induced electron spectroscopy measures the density of states of the outermost layer alone. The use of double-walled carbon nanotubes in electronic devices allows for the outer wall to be functionalised whilst the inner wall remains defect free and the density of states is kept intact for electron transport. Separating the information of the inner and outer walls enables development of double-walled carbon nanotubes to be independent, such that the charge transport of the inner wall is maintained and confirmed whilst the outer wall is modified for functional purposes.

Rocket radio measurement of electron density in the nighttime ionosphere

NASA Technical Reports Server (NTRS)

Gilchrist, B. E.; Smith, L. G.

1979-01-01

One experimental technique based on the Faraday rotation effect of radio waves is presented for measuring electron density in the nighttime ionosphere at midlatitudes. High frequency linearly-polarized radio signals were transmitted to a linearly-polarized receiving system located in a spinning rocket moving through the ionosphere. Faraday rotation was observed in the reference plane of the rocket as a change in frequency of the detected receiver output. The frequency change was measured and the information was used to obtain electron density data. System performance was evaluated and some sources of error were identified. The data obtained was useful in calibrating a Langmuir probe experiment for electron density values of 100/cu cm and greater. Data from two rocket flights are presented to illustrate the experiment.

System for tomographic determination of the power distribution in electron beams

DOEpatents

Elmer, John W.; Teruya, Alan T.; O'Brien, Dennis W.

1995-01-01

A tomographic technique for measuring the current density distribution in electron beams using electron beam profile data acquired from a modified Faraday cup to create an image of the current density in high and low power beams. The modified Faraday cup includes a narrow slit and is rotated by a stepper motor and can be moved in the x, y and z directions. The beam is swept across the slit perpendicular thereto and controlled by deflection coils, and the slit rotated such that waveforms are taken every few degrees form 0.degree. to 360.degree. and the waveforms are recorded by a digitizing storage oscilloscope. Two-dimensional and three-dimensional images of the current density distribution in the beam can be reconstructed by computer tomography from this information, providing quantitative information about the beam focus and alignment.

System for tomographic determination of the power distribution in electron beams

DOEpatents

Elmer, J.W.; Teruya, A.T.; O`Brien, D.W.

1995-11-21

A tomographic technique for measuring the current density distribution in electron beams using electron beam profile data acquired from a modified Faraday cup to create an image of the current density in high and low power beams. The modified Faraday cup includes a narrow slit and is rotated by a stepper motor and can be moved in the x, y and z directions. The beam is swept across the slit perpendicular thereto and controlled by deflection coils, and the slit rotated such that waveforms are taken every few degrees form 0{degree} to 360{degree} and the waveforms are recorded by a digitizing storage oscilloscope. Two-dimensional and three-dimensional images of the current density distribution in the beam can be reconstructed by computer tomography from this information, providing quantitative information about the beam focus and alignment. 12 figs.

Monitoring nonadiabatic avoided crossing dynamics in molecules by ultrafast X-ray diffraction

DOE PAGES

Kowalewski, Markus; Bennett, Kochise; Mukamel, Shaul

2017-05-26

We examine time-resolved X-ray diffraction from molecules in the gas phase which undergo nonadiabatic avoided-crossing dynamics involving strongly coupled electrons and nuclei. Several contributions to the signal are identified, representing (in decreasing strength) elastic scattering, contributions of the electronic coherences created by nonadiabatic couplings in the avoided crossing regime, and inelastic scattering. The former probes the charge density and delivers direct information on the evolving molecular geometry. The latter two contributions are weaker and carry spatial information through the transition charge densities (off-diagonal elements of the charge-density operator). Furthermore, simulations are presented for the nonadiabatic harpooning process in the excitedmore » state of sodium fluoride.« less

Dynamics of electron injection in a laser-wakefield accelerator

NASA Astrophysics Data System (ADS)

Xu, J.; Buck, A.; Chou, S.-W.; Schmid, K.; Shen, B.; Tajima, T.; Kaluza, M. C.; Veisz, L.

2017-08-01

The detailed temporal evolution of the laser-wakefield acceleration process with controlled injection, producing reproducible high-quality electron bunches, has been investigated. The localized injection of electrons into the wakefield has been realized in a simple way—called shock-front injection—utilizing a sharp drop in plasma density. Both experimental and numerical results reveal the electron injection and acceleration process as well as the electron bunch's temporal properties. The possibility to visualize the plasma wave gives invaluable spatially resolved information about the local background electron density, which in turn allows for an efficient suppression of electron self-injection before the controlled process of injection at the sharp density jump. Upper limits for the electron bunch duration of 6.6 fs FWHM, or 2.8 fs (r.m.s.) were found. These results indicate that shock-front injection not only provides stable and tunable, but also few-femtosecond short electron pulses for applications such as ultrashort radiation sources, time-resolved electron diffraction or for the seeding of further acceleration stages.

System for tomographic determination of the power distribution in electron beams

DOEpatents

Elmer, J.W.; Teruya, A.T.; O'Brien, D.W.

1995-01-17

A tomographic technique is disclosed for measuring the current density distribution in electron beams using electron beam profile data acquired from a modified Faraday cup to create an image of the current density in high and low power beams. The modified Faraday cup includes a narrow slit and is rotated by a stepper motor and can be moved in the x, y and z directions. The beam is swept across the slit perpendicular thereto and controlled by deflection coils, and the slit rotated such that waveforms are taken every few degrees form 0[degree] to 360[degree] and the waveforms are recorded by a digitizing storage oscilloscope. Two-dimensional and three-dimensional images of the current density distribution in the beam can be reconstructed by computer tomography from this information, providing quantitative information about the beam focus and alignment. 12 figures.

Validation of COSMIC radio occultation electron density profiles by incoherent scatter radar data

NASA Astrophysics Data System (ADS)

Cherniak, Iurii; Zakharenkova, Irina

The COSMIC/FORMOSAT-3 is a joint US/Taiwan radio occultation mission consisting of six identical micro-satellites. Each microsatellite has a GPS Occultation Experiment payload to operate the ionospheric RO measurements. FS3/COSMIC data can make a positive impact on global ionosphere study providing essential information about height electron density distribu-tion. For correct using of the RO electron density profiles for geophysical analysis, modeling and other applications it is necessary to make validation of these data with electron density distributions obtained by another measurement techniques such as proven ground based facili-ties -ionosondes and IS radars. In fact as the ionosondes provide no direct information on the profile above the maximum electron density and the topside ionosonde profile is obtained by fitting a model to the peak electron density value, the COSMIC RO measurements can make an important contribution to the investigation of the topside part of the ionosphere. IS radars provide information about the whole electron density profile, so we can estimate the agreement of topside parts between two independent measurements. To validate the reliability of COS-MIC data we have used the ionospheric electron density profiles derived from IS radar located near Kharkiv, Ukraine (geographic coordinates: 49.6N, 36.3E, geomagnetic coordinates: 45.7N, 117.8E). The Kharkiv radar is a sole incoherent scatter facility on the middle latitudes of Eu-ropean region. The radar operates with 100-m zenith parabolic antenna at 158 MHz with peak transmitted power 2.0 MW. The Kharkiv IS radar is able to determine the heights-temporal distribution of ionosphere parameters in height range of 70-1500 km. At the ionosphere in-vestigation by incoherent scatter method there are directly measured the power spectrum (or autocorrelation function) of scattered signal. With using of rather complex procedure of the received signal processing it is possible to estimate the majority of the ionospheric parameters -density and kinetic temperature of electron and main ions, the plasma drift velocity and others. The comparison of RO reveals that usually COSMIC RO profiles are in a rather good agreement with ISR profiles both in the F2 layer peak electron density (NmF2) and the form of profiles. The coincidence of profiles is better in the cases when projection of the ray path of tangent points is closer to the ISR location. It is necessary to note that retrieved electron density profiles should not be interpreted as actual vertical profiles. The geographical location of the ray path tangent points at the top and at the bottom of a profile may differ by several hundred kilometers. So the spatial smearing of data takes place and RO technique represents an image of vertical and horizontal ionospheric structure. That is why the comparison with ground-based data has rather relative character. We derived quantitative parameters to char-acterize the differences of the compared profiles: the peak height difference, the relative peak density difference. Most of the compared profiles agree within error limits, depending on the accuracy of the occultation-and the radar-derived profiles. In general COSMIC RO profiles are in a good agreement with incoherent radar profiles both in the F2 layer peak electron density (NmF2) and the form of the profiles. The coincidence of COSMIC and incoherent radar pro-files is better in the cases when projection of the ray path tangent points is closer to the radar location. COSMIC measurements can be efficiently used to study the topside part of the iono-spheric electron density. To validate the reliability of the COSMIC ionospheric observations it must be done the big work on the analysis and statistical generalization of the huge data array (today the total number of ionospheric occultation is more than 2.300.000), but this technique is a very promising one to retrieve accurate profiles of the ionospheric electron density with ground-based measurements on a global scale. We acknowledge the Taiwan's National Space Organization (NSPO) and the University Corporation for Atmospheric Research (UCAR) for providing the COSMIC Data.

Satellite Anomalies: Benefits of a Centralized Anomaly Database and Methods for Securely Sharing Information Among Satellite Operators

DTIC Science & Technology

2014-01-01

unprecedented efficiencies in global busi- ness collaboration through communication, information distribution, and fast electronic monetary transactions...tudes (which peaks in free electron density at 300–400 km but extends to just above 1,000 km). At GEO, surface charging occurs intermit - tently

The dependence of graphene Raman D-band on carrier density.

PubMed

Liu, Junku; Li, Qunqing; Zou, Yuan; Qian, Qingkai; Jin, Yuanhao; Li, Guanhong; Jiang, Kaili; Fan, Shoushan

2013-01-01

Raman spectroscopy has been an integral part of graphene research and can provide information about graphene structure, electronic characteristics, and electron-phonon interactions. In this study, the characteristics of the graphene Raman D-band, which vary with carrier density, are studied in detail, including the frequency, full width half-maximum, and intensity. We find the Raman D-band frequency increases for hole doping and decreases for electron doping. The Raman D-band intensity increases when the Fermi level approaches half of the excitation energy and is higher in the case of electron doping than that of hole doping. These variations can be explained by electron-phonon interaction theory and quantum interference between different Raman pathways in graphene. The intensity ratio of Raman D- and G-band, which is important for defects characterization in graphene, shows a strong dependence on carrier density.

Excited level populations and excitation kinetics of nonequilibrium ionizing argon discharge plasma of atmospheric pressure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Akatsuka, Hiroshi

2009-04-15

Population densities of excited states of argon atoms are theoretically examined for ionizing argon plasma in a state of nonequilibrium under atmospheric pressure from the viewpoint of elementary processes with collisional radiative model. The dependence of excited state populations on the electron and gas temperatures is discussed. Two electron density regimes are found, which are distinguished by the population and depopulation mechanisms for the excited states in problem. When the electron impact excitation frequency for the population or depopulation is lower than the atomic impact one, the electron density of the plasma is considered as low to estimate the populationmore » and depopulation processes. Some remarkable characteristics of population and depopulation mechanisms are found for the low electron density atmospheric plasma, where thermal relaxation by atomic collisions becomes the predominant process within the group of close-energy states in the ionizing plasma of atmospheric pressure, and the excitation temperature is almost the same as the gas temperature. In addition to the collisional relaxation by argon atoms, electron impact excitation from the ground state is also an essential population mechanism. The ratios of population density of the levels pairs, between which exists a large energy gap, include information on the electron collisional kinetics. For high electron density, the effect of atomic collisional relaxation becomes weak. For this case, the excitation mechanism is explained as electron impact ladderlike excitation similar to low-pressure ionizing plasma, since the electron collision becomes the dominant process for the population and depopulation kinetics.« less

Shannon entropies and Fisher information of K-shell electrons of neutral atoms

NASA Astrophysics Data System (ADS)

Sekh, Golam Ali; Saha, Aparna; Talukdar, Benoy

2018-02-01

We represent the two K-shell electrons of neutral atoms by Hylleraas-type wave function which fulfils the exact behavior at the electron-electron and electron-nucleus coalescence points and, derive a simple method to construct expressions for single-particle position- and momentum-space charge densities, ρ (r) and γ (p) respectively. We make use of the results for ρ (r) and γ (p) to critically examine the effect of correlation on bare (uncorrelated) values of Shannon information entropies (S) and of Fisher information (F) for the K-shell electrons of atoms from helium to neon. Due to inter-electronic repulsion the values of the uncorrelated Shannon position-space entropies are augmented while those of the momentum-space entropies are reduced. The corresponding Fisher information are found to exhibit opposite behavior in respect of this. Attempts are made to provide some plausible explanation for the observed response of S and F to electronic correlation.

Electronic entanglement in late transition metal oxides.

PubMed

Thunström, Patrik; Di Marco, Igor; Eriksson, Olle

2012-11-02

We present a study of the entanglement in the electronic structure of the late transition metal monoxides--MnO, FeO, CoO, and NiO--obtained by means of density-functional theory in the local density approximation combined with dynamical mean-field theory. The impurity problem is solved through exact diagonalization, which grants full access to the thermally mixed many-body ground state density operator. The quality of the electronic structure is affirmed through a direct comparison between the calculated electronic excitation spectrum and photoemission experiments. Our treatment allows for a quantitative investigation of the entanglement in the electronic structure. Two main sources of entanglement are explicitly resolved through the use of a fidelity based geometrical entanglement measure, and additional information is gained from a complementary entropic entanglement measure. We show that the interplay of crystal field effects and Coulomb interaction causes the entanglement in CoO to take a particularly intricate form.

Modified Faraday cup

DOEpatents

Elmer, John W.; Teruya, Alan T.; O'Brien, Dennis W.

1996-01-01

A tomographic technique for measuring the current density distribution in electron beams using electron beam profile data acquired from a modified Faraday cup to create an image of the current density in high and low power beams. The modified Faraday cup includes a narrow slit and is rotated by a stepper motor and can be moved in the x, y and z directions. The beam is swept across the slit perpendicular thereto and controlled by deflection coils, and the slit rotated such that waveforms are taken every few degrees form 0.degree. to 360.degree. and the waveforms are recorded by a digitizing storage oscilloscope. Two-din-tensional and three-dimensional images of the current density distribution in the beam can be reconstructed by computer tomography from this information, providing quantitative information about the beam focus and alignment.

Path Integrals for Electronic Densities, Reactivity Indices, and Localization Functions in Quantum Systems

PubMed Central

Putz, Mihai V.

2009-01-01

The density matrix theory, the ancestor of density functional theory, provides the immediate framework for Path Integral (PI) development, allowing the canonical density be extended for the many-electronic systems through the density functional closure relationship. Yet, the use of path integral formalism for electronic density prescription presents several advantages: assures the inner quantum mechanical description of the system by parameterized paths; averages the quantum fluctuations; behaves as the propagator for time-space evolution of quantum information; resembles Schrödinger equation; allows quantum statistical description of the system through partition function computing. In this framework, four levels of path integral formalism were presented: the Feynman quantum mechanical, the semiclassical, the Feynman-Kleinert effective classical, and the Fokker-Planck non-equilibrium ones. In each case the density matrix or/and the canonical density were rigorously defined and presented. The practical specializations for quantum free and harmonic motions, for statistical high and low temperature limits, the smearing justification for the Bohr’s quantum stability postulate with the paradigmatic Hydrogen atomic excursion, along the quantum chemical calculation of semiclassical electronegativity and hardness, of chemical action and Mulliken electronegativity, as well as by the Markovian generalizations of Becke-Edgecombe electronic focalization functions – all advocate for the reliability of assuming PI formalism of quantum mechanics as a versatile one, suited for analytically and/or computationally modeling of a variety of fundamental physical and chemical reactivity concepts characterizing the (density driving) many-electronic systems. PMID:20087467

Path integrals for electronic densities, reactivity indices, and localization functions in quantum systems.

PubMed

Putz, Mihai V

2009-11-10

The density matrix theory, the ancestor of density functional theory, provides the immediate framework for Path Integral (PI) development, allowing the canonical density be extended for the many-electronic systems through the density functional closure relationship. Yet, the use of path integral formalism for electronic density prescription presents several advantages: assures the inner quantum mechanical description of the system by parameterized paths; averages the quantum fluctuations; behaves as the propagator for time-space evolution of quantum information; resembles Schrödinger equation; allows quantum statistical description of the system through partition function computing. In this framework, four levels of path integral formalism were presented: the Feynman quantum mechanical, the semiclassical, the Feynman-Kleinert effective classical, and the Fokker-Planck non-equilibrium ones. In each case the density matrix or/and the canonical density were rigorously defined and presented. The practical specializations for quantum free and harmonic motions, for statistical high and low temperature limits, the smearing justification for the Bohr's quantum stability postulate with the paradigmatic Hydrogen atomic excursion, along the quantum chemical calculation of semiclassical electronegativity and hardness, of chemical action and Mulliken electronegativity, as well as by the Markovian generalizations of Becke-Edgecombe electronic focalization functions - all advocate for the reliability of assuming PI formalism of quantum mechanics as a versatile one, suited for analytically and/or computationally modeling of a variety of fundamental physical and chemical reactivity concepts characterizing the (density driving) many-electronic systems.

Terrain Display Alternatives Assessment of Information Density and Alerting Strategies

DOT National Transportation Integrated Search

1998-04-01

Current technology makes it possible to display navigation and terrain information on electronic : screens in the cockpit. The conventions used for position and terrain information must be clearly : presented so pilots can maintain their positional a...

Exploring charge density analysis in crystals at high pressure: data collection, data analysis and advanced modelling.

PubMed

Casati, Nicola; Genoni, Alessandro; Meyer, Benjamin; Krawczuk, Anna; Macchi, Piero

2017-08-01

The possibility to determine electron-density distribution in crystals has been an enormous breakthrough, stimulated by a favourable combination of equipment for X-ray and neutron diffraction at low temperature, by the development of simplified, though accurate, electron-density models refined from the experimental data and by the progress in charge density analysis often in combination with theoretical work. Many years after the first successful charge density determination and analysis, scientists face new challenges, for example: (i) determination of the finer details of the electron-density distribution in the atomic cores, (ii) simultaneous refinement of electron charge and spin density or (iii) measuring crystals under perturbation. In this context, the possibility of obtaining experimental charge density at high pressure has recently been demonstrated [Casati et al. (2016). Nat. Commun. 7, 10901]. This paper reports on the necessities and pitfalls of this new challenge, focusing on the species syn-1,6:8,13-biscarbonyl[14]annulene. The experimental requirements, the expected data quality and data corrections are discussed in detail, including warnings about possible shortcomings. At the same time, new modelling techniques are proposed, which could enable specific information to be extracted, from the limited and less accurate observations, like the degree of localization of double bonds, which is fundamental to the scientific case under examination.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Borovsky, Joseph E; Cayton, Thomas E; Denton, Michael H

Electron flux measurements from 7 satellites in geosynchronous orbit from 1990-2007 are fit with relativistic bi-Maxwellians, yielding a number density n and temperature T description of the outer electron radiation belt. For 54.5 spacecraft years of measurements the median value ofn is 3.7x10-4 cm-3 and the median value ofT is 142 keY. General statistical properties of n, T, and the 1.1-1.5 MeV flux J are investigated, including local-time and solar-cycle dependencies. Using superposed-epoch analysis triggered on storm onset, the evolution of the outer electron radiation belt through high-speed-steam-driven storms is investigated. The number density decay during the calm before themore » storm is seen, relativistic-electron dropouts and recoveries from dropout are investigated, and the heating of the outer electron radiation belt during storms is examined. Using four different triggers (SSCs, southward-IMF CME sheaths, southward-IMF magnetic clouds, and minimum Dst), CME-driven storms are analyzed with superposed-epoch techniques. For CME-driven storms an absence of a density decay prior to storm onset is found, the compression of the outer electron radiation belt at time of SSC is analyzed, the number-density increase and temperature decrease during storm main phase is seen, and the increase in density and temperature during storm recovery phase is observed. Differences are found between the density-temperature and the flux descriptions, with more information for analysis being available in the density-temperature description.« less

Proposed imaging of the ultrafast electronic motion in samples using x-ray phase contrast.

PubMed

Dixit, Gopal; Slowik, Jan Malte; Santra, Robin

2013-03-29

Tracing the motion of electrons has enormous relevance to understanding ubiquitous phenomena in ultrafast science, such as the dynamical evolution of the electron density during complex chemical and biological processes. Scattering of ultrashort x-ray pulses from an electronic wave packet would appear to be the most obvious approach to image the electronic motion in real time and real space with the notion that such scattering patterns, in the far-field regime, encode the instantaneous electron density of the wave packet. However, recent results by Dixit et al. [Proc. Natl. Acad. Sci. U.S.A. 109, 11636 (2012)] have put this notion into question and have shown that the scattering in the far-field regime probes spatiotemporal density-density correlations. Here, we propose a possible way to image the instantaneous electron density of the wave packet via ultrafast x-ray phase contrast imaging. Moreover, we show that inelastic scattering processes, which plague ultrafast scattering in the far-field regime, do not contribute in ultrafast x-ray phase contrast imaging as a consequence of an interference effect. We illustrate our general findings by means of a wave packet that lies in the time and energy range of the dynamics of valence electrons in complex molecular and biological systems. This present work offers a potential to image not only instantaneous snapshots of nonstationary electron dynamics, but also the laplacian of these snapshots which provide information about the complex bonding and topology of the charge distributions in the systems.

Proposed Imaging of the Ultrafast Electronic Motion in Samples using X-Ray Phase Contrast

NASA Astrophysics Data System (ADS)

Dixit, Gopal; Slowik, Jan Malte; Santra, Robin

2013-03-01

Tracing the motion of electrons has enormous relevance to understanding ubiquitous phenomena in ultrafast science, such as the dynamical evolution of the electron density during complex chemical and biological processes. Scattering of ultrashort x-ray pulses from an electronic wave packet would appear to be the most obvious approach to image the electronic motion in real time and real space with the notion that such scattering patterns, in the far-field regime, encode the instantaneous electron density of the wave packet. However, recent results by Dixit et al. [Proc. Natl. Acad. Sci. U.S.A. 109, 11 636 (2012)] have put this notion into question and have shown that the scattering in the far-field regime probes spatiotemporal density-density correlations. Here, we propose a possible way to image the instantaneous electron density of the wave packet via ultrafast x-ray phase contrast imaging. Moreover, we show that inelastic scattering processes, which plague ultrafast scattering in the far-field regime, do not contribute in ultrafast x-ray phase contrast imaging as a consequence of an interference effect. We illustrate our general findings by means of a wave packet that lies in the time and energy range of the dynamics of valence electrons in complex molecular and biological systems. This present work offers a potential to image not only instantaneous snapshots of nonstationary electron dynamics, but also the Laplacian of these snapshots which provide information about the complex bonding and topology of the charge distributions in the systems.

Two color interferometric electron density measurement in an axially blown arc

NASA Astrophysics Data System (ADS)

Stoller, Patrick; Carstensen, Jan; Galletti, Bernardo; Doiron, Charles; Sokolov, Alexey; Salzmann, René; Simon, Sandor; Jabs, Philipp

2016-09-01

High voltage circuit breakers protect the power grid by interrupting the current in case of a short circuit. To do so an arc is ignited between two contacts as they separate; transonic gas flow is used to cool and ultimately extinguish the arc at a current-zero crossing of the alternating current. A detailed understanding of the arc interruption process is needed to improve circuit breaker design. The conductivity of the partially ionized gas remaining after the current-zero crossing, a key parameter in determining whether the arc will be interrupted or not, is a function of the electron density. The electron density, in turn, is a function of the detailed dynamics of the arc cooling process, which does not necessarily occur under local thermodynamic equilibrium (LTE) conditions. In this work, we measure the spatially resolved line-integrated index of refraction in a near-current-zero arc stabilized in an axial flow of synthetic air with two nanosecond pulsed lasers at wavelengths of 532 nm and 671 nm. Generating a stable, cylindrically symmetric arc enables us to determine the three-dimensional index of refraction distribution using Abel inversion. Due to the wavelength dependence of the component of the index of refraction related to the free electrons, the information at two different wavelengths can be used to determine the electron density. This information allows us to determine how important it is to take into account non-equilibrium effects for accurate modeling of the physics of decaying arcs.

Dual descriptors within the framework of spin-polarized density functional theory.

PubMed

Chamorro, E; Pérez, P; Duque, M; De Proft, F; Geerlings, P

2008-08-14

Spin-polarized density functional theory (SP-DFT) allows both the analysis of charge-transfer (e.g., electrophilic and nucleophilic reactivity) and of spin-polarization processes (e.g., photophysical changes arising from electron transitions). In analogy with the dual descriptor introduced by Morell et al. [J. Phys. Chem. A 109, 205 (2005)], we introduce new dual descriptors intended to simultaneously give information of the molecular regions where the spin-polarization process linking states of different multiplicity will drive electron density and spin density changes. The electronic charge and spin rearrangement in the spin forbidden radiative transitions S(0)-->T(n,pi(*)) and S(0)-->T(pi,pi(*)) in formaldehyde and ethylene, respectively, have been used as benchmark examples illustrating the usefulness of the new spin-polarization dual descriptors. These quantities indicate those regions where spin-orbit coupling effects are at work in such processes. Additionally, the qualitative relationship between the topology of the spin-polarization dual descriptors and the vertical singlet triplet energy gap in simple substituted carbene series has been also discussed. It is shown that the electron density and spin density rearrangements arise in agreement with spectroscopic experimental evidence and other theoretical results on the selected target systems.

Self-Attractive Hartree Decomposition: Partitioning Electron Density into Smooth Localized Fragments.

PubMed

Zhu, Tianyu; de Silva, Piotr; Van Voorhis, Troy

2018-01-09

Chemical bonding plays a central role in the description and understanding of chemistry. Many methods have been proposed to extract information about bonding from quantum chemical calculations, the majority of them resorting to molecular orbitals as basic descriptors. Here, we present a method called self-attractive Hartree (SAH) decomposition to unravel pairs of electrons directly from the electron density, which unlike molecular orbitals is a well-defined observable that can be accessed experimentally. The key idea is to partition the density into a sum of one-electron fragments that simultaneously maximize the self-repulsion and maintain regular shapes. This leads to a set of rather unusual equations in which every electron experiences self-attractive Hartree potential in addition to an external potential common for all the electrons. The resulting symmetry breaking and localization are surprisingly consistent with chemical intuition. SAH decomposition is also shown to be effective in visualization of single/multiple bonds, lone pairs, and unusual bonds due to the smooth nature of fragment densities. Furthermore, we demonstrate that it can be used to identify specific chemical bonds in molecular complexes and provides a simple and accurate electrostatic model of hydrogen bonding.

Modified Faraday cup

DOEpatents

Elmer, J.W.; Teruya, A.T.; O`Brien, D.W.

1996-09-10

A tomographic technique for measuring the current density distribution in electron beams using electron beam profile data acquired from a modified Faraday cup to create an image of the current density in high and low power beams is disclosed. The modified Faraday cup includes a narrow slit and is rotated by a stepper motor and can be moved in the x, y and z directions. The beam is swept across the slit perpendicular thereto and controlled by deflection coils, and the slit rotated such that waveforms are taken every few degrees from 0{degree} to 360{degree} and the waveforms are recorded by a digitizing storage oscilloscope. Two-dimensional and three-dimensional images of the current density distribution in the beam can be reconstructed by computer tomography from this information, providing quantitative information about the beam focus and alignment. 12 figs.

New Data on the Topside Electron Density Distribution

NASA Technical Reports Server (NTRS)

Huang, Xue-Qin; Reinisch, Bodo; Bilitza, Dieter; Benson, Robert F.

2001-01-01

The existing uncertainties about the electron density profiles in the topside ionosphere, i.e., in the height region from hmF2 to approx. 2000 km, require the search for new data sources. The ISIS and Alouette topside sounder satellites from the sixties to the eighties recorded millions of ionograms and most were not analyzed in terms of electron density profiles. In recent years an effort started to digitize the analog recordings to prepare the ionograms for computerized analysis. As of November 2001 about 350,000 ionograms have been digitized from the original 7-track analog tapes. These data are available in binary and CDF format from the anonymous ftp site of the National Space Science Data Center. A search site and browse capabilities on CDAWeb assist the scientific usage of these data. All information and access links can be found at http://nssdc.gsfc.nasa.gov/space/isis/isis-status.html. This paper describes the ISIS data restoration effort and shows how the digital ionograms are automatically processed into electron density profiles from satellite orbit altitude (1400 km for ISIS-2) down to the F peak. Because of the large volume of data an automated processing algorithm is imperative. The automatic topside ionogram scaler with true height algorithm TOPIST software developed for this task is successfully scaling approx.70 % of the ionograms. An 'editing process' is available to manually scale the more difficult ionograms. The automated processing of the digitized ISIS ionograms is now underway, producing a much-needed database of topside electron density profiles for ionospheric modeling covering more than one solar cycle. The ISIS data restoration efforts are supported through NASA's Applied Systems and Information Research Program.

Experimental determination of spin-dependent electron density by joint refinement of X-ray and polarized neutron diffraction data.

PubMed

Deutsch, Maxime; Claiser, Nicolas; Pillet, Sébastien; Chumakov, Yurii; Becker, Pierre; Gillet, Jean Michel; Gillon, Béatrice; Lecomte, Claude; Souhassou, Mohamed

2012-11-01

New crystallographic tools were developed to access a more precise description of the spin-dependent electron density of magnetic crystals. The method combines experimental information coming from high-resolution X-ray diffraction (XRD) and polarized neutron diffraction (PND) in a unified model. A new algorithm that allows for a simultaneous refinement of the charge- and spin-density parameters against XRD and PND data is described. The resulting software MOLLYNX is based on the well known Hansen-Coppens multipolar model, and makes it possible to differentiate the electron spins. This algorithm is validated and demonstrated with a molecular crystal formed by a bimetallic chain, MnCu(pba)(H(2)O)(3)·2H(2)O, for which XRD and PND data are available. The joint refinement provides a more detailed description of the spin density than the refinement from PND data alone.

Watching excitons move: the time-dependent transition density matrix

NASA Astrophysics Data System (ADS)

Ullrich, Carsten

2012-02-01

Time-dependent density-functional theory allows one to calculate excitation energies and the associated transition densities in principle exactly. The transition density matrix (TDM) provides additional information on electron-hole localization and coherence of specific excitations of the many-body system. We have extended the TDM concept into the real-time domain in order to visualize the excited-state dynamics in conjugated molecules. The time-dependent TDM is defined as an implicit density functional, and can be approximately obtained from the time-dependent Kohn-Sham orbitals. The quality of this approximation is assessed in simple model systems. A computational scheme for real molecular systems is presented: the time-dependent Kohn-Sham equations are solved with the OCTOPUS code and the time-dependent Kohn-Sham TDM is calculated using a spatial partitioning scheme. The method is applied to show in real time how locally created electron-hole pairs spread out over neighboring conjugated molecular chains. The coupling mechanism, electron-hole coherence, and the possibility of charge separation are discussed.

Calculation of gyrosynchrotron radiation brightness temperature for outer bright loop of ICME

NASA Astrophysics Data System (ADS)

Sun, Weiying; Wu, Ji; Wang, C. B.; Wang, S.

:Solar polar orbit radio telescope (SPORT) is proposed to detect the high density plasma clouds of outer bright loop of ICMEs from solar orbit with large inclination. Of particular interest is following the propagation of the plasma clouds with remote sensor in radio wavelength band. Gyrosynchrotron emission is a main radio radiation mechanism of the plasma clouds and can provide information of interplanetary magnetic field. In this paper, we statistically analyze the electron density, electron temperature and magnetic field of background solar wind in time of quiet sun and ICMEs propagation. We also estimate the fluctuation range of the electron density, electron temperature and magnetic field of outer bright loop of ICMEs. Moreover, we calculate and analyze the emission brightness temperature and degree of polarization on the basis of the study of gyrosynchrotron emission, absorption and polarization characteristics as the optical depth is less than or equal to 1.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fang, Dongfan, E-mail: fangdongfan1208@126.com; Sun, Qizhi; Zhao, Xiaoming

A 633 nm laser interferometer has been designed based on a novel concept, which, without the acousto-optic modulator or the demodulator circuit, adopts the fibers to connect all elements except photodetectors and oscilloscope in this system to make it more compact, portable, and efficient. The noteworthy feature is to mathematically compare the two divided interference signals, which have the same phase-shift caused by the electron density but possess the different initial phase and low angular frequencies. It is possible to read the plasma density directly on the oscilloscope by our original mathematic demodulation method without a camera. Based on themore » Abel inversion algorithm, the radial electron density profiles versus time can be obtained by using the multi-chord system. The designed measurable phase shift ranges from 0 to 2π rad corresponding to the maximum line integral of electron density less than 3.5 × 10{sup 17} cm{sup −2}, and the phase accuracy is about 0.017 rad corresponding to the line integral of electron density accuracy of 1 × 10{sup 15} cm{sup −2}. After the construction of eight-chord interferometer, it will provide the detailed time resolved information of the spatial distribution of the electron density in the field-reversed configuration (FRC) plasma target produced by the “Yingguang-1” programmed-discharge device, which is being constructed in the Key Laboratory of Pulsed Power, China Academy of Engineering Physics.« less

Iterative projection algorithms for ab initio phasing in virus crystallography.

PubMed

Lo, Victor L; Kingston, Richard L; Millane, Rick P

2016-12-01

Iterative projection algorithms are proposed as a tool for ab initio phasing in virus crystallography. The good global convergence properties of these algorithms, coupled with the spherical shape and high structural redundancy of icosahedral viruses, allows high resolution phases to be determined with no initial phase information. This approach is demonstrated by determining the electron density of a virus crystal with 5-fold non-crystallographic symmetry, starting with only a spherical shell envelope. The electron density obtained is sufficiently accurate for model building. The results indicate that iterative projection algorithms should be routinely applicable in virus crystallography, without the need for ancillary phase information. Copyright © 2016 Elsevier Inc. All rights reserved.

Ionospheric tomography by gradient-enhanced kriging with STEC measurements and ionosonde characteristics

NASA Astrophysics Data System (ADS)

Minkwitz, David; van den Boogaart, Karl Gerald; Gerzen, Tatjana; Hoque, Mainul; Hernández-Pajares, Manuel

2016-11-01

The estimation of the ionospheric electron density by kriging is based on the optimization of a parametric measurement covariance model. First, the extension of kriging with slant total electron content (STEC) measurements based on a spatial covariance to kriging with a spatial-temporal covariance model, assimilating STEC data of a sliding window, is presented. Secondly, a novel tomography approach by gradient-enhanced kriging (GEK) is developed. Beyond the ingestion of STEC measurements, GEK assimilates ionosonde characteristics, providing peak electron density measurements as well as gradient information. Both approaches deploy the 3-D electron density model NeQuick as a priori information and estimate the covariance parameter vector within a maximum likelihood estimation for the dedicated tomography time stamp. The methods are validated in the European region for two periods covering quiet and active ionospheric conditions. The kriging with spatial and spatial-temporal covariance model is analysed regarding its capability to reproduce STEC, differential STEC and foF2. Therefore, the estimates are compared to the NeQuick model results, the 2-D TEC maps of the International GNSS Service and the DLR's Ionospheric Monitoring and Prediction Center, and in the case of foF2 to two independent ionosonde stations. Moreover, simulated STEC and ionosonde measurements are used to investigate the electron density profiles estimated by the GEK in comparison to a kriging with STEC only. The results indicate a crucial improvement in the initial guess by the developed methods and point out the potential compensation for a bias in the peak height hmF2 by means of GEK.

Density-functional theory based on the electron distribution on the energy coordinate

NASA Astrophysics Data System (ADS)

Takahashi, Hideaki

2018-03-01

We developed an electronic density functional theory utilizing a novel electron distribution n(ɛ) as a basic variable to compute ground state energy of a system. n(ɛ) is obtained by projecting the electron density n({\\boldsymbol{r}}) defined on the space coordinate {\\boldsymbol{r}} onto the energy coordinate ɛ specified with the external potential {\\upsilon }ext}({\\boldsymbol{r}}) of interest. It was demonstrated that the Kohn-Sham equation can also be formulated with the exchange-correlation functional E xc[n(ɛ)] that employs the density n(ɛ) as an argument. It turned out an exchange functional proposed in our preliminary development suffices to describe properly the potential energies of several types of chemical bonds with comparable accuracies to the corresponding functional based on local density approximation. As a remarkable feature of the distribution n(ɛ) it inherently involves the spatially non-local information of the exchange hole at the bond dissociation limit in contrast to conventional approximate functionals. By taking advantage of this property we also developed a prototype of the static correlation functional E sc including no empirical parameters, which showed marked improvements in describing the dissociations of covalent bonds in {{{H}}}2,{{{C}}}2{{{H}}}4 and {CH}}4 molecules.

A molecular shift register based on electron transfer

NASA Technical Reports Server (NTRS)

Hopfield, J. J.; Onuchic, Josenelson; Beratan, David N.

1988-01-01

An electronic shift-register memory at the molecular level is described. The memory elements are based on a chain of electron-transfer molecules and the information is shifted by photoinduced electron-transfer reactions. This device integrates designed electronic molecules onto a very large scale integrated (silicon microelectronic) substrate, providing an example of a 'molecular electronic device' that could actually be made. The design requirements for such a device and possible synthetic strategies are discussed. Devices along these lines should have lower energy usage and enhanced storage density.

Muon radiolysis affected by density inhomogeneity in near-critical fluids.

PubMed

Cormier, P J; Alcorn, C; Legate, G; Ghandi, K

2014-04-01

In this article we show the significant tunability of radiation chemistry in supercritical ethane and to a lesser extent in near critical CO2. The information was obtained by studies of muonium (Mu = μ(+)e(-)), which is formed by the thermalization of positive muons in different materials. The studies of the proportions of three fractions of muon polarization, PMu, diamagnetic PD and lost fraction, PL provided the information on radiolysis processes involved in muon thermalization. Our studies include three different supercritical fluids, water, ethane and carbon dioxide. A combination of mobile electrons and other radiolysis products such as (•)C2H5 contribute to interesting behavior at densities ∼40% above the critical point in ethane. In carbon dioxide, an increase in electron mobility contributes to the lost fraction. The hydrated electron in water is responsible for the lost fraction and decreases the muonium fraction.

Thermal shock tests with beryllium coupons in the electron beam facility JUDITH

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roedig, M.; Duwe, R.; Schuster, J.L.A.

1995-09-01

Several grades of American and Russian beryllium have been tested in high heat flux tests by means of an electron beam facility. For safety reasons, major modifications of the facility had to be fulfilled in advance to the tests. The influence of energy densities has been investigated in the range between 1 and 7 MJ/m{sup 2}. In addition the influence of an increasing number of shots at constant energy density has been studied. For all samples, surface profiles have been measured before and after the experiments. Additional information has been gained from scanning electron microscopy, and from metallography.

Kinetic energy as functional of the correlation hole

NASA Astrophysics Data System (ADS)

Nalewajski, Roman F.

2003-01-01

Using the marginal decomposition of the many-body probability distribution the electronic kinetic energy is expressed as the functional of the electron density and correlation hole. The analysis covers both the molecule as a whole and its constituent subsystems. The importance of the Fisher information for locality is emphasized.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Scaling of confinement and profiles in the EXTRAP T2 reversed-field pinch

NASA Astrophysics Data System (ADS)

Welander, A.

1999-01-01

In the EXTRAP T2 reversed-field pinch the diagnostic techniques for the measurement of electron density and temperature include; Thomson scattering which gives values at three radial positions in the core (r/a = 0, 0.28, 0.56), Langmuir probes which give values at the edge (r/a > 0.9) and interferometry which gives a line-averaged density. The empirical scaling of electron density and temperature including profile information with global plasma parameters has been studied. The density profile is subject to large variations, with an average parabolic shape when the density is low and flatter shapes when the density is increased. The change in the profile shape can be attributed to a shift in the penetration length of neutrals from the vicinity of the wall. The temperature scales roughly as I/n1/2 where I is the plasma current and n is the density. The temperature profile is always quite flat with lower variations and there is a tendency for a flatter profile at higher temperatures.

PAREMD: A parallel program for the evaluation of momentum space properties of atoms and molecules

NASA Astrophysics Data System (ADS)

Meena, Deep Raj; Gadre, Shridhar R.; Balanarayan, P.

2018-03-01

The present work describes a code for evaluating the electron momentum density (EMD), its moments and the associated Shannon information entropy for a multi-electron molecular system. The code works specifically for electronic wave functions obtained from traditional electronic structure packages such as GAMESS and GAUSSIAN. For the momentum space orbitals, the general expression for Gaussian basis sets in position space is analytically Fourier transformed to momentum space Gaussian basis functions. The molecular orbital coefficients of the wave function are taken as an input from the output file of the electronic structure calculation. The analytic expressions of EMD are evaluated over a fine grid and the accuracy of the code is verified by a normalization check and a numerical kinetic energy evaluation which is compared with the analytic kinetic energy given by the electronic structure package. Apart from electron momentum density, electron density in position space has also been integrated into this package. The program is written in C++ and is executed through a Shell script. It is also tuned for multicore machines with shared memory through OpenMP. The program has been tested for a variety of molecules and correlated methods such as CISD, Møller-Plesset second order (MP2) theory and density functional methods. For correlated methods, the PAREMD program uses natural spin orbitals as an input. The program has been benchmarked for a variety of Gaussian basis sets for different molecules showing a linear speedup on a parallel architecture.

DensToolKit: A comprehensive open-source package for analyzing the electron density and its derivative scalar and vector fields

NASA Astrophysics Data System (ADS)

Solano-Altamirano, J. M.; Hernández-Pérez, Julio M.

2015-11-01

DensToolKit is a suite of cross-platform, optionally parallelized, programs for analyzing the molecular electron density (ρ) and several fields derived from it. Scalar and vector fields, such as the gradient of the electron density (∇ρ), electron localization function (ELF) and its gradient, localized orbital locator (LOL), region of slow electrons (RoSE), reduced density gradient, localized electrons detector (LED), information entropy, molecular electrostatic potential, kinetic energy densities K and G, among others, can be evaluated on zero, one, two, and three dimensional grids. The suite includes a program for searching critical points and bond paths of the electron density, under the framework of Quantum Theory of Atoms in Molecules. DensToolKit also evaluates the momentum space electron density on spatial grids, and the reduced density matrix of order one along lines joining two arbitrary atoms of a molecule. The source code is distributed under the GNU-GPLv3 license, and we release the code with the intent of establishing an open-source collaborative project. The style of DensToolKit's code follows some of the guidelines of an object-oriented program. This allows us to supply the user with a simple manner for easily implement new scalar or vector fields, provided they are derived from any of the fields already implemented in the code. In this paper, we present some of the most salient features of the programs contained in the suite, some examples of how to run them, and the mathematical definitions of the implemented fields along with hints of how we optimized their evaluation. We benchmarked our suite against both a freely-available program and a commercial package. Speed-ups of ˜2×, and up to 12× were obtained using a non-parallel compilation of DensToolKit for the evaluation of fields. DensToolKit takes similar times for finding critical points, compared to a commercial package. Finally, we present some perspectives for the future development and growth of the suite.

MO-F-CAMPUS-J-04: Tissue Segmentation-Based MR Electron Density Mapping Method for MR-Only Radiation Treatment Planning of Brain

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yu, H; Lee, Y; Ruschin, M

2015-06-15

Purpose: Automatically derive electron density of tissues using MR images and generate a pseudo-CT for MR-only treatment planning of brain tumours. Methods: 20 stereotactic radiosurgery (SRS) patients’ T1-weighted MR images and CT images were retrospectively acquired. First, a semi-automated tissue segmentation algorithm was developed to differentiate tissues with similar MR intensities and large differences in electron densities. The method started with approximately 12 slices of manually contoured spatial regions containing sinuses and airways, then air, bone, brain, cerebrospinal fluid (CSF) and eyes were automatically segmented using edge detection and anatomical information including location, shape, tissue uniformity and relative intensity distribution.more » Next, soft tissues - muscle and fat were segmented based on their relative intensity histogram. Finally, intensities of voxels in each segmented tissue were mapped into their electron density range to generate pseudo-CT by linearly fitting their relative intensity histograms. Co-registered CT was used as a ground truth. The bone segmentations of pseudo-CT were compared with those of co-registered CT obtained by using a 300HU threshold. The average distances between voxels on external edges of the skull of pseudo-CT and CT in three axial, coronal and sagittal slices with the largest width of skull were calculated. The mean absolute electron density (in Hounsfield unit) difference of voxels in each segmented tissues was calculated. Results: The average of distances between voxels on external skull from pseudo-CT and CT were 0.6±1.1mm (mean±1SD). The mean absolute electron density differences for bone, brain, CSF, muscle and fat are 78±114 HU, and 21±8 HU, 14±29 HU, 57±37 HU, and 31±63 HU, respectively. Conclusion: The semi-automated MR electron density mapping technique was developed using T1-weighted MR images. The generated pseudo-CT is comparable to that of CT in terms of anatomical position of tissues and similarity of electron density assignment. This method can allow MR-only treatment planning.« less

Validation of Ionosonde Electron Density Reconstruction Algorithms with IONOLAB-RAY in Central Europe

NASA Astrophysics Data System (ADS)

Gok, Gokhan; Mosna, Zbysek; Arikan, Feza; Arikan, Orhan; Erdem, Esra

2016-07-01

Ionospheric observation is essentially accomplished by specialized radar systems called ionosondes. The time delay between the transmitted and received signals versus frequency is measured by the ionosondes and the received signals are processed to generate ionogram plots, which show the time delay or reflection height of signals with respect to transmitted frequency. The critical frequencies of ionospheric layers and virtual heights, that provide useful information about ionospheric structurecan be extracted from ionograms . Ionograms also indicate the amount of variability or disturbances in the ionosphere. With special inversion algorithms and tomographical methods, electron density profiles can also be estimated from the ionograms. Although structural pictures of ionosphere in the vertical direction can be observed from ionosonde measurements, some errors may arise due to inaccuracies that arise from signal propagation, modeling, data processing and tomographic reconstruction algorithms. Recently IONOLAB group (www.ionolab.org) developed a new algorithm for effective and accurate extraction of ionospheric parameters and reconstruction of electron density profile from ionograms. The electron density reconstruction algorithm applies advanced optimization techniques to calculate parameters of any existing analytical function which defines electron density with respect to height using ionogram measurement data. The process of reconstructing electron density with respect to height is known as the ionogram scaling or true height analysis. IONOLAB-RAY algorithm is a tool to investigate the propagation path and parameters of HF wave in the ionosphere. The algorithm models the wave propagation using ray representation under geometrical optics approximation. In the algorithm , the structural ionospheric characteristics arerepresented as realistically as possible including anisotropicity, inhomogenity and time dependence in 3-D voxel structure. The algorithm is also used for various purposes including calculation of actual height and generation of ionograms. In this study, the performance of electron density reconstruction algorithm of IONOLAB group and standard electron density profile algorithms of ionosondes are compared with IONOLAB-RAY wave propagation simulation in near vertical incidence. The electron density reconstruction and parameter extraction algorithms of ionosondes are validated with the IONOLAB-RAY results both for quiet anddisturbed ionospheric states in Central Europe using ionosonde stations such as Pruhonice and Juliusruh . It is observed that IONOLAB ionosonde parameter extraction and electron density reconstruction algorithm performs significantly better compared to standard algorithms especially for disturbed ionospheric conditions. IONOLAB-RAY provides an efficient and reliable tool to investigate and validate ionosonde electron density reconstruction algorithms, especially in determination of reflection height (true height) of signals and critical parameters of ionosphere. This study is supported by TUBITAK 114E541, 115E915 and Joint TUBITAK 114E092 and AS CR 14/001 projects.

RF attenuation as a dusty plasma diagnostic

NASA Astrophysics Data System (ADS)

Doyle, Brandon; Konopka, Uwe; Thomas, Edward

2017-10-01

When a dusty plasma is formed by adding dust to a plasma environment, the electron density of the background plasma is depleted as the dust particles acquire their negative charge. The magnitude of the electron depletion depends on the dust particle charge, and thus its properties, as well as the dust number density. A direct measurement of the electron density in a dusty plasma therefore contains information about the charging state of the dust particles. This measurement is difficult to obtain without influencing the system. For example, Langmuir probes influence the system by creating voids, or they become unreliable due to their potential contamination with dust. A less invasive diagnostic tool might be realized using plasma chamber electrodes for a plasma impedance measurement as it depends on the excitation frequency: the spatially averaged electron density is derived from the electron plasma frequency, which is related to the radio frequency attenuation characteristic. We present preliminary experiments using two impedance probe designs: probes immersed in a plasma and electrodes located at the edge of the plasma. We evaluate the potential application of this method for ground-based laboratory experiments and future microgravity experiment facilities aboard the ISS. This work was supported by JPL/NASA (JPL-RSA 1571699) the US Dept. of Energy (DE-SC0016330) and NSF (PHY-1613087).

Effective homogeneity of the exchange-correlation and non-interacting kinetic energy functionals under density scaling.

PubMed

Borgoo, Alex; Teale, Andrew M; Tozer, David J

2012-01-21

Correlated electron densities, experimental ionisation potentials, and experimental electron affinities are used to investigate the homogeneity of the exchange-correlation and non-interacting kinetic energy functionals of Kohn-Sham density functional theory under density scaling. Results are presented for atoms and small molecules, paying attention to the influence of the integer discontinuity and the choice of the electron affinity. For the exchange-correlation functional, effective homogeneities are highly system-dependent on either side of the integer discontinuity. By contrast, the average homogeneity-associated with the potential that averages over the discontinuity-is generally close to 4/3 when the discontinuity is computed using positive affinities for systems that do bind an excess electron and negative affinities for those that do not. The proximity to 4/3 becomes increasingly pronounced with increasing atomic number. Evaluating the discontinuity using a zero affinity in systems that do not bind an excess electron instead leads to effective homogeneities on the electron abundant side that are close to 4/3. For the non-interacting kinetic energy functional, the effective homogeneities are less system-dependent and the effect of the integer discontinuity is less pronounced. Average values are uniformly below 5/3. The study provides information that may aid the development of improved exchange-correlation and non-interacting kinetic energy functionals. © 2012 American Institute of Physics

Orbital relaxation effects on Kohn–Sham frontier orbital energies in density functional theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, DaDi; Zheng, Xiao, E-mail: xz58@ustc.edu.cn; Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, Anhui 230026

2015-04-21

We explore effects of orbital relaxation on Kohn–Sham frontier orbital energies in density functional theory by using a nonempirical scaling correction approach developed in Zheng et al. [J. Chem. Phys. 138, 174105 (2013)]. Relaxation of Kohn–Sham orbitals upon addition/removal of a fractional number of electrons to/from a finite system is determined by a systematic perturbative treatment. The information of orbital relaxation is then used to improve the accuracy of predicted Kohn–Sham frontier orbital energies by Hartree–Fock, local density approximation, and generalized gradient approximation methods. The results clearly highlight the significance of capturing the orbital relaxation effects. Moreover, the proposed scalingmore » correction approach provides a useful way of computing derivative gaps and Fukui quantities of N-electron finite systems (N is an integer), without the need to perform self-consistent-field calculations for (N ± 1)-electron systems.« less

Statistical density modification using local pattern matching

DOEpatents

Terwilliger, Thomas C.

2007-01-23

A computer implemented method modifies an experimental electron density map. A set of selected known experimental and model electron density maps is provided and standard templates of electron density are created from the selected experimental and model electron density maps by clustering and averaging values of electron density in a spherical region about each point in a grid that defines each selected known experimental and model electron density maps. Histograms are also created from the selected experimental and model electron density maps that relate the value of electron density at the center of each of the spherical regions to a correlation coefficient of a density surrounding each corresponding grid point in each one of the standard templates. The standard templates and the histograms are applied to grid points on the experimental electron density map to form new estimates of electron density at each grid point in the experimental electron density map.

Ab initio Computations of the Electronic, Mechanical, and Thermal Properties of Ultra High Temperature Ceramics (UHTC) ZrB2 and HfB2

NASA Technical Reports Server (NTRS)

Lawson, John W.; Bauschlicher, Charles W.; Daw, Murray

2011-01-01

Refractory materials such as metallic borides, often considered as ultra high temperature ceramics (UHTC), are characterized by high melting point, high hardness, and good chemical inertness. These materials have many applications which require high temperature materials that can operate with no or limited oxidation. Ab initio, first principles methods are the most accurate modeling approaches available and represent a parameter free description of the material based on the quantum mechanical equations. Using these methods, many of the intrinsic properties of these material can be obtained. We performed ab initio calculations based on density functional theory for the UHTC materials ZrB2 and HfB2. Computational results are presented for structural information (lattice constants, bond lengths, etc), electronic structure (bonding motifs, densities of states, band structure, etc), thermal quantities (phonon spectra, phonon densities of states, specific heat), as well as information about point defects such as vacancy and antisite formation energies.

Electronic structure of the organic semiconductor copper phthalocyanine: experiment and theory.

PubMed

Aristov, V Yu; Molodtsova, O V; Maslyuk, V V; Vyalikh, D V; Zhilin, V M; Ossipyan, Yu A; Bredow, T; Mertig, I; Knupfer, M

2008-01-21

The electronic structure of the organic semiconductor copper-phthalocyanine (CuPc) has been determined by a combination of conventional and resonant photoemission, near-edge x-ray absorption, as well as by the first-principles calculations. The experimentally obtained electronic valence band structure of CuPc is in very good agreement with the calculated density of states results, allowing the derivation of detailed site specific information.

Dynamic Structure Factor: An Introduction

NASA Astrophysics Data System (ADS)

Sturm, K.

1993-02-01

The doubly differential cross-section for weak inelastic scattering of waves or particles by manybody systems is derived in Born approximation and expressed in terms of the dynamic structure factor according to van Hove. The application of this very general scheme to scattering of neutrons, x-rays and high-energy electrons is discussed briefly. The dynamic structure factor, which is the space and time Fourier transform of the density-density correlation function, is a property of the many-body system independent of the external probe and carries information on the excitation spectrum of the system. The relation of the electronic structure factor to the density-density response function defined in linear-response theory is shown using the fluctuation-dissipation theorem. This is important for calculations, since the response function can be calculated approximately from the independent-particle response function in self-consistent field approximations, such as the random-phase approximation or the local-density approximation of the density functional theory. Since the density-density response function also determines the dielectric function, the dynamic structure can be expressed by the dielectric function.

C library for topological study of the electronic charge density.

PubMed

Vega, David; Aray, Yosslen; Rodríguez, Jesús

2012-12-05

The topological study of the electronic charge density is useful to obtain information about the kinds of bonds (ionic or covalent) and the atom charges on a molecule or crystal. For this study, it is necessary to calculate, at every space point, the electronic density and its electronic density derivatives values up to second order. In this work, a grid-based method for these calculations is described. The library, implemented for three dimensions, is based on a multidimensional Lagrange interpolation in a regular grid; by differentiating the resulting polynomial, the gradient vector, the Hessian matrix and the Laplacian formulas were obtained for every space point. More complex functions such as the Newton-Raphson method (to find the critical points, where the gradient is null) and the Cash-Karp Runge-Kutta method (used to make the gradient paths) were programmed. As in some crystals, the unit cell has angles different from 90°, the described library includes linear transformations to correct the gradient and Hessian when the grid is distorted (inclined). Functions were also developed to handle grid containing files (grd from DMol® program, CUBE from Gaussian® program and CHGCAR from VASP® program). Each one of these files contains the data for a molecular or crystal electronic property (such as charge density, spin density, electrostatic potential, and others) in a three-dimensional (3D) grid. The library can be adapted to make the topological study in any regular 3D grid by modifying the code of these functions. Copyright © 2012 Wiley Periodicals, Inc.

New computational tools for H/D determination in macromolecular structures from neutron data.

PubMed

Siliqi, Dritan; Caliandro, Rocco; Carrozzini, Benedetta; Cascarano, Giovanni Luca; Mazzone, Annamaria

2010-11-01

Two new computational methods dedicated to neutron crystallography, called n-FreeLunch and DNDM-NDM, have been developed and successfully tested. The aim in developing these methods is to determine hydrogen and deuterium positions in macromolecular structures by using information from neutron density maps. Of particular interest is resolving cases in which the geometrically predicted hydrogen or deuterium positions are ambiguous. The methods are an evolution of approaches that are already applied in X-ray crystallography: extrapolation beyond the observed resolution (known as the FreeLunch procedure) and a difference electron-density modification (DEDM) technique combined with the electron-density modification (EDM) tool (known as DEDM-EDM). It is shown that the two methods are complementary to each other and are effective in finding the positions of H and D atoms in neutron density maps.

3D electron density distributions in the solar corona during solar minima: assessment for more realistic solar wind modeling

NASA Astrophysics Data System (ADS)

de Patoul, J.; Foullon, C.; Riley, P.

2015-12-01

Knowledge of the electron density distribution in the solar corona put constraints on the magnetic field configurations for coronal modeling, and on initial conditions for solar wind modeling. We work with polarized SOHO/LASCO-C2 images from the last two recent minima of solar activity (1996-1997 and 2008-2010), devoid of coronal mass ejections. We derive the 4D electron density distributions in the corona by applying a newly developed time-dependent tomographic reconstruction method. First we compare the density distributions obtained from tomography with magnetohydrodynamic (MHD) solutions. The tomography provides more accurate distributions of electron densities in the polar regions, and we find that the observed density varies with the solar cycle in both polar and equatorial regions. Second, we find that the highest-density structures do not always correspond to the predicted large-scale heliospheric current sheet or its helmet streamer but can follow the locations of pseudo-streamers. We conclude that tomography offers reliable density distribution in the corona, reproducing the slow time evolution of coronal structures, without prior knowledge of the coronal magnetic field over a full rotation. Finally, we suggest that the highest-density structures show a differential rotation well above the surface depending on how it is magnetically connected to the surface. Such valuable information on the rotation of large-scale structures could help to connect the sources of the solar wind to their in-situ counterparts in future missions such as Solar Orbiter and Solar Probe Plus. This research combined with the MHD coronal modeling efforts has the potential to increase the reliability for future space weather forecasting.

High-pressure studies with x-rays using diamond anvil cells

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shen, Guoyin; Mao, Ho Kwang

2016-11-22

Pressure profoundly alters all states of matter. The symbiotic development of ultrahigh-pressure diamond anvil cells, to compress samples to sustainable multi-megabar pressures; and synchrotron x-ray techniques, to probe materials' properties in situ, has enabled the exploration of rich high-pressure (HP) science. In this article, we first introduce the essential concept of diamond anvil cell technology, together with recent developments and its integration with other extreme environments. We then provide an overview of the latest developments in HP synchrotron techniques, their applications, and current problems, followed by a discussion of HP scientific studies using x-rays in the key multidisciplinary fields. Thesemore » HP studies include: HP x-ray emission spectroscopy, which provides information on the filled electronic states of HP samples; HP x-ray Raman spectroscopy, which probes the HP chemical bonding changes of light elements; HP electronic inelastic x-ray scattering spectroscopy, which accesses high energy electronic phenomena, including electronic band structure, Fermi surface, excitons, plasmons, and their dispersions; HP resonant inelastic x-ray scattering spectroscopy, which probes shallow core excitations, multiplet structures, and spin-resolved electronic structure; HP nuclear resonant x-ray spectroscopy, which provides phonon densities of state and time-resolved Mössbauer information; HP x-ray imaging, which provides information on hierarchical structures, dynamic processes, and internal strains; HP x-ray diffraction, which determines the fundamental structures and densities of single-crystal, polycrystalline, nanocrystalline, and non-crystalline materials; and HP radial x-ray diffraction, which yields deviatoric, elastic and rheological information. Integrating these tools with hydrostatic or uniaxial pressure media, laser and resistive heating, and cryogenic cooling, has enabled investigations of the structural, vibrational, electronic, and magnetic properties of materials over a wide range of pressure-temperature conditions.« less

High-pressure studies with x-rays using diamond anvil cells

NASA Astrophysics Data System (ADS)

Shen, Guoyin; Mao, Ho Kwang

2017-01-01

Pressure profoundly alters all states of matter. The symbiotic development of ultrahigh-pressure diamond anvil cells, to compress samples to sustainable multi-megabar pressures; and synchrotron x-ray techniques, to probe materials’ properties in situ, has enabled the exploration of rich high-pressure (HP) science. In this article, we first introduce the essential concept of diamond anvil cell technology, together with recent developments and its integration with other extreme environments. We then provide an overview of the latest developments in HP synchrotron techniques, their applications, and current problems, followed by a discussion of HP scientific studies using x-rays in the key multidisciplinary fields. These HP studies include: HP x-ray emission spectroscopy, which provides information on the filled electronic states of HP samples; HP x-ray Raman spectroscopy, which probes the HP chemical bonding changes of light elements; HP electronic inelastic x-ray scattering spectroscopy, which accesses high energy electronic phenomena, including electronic band structure, Fermi surface, excitons, plasmons, and their dispersions; HP resonant inelastic x-ray scattering spectroscopy, which probes shallow core excitations, multiplet structures, and spin-resolved electronic structure; HP nuclear resonant x-ray spectroscopy, which provides phonon densities of state and time-resolved Mössbauer information; HP x-ray imaging, which provides information on hierarchical structures, dynamic processes, and internal strains; HP x-ray diffraction, which determines the fundamental structures and densities of single-crystal, polycrystalline, nanocrystalline, and non-crystalline materials; and HP radial x-ray diffraction, which yields deviatoric, elastic and rheological information. Integrating these tools with hydrostatic or uniaxial pressure media, laser and resistive heating, and cryogenic cooling, has enabled investigations of the structural, vibrational, electronic, and magnetic properties of materials over a wide range of pressure-temperature conditions.

New Data Source for Studying and Modelling the Topside Ionosphere

NASA Technical Reports Server (NTRS)

Huang, Xue-Qin; Reinisch, Bodo; Bilitza, Dieter; Benson, Robert

2001-01-01

The existing uncertainties about density profiles in the topside ionosphere, i.e., in the height regime from hmF2 to approx. 2000 km, requires the search for new data sources. Millions of ionograms had been recorded by the ISIS and Alouette satellites in the sixties and seventies, that never were analyzed in terms of electron density profiles. In recent years an effort started to digitize the analog recordings to prepare the ionograms for computerized analysis. This paper shows how the digital ionograms are processed and the electron density profiles (from satellite orbit altitude, 1400 km for ISIS-2, down to the F peak) are calculated. The most difficult part of the task is the automatic scaling of the echo traces in the ISIS ionograms. Unlike the ionograms from modern ionosondes, the ISIS ionograms do not identify the wave polarization of the different echo traces, so physical logic must be applied to identify the ordinary ()) and extraordinary (X) traces, and this is not always successful. Characteristic resonance features seen in the topside ionograms occur at the gyro and plasma frequencies. An elaborate scheme was developed to identify these resonance frequencies in order to determine the local plasma and gyrofrequencies. This information helps in the identification of the O and X traces, and it provides the starting density of the electron density profile. The inversion of the echo traces into electron density profiles uses the same modified Chebyshev polynomial fitting technique that is successfully applied in the ground-based Digisonde network. The automatic topside ionogram scaler with true height algorithm TOPIST is successfully scaling approx. 70% of the ionograms. An 'editing process' is available to manually scale the more difficult ionograms. The home page for the ISIS project is at http://nssdc.gsfc.nasa.gov/space/isis/isis-status.html. It provides access to as of January 2001, 3000,000 digitized ISIS ionogram data and to related software. A search page lets users select data location, time, and a host of other search criteria. The automated processing of the ISIS ionograms will begin later this year and the electron density profiles will be made available from the project home page. The ISIS data restoration efforts are supported through NASA's Applied Systems and Information Research Program.

Imaging spectroscopy of type U and J solar radio bursts with LOFAR

NASA Astrophysics Data System (ADS)

Reid, Hamish A. S.; Kontar, Eduard P.

2017-10-01

Context. Radio U-bursts and J-bursts are signatures of electron beams propagating along magnetic loops confined to the corona. The more commonly observed type III radio bursts are signatures of electron beams propagating along magnetic loops that extend into interplanetary space. Given the prevalence of solar magnetic flux to be closed in the corona, why type III bursts are more frequently observed than U-bursts or J-bursts is an outstanding question. Aims: We use Low-Frequency Array (LOFAR) imaging spectroscopy between 30-80 MHz of low-frequency U-bursts and J-bursts, for the first time, to understand why electron beams travelling along coronal loops produce radio emission less often. Radio burst observations provide information not only about the exciting electron beams but also about the structure of large coronal loops with densities that are too low for standard extreme ultraviolet (EUV) or X-ray analysis. Methods: We analysed LOFAR images of a sequence of two J-bursts and one U-burst. The different radio source positions were used to model the spatial structure of the guiding magnetic flux tube and then deduce the energy range of the exciting electron beams without the assumption of a standard density model. We also estimated the electron density along the magnetic flux rope and compared it to coronal models. Results: The radio sources infer a magnetic loop that is 1 solar radius in altitude with the highest frequency sources starting around 0.6 solar radii. Electron velocities were found between 0.13 c and 0.24 c with the front of the electron beam travelling faster than the back of the electron beam. The velocities correspond to energy ranges within the beam from 0.7-11 keV to 0.7-43 keV. The density along the loop is higher than typical coronal density models and the density gradient is smaller. Conclusions: We found that a more restrictive range of accelerated beam and background plasma parameters can result in U-bursts or J-bursts, causing type III bursts to be more frequently observed. The large instability distances required before Langmuir waves are produced by some electron beams, and the small magnitude of the background density gradients makes closed loops less facilitative for radio emission than loops that extend into interplanetary space.

Geographic Information Systems to Assess External Validity in Randomized Trials.

PubMed

Savoca, Margaret R; Ludwig, David A; Jones, Stedman T; Jason Clodfelter, K; Sloop, Joseph B; Bollhalter, Linda Y; Bertoni, Alain G

2017-08-01

To support claims that RCTs can reduce health disparities (i.e., are translational), it is imperative that methodologies exist to evaluate the tenability of external validity in RCTs when probabilistic sampling of participants is not employed. Typically, attempts at establishing post hoc external validity are limited to a few comparisons across convenience variables, which must be available in both sample and population. A Type 2 diabetes RCT was used as an example of a method that uses a geographic information system to assess external validity in the absence of a priori probabilistic community-wide diabetes risk sampling strategy. A geographic information system, 2009-2013 county death certificate records, and 2013-2014 electronic medical records were used to identify community-wide diabetes prevalence. Color-coded diabetes density maps provided visual representation of these densities. Chi-square goodness of fit statistic/analysis tested the degree to which distribution of RCT participants varied across density classes compared to what would be expected, given simple random sampling of the county population. Analyses were conducted in 2016. Diabetes prevalence areas as represented by death certificate and electronic medical records were distributed similarly. The simple random sample model was not a good fit for death certificate record (chi-square, 17.63; p=0.0001) and electronic medical record data (chi-square, 28.92; p<0.0001). Generally, RCT participants were oversampled in high-diabetes density areas. Location is a highly reliable "principal variable" associated with health disparities. It serves as a directly measurable proxy for high-risk underserved communities, thus offering an effective and practical approach for examining external validity of RCTs. Copyright © 2017 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Synchrotron radiation from a runaway electron distribution in tokamaks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stahl, A.; Fülöp, T.; Landreman, M.

2013-09-15

The synchrotron radiation emitted by runaway electrons in a fusion plasma provides information regarding the particle momenta and pitch-angles of the runaway electron population through the strong dependence of the synchrotron spectrum on these parameters. Information about the runaway density and its spatial distribution, as well as the time evolution of the above quantities, can also be deduced. In this paper, we present the synchrotron radiation spectra for typical avalanching runaway electron distributions. Spectra obtained for a distribution of electrons are compared with the emission of mono-energetic electrons with a prescribed pitch-angle. We also examine the effects of magnetic fieldmore » curvature and analyse the sensitivity of the resulting spectrum to perturbations to the runaway distribution. The implications for the deduced runaway electron parameters are discussed. We compare our calculations to experimental data from DIII-D and estimate the maximum observed runaway energy.« less

Macromolecular refinement by model morphing using non-atomic parameterizations.

PubMed

Cowtan, Kevin; Agirre, Jon

2018-02-01

Refinement is a critical step in the determination of a model which explains the crystallographic observations and thus best accounts for the missing phase components. The scattering density is usually described in terms of atomic parameters; however, in macromolecular crystallography the resolution of the data is generally insufficient to determine the values of these parameters for individual atoms. Stereochemical and geometric restraints are used to provide additional information, but produce interrelationships between parameters which slow convergence, resulting in longer refinement times. An alternative approach is proposed in which parameters are not attached to atoms, but to regions of the electron-density map. These parameters can move the density or change the local temperature factor to better explain the structure factors. Varying the size of the region which determines the parameters at a particular position in the map allows the method to be applied at different resolutions without the use of restraints. Potential applications include initial refinement of molecular-replacement models with domain motions, and potentially the use of electron density from other sources such as electron cryo-microscopy (cryo-EM) as the refinement model.

Electronic shift register memory based on molecular electron-transfer reactions

NASA Technical Reports Server (NTRS)

Hopfield, J. J.; Onuchic, Jose Nelson; Beratan, David N.

1989-01-01

The design of a shift register memory at the molecular level is described in detail. The memory elements are based on a chain of electron-transfer molecules incorporated on a very large scale integrated (VLSI) substrate, and the information is shifted by photoinduced electron-transfer reactions. The design requirements for such a system are discussed, and several realistic strategies for synthesizing these systems are presented. The immediate advantage of such a hybrid molecular/VLSI device would arise from the possible information storage density. The prospect of considerable savings of energy per bit processed also exists. This molecular shift register memory element design solves the conceptual problems associated with integrating molecular size components with larger (micron) size features on a chip.

Methylation of zebularine investigated using density functional theory calculations.

PubMed

Selvam, Lalitha; Chen, Fang Fang; Wang, Feng

2011-07-30

Deoxyribonucleic acid (DNA) methylation is an epigenetic phenomenon, which adds methyl groups into DNA. This study reveals methylation of a nucleoside antibiotic drug 1-(β-D-ribofuranosyl)-2-pyrimidinone (zebularine or zeb) with respect to its methylated analog, 1-(β-D-ribofuranosyl)-5-methyl-2-pyrimidinone (d5) using density functional theory calculations in valence electronic space. Very similar infrared spectra suggest that zeb and d5 do not differ by types of the chemical bonds, but distinctly different Raman spectra of the nucleoside pair reveal that the impact caused by methylation of zeb can be significant. Further valence orbital-based information details on valence electronic structural changes caused by methylation of zebularine. Frontier orbitals in momentum space and position space of the molecules respond differently to methylation. Based on the additional methyl electron density concentration in d5, orbitals affected by the methyl moiety are classified into primary and secondary contributors. Primary methyl contributions include MO8 (57a), MO18 (47a), and MO37 (28a) of d5, which concentrates on methyl and the base moieties, suggest certain connection to their Frontier orbitals. The primary and secondary methyl affected orbitals provide useful information on chemical bonding mechanism of the methylation in zebularine. Copyright © 2011 Wiley Periodicals, Inc.

CT image electron density quantification in regions with metal implants: implications for radiotherapy treatment planning

NASA Astrophysics Data System (ADS)

Jechel, Christopher Alexander

In radiotherapy planning, computed tomography (CT) images are used to quantify the electron density of tissues and provide spatial anatomical information. Treatment planning systems use these data to calculate the expected spatial distribution of absorbed dose in a patient. CT imaging is complicated by the presence of metal implants which cause increased image noise, produce artifacts throughout the image and can exceed the available range of CT number values within the implant, perturbing electron density estimates in the image. Furthermore, current dose calculation algorithms do not accurately model radiation transport at metal-tissue interfaces. Combined, these issues adversely affect the accuracy of dose calculations in the vicinity of metal implants. As the number of patients with orthopedic and dental implants grows, so does the need to deliver safe and effective radiotherapy treatments in the presence of implants. The Medical Physics group at the Cancer Centre of Southeastern Ontario and Queen's University has developed a Cobalt-60 CT system that is relatively insensitive to metal artifacts due to the high energy, nearly monoenergetic Cobalt-60 photon beam. Kilovoltage CT (kVCT) images, including images corrected using a commercial metal artifact reduction tool, were compared to Cobalt-60 CT images throughout the treatment planning process, from initial imaging through to dose calculation. An effective metal artifact reduction algorithm was also implemented for the Cobalt-60 CT system. Electron density maps derived from the same kVCT and Cobalt-60 CT images indicated the impact of image artifacts on estimates of photon attenuation for treatment planning applications. Measurements showed that truncation of CT number data in kVCT images produced significant mischaracterization of the electron density of metals. Dose measurements downstream of metal inserts in a water phantom were compared to dose data calculated using CT images from kVCT and Cobalt-60 systems with and without artifact correction. The superior accuracy of electron density data derived from Cobalt-60 images compared to kVCT images produced calculated dose with far better agreement with measured results. These results indicated that dose calculation errors from metal image artifacts are primarily due to misrepresentation of electron density within metals rather than artifacts surrounding the implants.

Theoretical analysis of the electronic properties of the sex pheromone and its analogue derivatives in the female processionary moth Thaumetopoea pytiocampa.

PubMed

Chamorro, Ester R; Sequeira, Alfredo F; Zalazar, M Fernanda; Peruchena, Nélida M

2008-09-15

In the present work, the distribution of the electronic charge density of the natural sex pheromone, the (Z)-13-hexadecen-11-ynyl acetate, in the female processionary moth, Thaumetopoea pytiocampa, and its nine analogue derivatives was studied within the framework of the Density Functional Theory and the Atoms in Molecules (AIM) Theory at B3LYP/6-31G *//B3LYP/6-31++G * * level. Additionally, molecular electrostatic potential (MEP) maps of the previously mentioned compounds were computed and compared. Furthermore, the substitution of hydrogen atoms from the methyl group in the acetate group by electron withdrawing substituents (i.e., halogen atoms) as well as the replacement effect of hydrogen by electron donor substituents (+I effect) as methyl group, were explored. The key feature of the topological distribution of the charge density in analogue compounds, such as the variations of the topological properties encountered in the region formed by neighbouring atoms from the substitution site were presented and discussed. Using topological parameters, such as electronic charge density, Laplacian, kinetic energy density, and potential energy density evaluated at bond critical points (BCP), we provide here a detailed analysis of the nature of the chemical bonding of these molecules. In addition, the atomic properties (population, charge, energy, volume, and dipole moment) were determined on selected atoms. These properties were analyzed at the substitution site (with respect to the natural sex pheromone) and related to the biological activity and to the possible binding site with the pheromone binding protein, (PBP). Moreover, the Laplacian function of the electronic density was used to locate electrophilic regions susceptible to be attacked (by deficient electron atoms or donor hydrogen). Our results indicate that the change in the atomic properties, such as electronic population and atomic volume, are sensitive indicators of the loss of the biological activity in the analogues studied here. The crucial interaction between the acetate group of the natural sex pheromone and the PBP is most likely to be a hydrogen bonding and the substitution of hydrogen atoms by electronegative atoms in the pheromone molecule reduces the hydrogen acceptor capacity. This situation is mirrored by the diminish of the electronic population on carbon and oxygen atoms at the carbonylic group in the halo-acetate group. Additionally, the modified acetate group (with electronegative atoms) shows new charge concentration critical points or regions of concentration of charge density in which an electrophilic attack can also occur. Finally, the use of the topological analysis based in the charge density distribution and its Laplacian function, in conjunction with MEP maps provides valuable information about the steric volume and electronic requirement of the sex pheromone for binding to the PBP.

Materials Data on TiNi (SG:157) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-05

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CdAu (SG:157) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-03-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on KP (SG:19) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on PdC (SG:216) by Materials Project

DOE Data Explorer

Kristin Persson

2016-09-21

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on PdC (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2016-09-21

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on PuSe (SG:225) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TiRe (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-04-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca (SG:139) by Materials Project

DOE Data Explorer

Kristin Persson

2015-01-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca (SG:229) by Materials Project

DOE Data Explorer

Kristin Persson

2016-04-22

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca (SG:221) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca (SG:194) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TiC (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-05

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on PuB (SG:225) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on HoP (SG:225) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-05

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on HoP (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-07-26

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on YSb2 (SG:21) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-04-07

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TaN (SG:216) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-09-24

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TaN (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-09-25

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on KWO3 (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-07-17

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CaAs (SG:189) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on InN (SG:186) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on SnPd (SG:62) by Materials Project

DOE Data Explorer

Kristin Persson

2015-01-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on SrO (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on DyTh (SG:221) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on In (SG:194) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-11

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on GdGe (SG:63) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CrO (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on MgPt (SG:198) by Materials Project

DOE Data Explorer

Kristin Persson

2015-01-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on USnPt (SG:216) by Materials Project

DOE Data Explorer

Kristin Persson

2015-03-19

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Be (SG:136) by Materials Project

DOE Data Explorer

Kristin Persson

2016-09-17

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Nd (SG:229) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on DyNi (SG:62) by Materials Project

DOE Data Explorer

Kristin Persson

2015-01-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on SbIr (SG:194) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on BaSe (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on BaSe (SG:221) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on BPS4 (SG:23) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on GaN (SG:216) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on GaN (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on GaN (SG:194) by Materials Project

DOE Data Explorer

Kristin Persson

2016-09-15

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on GaN (SG:186) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TlBr (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TlBr (SG:221) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TlBr (SG:63) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on P (SG:12) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on LuP (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on P (SG:64) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CoPSe (SG:61) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on P (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on P (SG:0) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-11

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on P (SG:13) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on LuPPt (SG:187) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on NpP (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on P (SG:74) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on P (SG:2) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on P (SG:221) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on FeP (SG:62) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on P (SG:166) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CaPAu (SG:194) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VO2 (SG:59) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-09-30

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VO2 (SG:166) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-09-30

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VO2 (SG:1) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-09-30

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VO2 (SG:15) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-09-30

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VO2 (SG:227) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VO2 (SG:63) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-09-30

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VO2 (SG:139) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-04-22

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VO2 (SG:10) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-04-22

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VO2 (SG:166) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-04-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VO2 (SG:12) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-04-22

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VO2 (SG:74) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-09-30

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VO2 (SG:10) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-04

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VO2 (SG:2) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-04-22

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VO2 (SG:194) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-09-30

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VO2 (SG:62) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VO2 (SG:136) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VO2 (SG:11) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-09-30

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VO2 (SG:160) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-09-30

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VO2 (SG:87) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-09-30

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VO2 (SG:1) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-04-22

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on U (SG:136) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-05

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on U (SG:63) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on U (SG:229) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on U (SG:102) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on U (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2016-09-18

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CuS (SG:63) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-04-22

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CuS (SG:194) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-04-22

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CuS (SG:225) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-04-25

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on BiO (SG:160) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VO2 (SG:14) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-04-14

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on NaSi (SG:15) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VS2 (SG:2) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-04

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CoMo (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on SiS (SG:53) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on P (SG:53) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on B (SG:1) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-11

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on B (SG:134) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on B (SG:166) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on BNCl2 (SG:146) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on B (SG:134) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on BAs (SG:216) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TiB (SG:216) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on B (SG:166) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CoW (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-09-15

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Mg (SG:229) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Mg (SG:194) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-11

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on RbS (SG:71) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on RbS (SG:189) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CCl4 (SG:15) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-08-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on PH3 (SG:1) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on USb (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on USb (SG:225) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-07-14

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on UN (SG:225) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-04-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on KSb2 (SG:12) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on KTlO (SG:12) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ThC (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ThC (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on IrN (SG:131) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-09-15

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TiH (SG:131) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-11

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on PtO (SG:131) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on PdN (SG:131) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-05-24

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on OsN (SG:131) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-05-24

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CuO (SG:131) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-04-22

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on PbO (SG:131) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CrO (SG:131) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-04-22

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ReN (SG:131) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-09-03

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on YCoC (SG:131) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TcN (SG:131) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-05-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ZrH (SG:131) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on USO (SG:129) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-04-22

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on AlSb (SG:63) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-04-28

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on LaSn (SG:63) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-04-07

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CeRh (SG:63) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-04-07

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on AsS (SG:14) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-04

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on AsS (SG:1) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on AsS (SG:15) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on AsS (SG:14) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on K (SG:63) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-11

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on K (SG:221) by Materials Project

DOE Data Explorer

Kristin Persson

2016-05-19

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on K (SG:64) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-05

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on K (SG:229) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on K (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on K (SG:194) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-05

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on K (SG:15) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-05

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ScC (SG:216) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-09-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ScGe (SG:216) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-09-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ScSi (SG:216) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-09-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ScSn (SG:216) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-09-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Sb (SG:221) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on S (SG:14) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on S (SG:70) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on S (SG:58) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on S (SG:221) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on S (SG:19) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on S (SG:13) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on S (SG:148) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on S (SG:2) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on S (SG:15) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on S (SG:29) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on S (SG:15) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-04

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on S (SG:4) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-04

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on S (SG:99) by Materials Project

DOE Data Explorer

Kristin Persson

2016-04-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on S (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on S (SG:154) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on S (SG:14) by Materials Project

DOE Data Explorer

Kristin Persson

2016-07-14

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on S (SG:143) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-11

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on S (SG:143) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on S (SG:60) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on HoSe (SG:225) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on O2 (SG:69) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on O2 (SG:166) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on As (SG:166) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2015-01-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on N2 (SG:194) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CdC (SG:216) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-09-11

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CdC (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2016-09-11

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CdIBr (SG:160) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-05-05

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CSNOF5 (SG:2) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-03-28

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CSNF5 (SG:62) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-03-28

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on YP (SG:225) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on LaAs (SG:225) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on YP (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on LaP (SG:225) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on YAs (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on YAs (SG:225) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ZrRh (SG:51) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on HfIr (SG:51) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VPt (SG:51) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TiMo (SG:74) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-09-03

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ZrC (SG:216) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-09-25

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ZrC (SG:225) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2015-01-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ZrC (SG:194) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-12-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ZrC (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-09-25

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on V (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2016-04-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on V (SG:229) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ErSF (SG:129) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on F (SG:64) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CuF (SG:216) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on LiF (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TbSF (SG:129) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on YSF (SG:129) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CF4 (SG:15) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on YOF (SG:129) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on HoSF (SG:129) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on RbF (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Y (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2016-04-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Y (SG:194) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on SO2 (SG:41) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-04

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on NaNO (SG:14) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on LaS (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-09-19

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VB2 (SG:191) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on UTe3 (SG:63) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Rb (SG:70) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-04-07

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:166) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:12) by Materials Project

DOE Data Explorer

Kristin Persson

2016-04-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:58) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:63) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-04

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:227) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:166) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:65) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-05

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:221) by Materials Project

DOE Data Explorer

Kristin Persson

2016-05-19

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:194) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:214) by Materials Project

DOE Data Explorer

Kristin Persson

2017-04-07

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:65) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:166) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-05

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:67) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:194) by Materials Project

DOE Data Explorer

Kristin Persson

2016-04-20

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:0) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:71) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-05

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:65) by Materials Project

DOE Data Explorer

Kristin Persson

2016-09-16

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:191) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-11

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:166) by Materials Project

DOE Data Explorer

Kristin Persson

2016-05-16

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:164) by Materials Project

DOE Data Explorer

Kristin Persson

2016-09-03

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:202) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:69) by Materials Project

DOE Data Explorer

Kristin Persson

2016-07-22

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:229) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:191) by Materials Project

DOE Data Explorer

Kristin Persson

2017-07-14

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:67) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-11

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:139) by Materials Project

DOE Data Explorer

Kristin Persson

2016-09-17

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:206) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on C (SG:191) by Materials Project

DOE Data Explorer

Kristin Persson

2016-04-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VS2 (SG:47) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-04-22

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Tb (SG:194) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ReN (SG:216) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-07-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ReN (SG:225) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-09-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ReN (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-07-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ReN (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-04-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ReN (SG:194) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-09-03

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ReN (SG:187) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-09-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on YSn2 (SG:63) by Materials Project

DOE Data Explorer

Kristin Persson

2015-01-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on VSn2 (SG:70) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on UAs (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CoN (SG:216) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on BNF8 (SG:113) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-04

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on SnP (SG:107) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-07-14

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on SnP (SG:225) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-04-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on UN2 (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on UO (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TlC (SG:225) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-07-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ErP (SG:225) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ErP (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-07-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ErS (SG:225) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CoN (SG:225) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-09-19

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CoN (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-09-19

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TbAu (SG:221) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TbSe (SG:186) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TbAl (SG:57) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TbRh (SG:221) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Tb (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2016-04-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Tb (SG:229) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Tb (SG:166) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TbTe (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TbGa (SG:63) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Electron Density Profiles of the Topside Ionosphere

NASA Technical Reports Server (NTRS)

Huang, Xue-Qin; Reinsch, Bodo W.; Bilitza, Dieter; Benson, Robert F.

2002-01-01

The existing uncertainties about the electron density profiles in the topside ionosphere, i.e., in the height region from h,F2 to - 2000 km, require the search for new data sources. The ISIS and Alouette topside sounder satellites from the sixties to the eighties recorded millions of ionograms but most were not analyzed in terms of electron density profiles. In recent years an effort started to digitize the analog recordings to prepare the ionograms for computerized analysis. As of November 2001 about 350000 ionograms have been digitized from the original 7-track analog tapes. These data are available in binary and CDF format from the anonymous ftp site of the National Space Science Data Center. A search site and browse capabilities on CDAWeb assist the scientific usage of these data. All information and access links can be found at http://nssdc.gsfc.nasa.gov/space/isis/isis- status.htm1. This paper describes the ISIS data restoration effort and shows how the digital ionograms are automatically processed into electron density profiles from satellite orbit altitude (1400 km for ISIS-2) down to the F peak. Because of the large volume of data an automated processing algorithm is imperative. The TOPside Ionogram Scaler with True height algorithm TOPIST software developed for this task is successfully scaling - 70% of the ionograms. An <> is available to manually scale the more difficult ionograms. The automated processing of the digitized ISIS ionograms is now underway, producing a much-needed database of topside electron density profiles for ionospheric modeling covering more than one solar cycle.

On the stability of the electronic system in transition metal dichalcogenides.

PubMed

Faraggi, M N; Zubizarreta, X; Arnau, A; Silkin, V M

2016-05-11

Based on first-principles calculations, we prove that the origin of charge-density wave formation in metallic layered transition metal dichalcogenides (TMDC) is not due to an electronic effect, like the Fermi surface (FS) nesting, as it had been proposed. In particular, we consider NbSe2, NbS2, TaSe2, and TaS2 as representative examples of 2H-TMDC polytypes. Our main result consists that explicit inclusion of the matrix elements in first-principles calculations of the electron susceptibility [Formula: see text] removes, due to strong momentum dependence of the matrix elements, almost all the information about the FS topologies in the resulting [Formula: see text]. This finding strongly supports an interpretation in which the momentum dependence of the electron-phonon interaction is the only reason why the phenomenon of charge-density waves appears in this class of materials.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zuo, Zhiqi

The Full Potential Linear Augmented Plane Wave (FPLAPW or FLAPW) method is used for a spin-polarized band calculation for ordered Fe 3Pt. As major purpose, the momentum distributions of the spin-polarized electrons are calculated and compared with results from a magnetic Compton scattering measurement. To get related information, the electronic behavior is also analyzed by examining the partial densities of states and the spatial electron distributions; the role of alloying effects is then explored by studying the electrons in some related alloys: Fe 3Ni, Fe 3Pd, Ni 3Pt and Co 3Pt.

Experimental and DFT studies of (E)-2-[2-(2,6-dichlorophenyl)ethenyl]-8-hydroxyquinoline: electronic and vibrational properties.

PubMed

Sun, Wenqi; Yuan, Guozan; Liu, Jingxin; Ma, Li; Liu, Chengbu

2013-04-01

The title molecule (E)-2-[2-(2,6-dichlorophenyl)ethenyl]-8-hydroxyquinoline (DPEQ) was synthesized and characterized by FT-IR, UV-vis, NMR spectroscopy. The molecular geometry, vibrational frequencies and gauge independent atomic orbital (GIAO) 1H and 13C NMR chemical shift values of the compound in the ground state have been calculated by using the density functional theory (DFT) method. All the assignments of the theoretical frequencies were performed by potential energy distributions using VEDA 4 program. The calculated results indicate that the theoretical vibrational frequencies, 1H and 13C NMR chemical shift values show good agreement with experimental data. The electronic properties like UV-vis spectral analysis and HOMO-LUMO analysis of DPEQ have been reported and compared with experimental data. Information about the size, shape, charge density distribution and site of chemical reactivity of the molecule has been obtained by mapping electron density isosurface with molecular electrostatic potential (MEP). Copyright © 2013 Elsevier B.V. All rights reserved.

Experimental and DFT studies of (E)-2-[2-(2,6-dichlorophenyl)ethenyl]-8-hydroxyquinoline: Electronic and vibrational properties

NASA Astrophysics Data System (ADS)

Sun, Wenqi; Yuan, Guozan; Liu, Jingxin; Ma, Li; Liu, Chengbu

2013-04-01

The title molecule (E)-2-[2-(2,6-dichlorophenyl)ethenyl]-8-hydroxyquinoline (DPEQ) was synthesized and characterized by FT-IR, UV-vis, NMR spectroscopy. The molecular geometry, vibrational frequencies and gauge independent atomic orbital (GIAO) 1H and 13C NMR chemical shift values of the compound in the ground state have been calculated by using the density functional theory (DFT) method. All the assignments of the theoretical frequencies were performed by potential energy distributions using VEDA 4 program. The calculated results indicate that the theoretical vibrational frequencies, 1H and 13C NMR chemical shift values show good agreement with experimental data. The electronic properties like UV-vis spectral analysis and HOMO-LUMO analysis of DPEQ have been reported and compared with experimental data. Information about the size, shape, charge density distribution and site of chemical reactivity of the molecule has been obtained by mapping electron density isosurface with molecular electrostatic potential (MEP).

Electron Density Calibration for Radiotherapy Treatment Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Herrera-Martinez, F.; Rodriguez-Villafuerte, M.; Martinez-Davalos, A.

2006-09-08

Computed tomography (CT) images are used as basic input data for most modern radiosurgery treatment planning systems (TPS). CT data not only provide anatomic information to delineate target volumes, but also allow the introduction of corrections for tissue inhomogeneities into dose calculations during the treatment planning procedure. These corrections involve the determination of a relationship between tissue electron density ({rho}e) and their corresponding Hounsfield Units (HU). In this work, an elemental analysis of different commercial tissue equivalent materials using Scanning Electron Microscopy was carried out to characterize their chemical composition. The tissue equivalent materials were chosen to ensure a largemore » range of {rho}e to be included in the CT scanner calibration. A phantom was designed and constructed with these materials to simulate the size of a human head.« less

Dephasing of LO-phonon-plasmon hybrid modes in n-type GaAs

NASA Astrophysics Data System (ADS)

Vallée, F.; Ganikhanov, F.; Bogani, F.

1997-11-01

The relaxation dynamics of coherent phononlike LO-phonon-plasmon hybrid modes is investigated in n-doped GaAs using an infrared time-resolved coherent anti-Stokes Raman scattering technique. Measurements performed for different crystal temperatures in the range 10-300 K as a function of the electron density injected by doping show a large reduction of the hybrid mode dephasing time compared to the bare LO-phonon one for densities larger than 1016 cm-3. The results are interpreted in terms of coherent decay of the LO-phonon-plasmon mixed mode in the weak-coupling regime and yield information on the plasmon and electron relaxation. The estimated average electron momentum relaxation times are smaller than those deduced from Hall mobility measurements, as expected from our theoretical model.

The research of single intersection sensor signal control based on section data

NASA Astrophysics Data System (ADS)

Liu, Yunxiang; Huang, Yue; Wang, Hao

2016-12-01

Propose a sensing signal intersection control design electronic license based on the design by setting the intersection readers to interact with active electronic tags equipped vehicles, vehicle information obtained on the road section. In the vehicle detection sensor may control the green density as evaluation criteria are extended when the vehicle is higher than the threshold, the green density continuity, whereas the switching phases. Induction showed improved control strategy can achieve real-time traffic signal control effectively in high saturation intersection, to overcome the traditional sensor control failure at high saturation drawbacks and improve the utilization of urban Intersection comparative analysis by simulation.

Methodological accuracy of image-based electron density assessment using dual-energy computed tomography.

PubMed

Möhler, Christian; Wohlfahrt, Patrick; Richter, Christian; Greilich, Steffen

2017-06-01

Electron density is the most important tissue property influencing photon and ion dose distributions in radiotherapy patients. Dual-energy computed tomography (DECT) enables the determination of electron density by combining the information on photon attenuation obtained at two different effective x-ray energy spectra. Most algorithms suggested so far use the CT numbers provided after image reconstruction as input parameters, i.e., are imaged-based. To explore the accuracy that can be achieved with these approaches, we quantify the intrinsic methodological and calibration uncertainty of the seemingly simplest approach. In the studied approach, electron density is calculated with a one-parametric linear superposition ('alpha blending') of the two DECT images, which is shown to be equivalent to an affine relation between the photon attenuation cross sections of the two x-ray energy spectra. We propose to use the latter relation for empirical calibration of the spectrum-dependent blending parameter. For a conclusive assessment of the electron density uncertainty, we chose to isolate the purely methodological uncertainty component from CT-related effects such as noise and beam hardening. Analyzing calculated spectrally weighted attenuation coefficients, we find universal applicability of the investigated approach to arbitrary mixtures of human tissue with an upper limit of the methodological uncertainty component of 0.2%, excluding high-Z elements such as iodine. The proposed calibration procedure is bias-free and straightforward to perform using standard equipment. Testing the calibration on five published data sets, we obtain very small differences in the calibration result in spite of different experimental setups and CT protocols used. Employing a general calibration per scanner type and voltage combination is thus conceivable. Given the high suitability for clinical application of the alpha-blending approach in combination with a very small methodological uncertainty, we conclude that further refinement of image-based DECT-algorithms for electron density assessment is not advisable. © 2017 American Association of Physicists in Medicine.

Validation of ionospheric electron density profiles inferred from GPS occultation observations of the GPS/MET experiment

NASA Astrophysics Data System (ADS)

Kawakami, Todd Mori

In April of 1995, the launch of the GPS Meteorology Experiment (GPS/MET) onboard the Orbview-1 satellite, formerly known as Microlab-1, provided the first technology demonstration of active limb sounding of the Earth's atmosphere with a low Earth orbiting spacecraft utilizing the signals transmitted by the satellites of the Global Positioning System (GPS). Though the experiment's primary mission was to probe the troposphere and stratosphere, GPS/MET was also capable of making radio occultation observations of the ionosphere. The application of the GPS occultation technique to the upper atmosphere created a unique opportunity to conduct ionospheric research with an unprecedented global distribution of observations. For operational support requirements, the Abel transform could be employed to invert the horizontal TEC profiles computed from the L1 and L2 phase measurements observed by GPS/MET into electron density profiles versus altitude in near real time. The usefulness of the method depends on how effectively the TEC limb profiles can be transformed into vertical electron density profiles. An assessment of GPS/MET's ability to determine electron density profiles needs to be examined to validate the significance of the GPS occultation method as a new and complementary ionospheric research tool to enhance the observational databases and improve space weather modeling and forecasting. To that end, simulations of the occultation observations and their inversions have been conducted to test the Abel transform algorithm and to provide qualitative information about the type and range of errors that might be experienced during the processing of real data. Comparisons of the electron density profiles inferred from real GPS/MET observations are then compared with coincident in situ measurements from the satellites of Defense Meteorological Satellite Program (DMSP) and ground-based remote sensing from digisonde and incoherent scatter radar facilities. The principal focus of this study is the validation of the electron density profiles inferred from GPS occultation observations using the Abel transform.

Some effects of electron channeling on electron energy loss spectroscopy.

PubMed

Kirkland, Earl J

2005-02-01

As an electron beam (of order 100 keV) travels through a crystalline solid it can be channeled down a zone axis of the crystal to form a channeling peak centered on the atomic columns. The channeling peak can be similar in size to the outer atomic orbitals. Electron energy loss spectroscopy (EELS) measures the losses that the electron experiences as it passes through the solid yielding information about the unoccupied density of states in the solid. The interaction matrix element for this process typically produces dipole selection rules for small angle scattering. In this paper, a theoretical calculation of the EELS cross section in the presence of strong channeling is performed for the silicon L23 edge. The presence of channeling is found to alter both the intensity and selection rules for this EELS signal as a function of depth in the solid. At some depths in the specimen small but significant non-dipole transition components can be produced, which may influence measurements of the density of states in solids.

Improved Abel transform inversion: First application to COSMIC/FORMOSAT-3

NASA Astrophysics Data System (ADS)

Aragon-Angel, A.; Hernandez-Pajares, M.; Juan, J.; Sanz, J.

2007-05-01

In this paper the first results of Ionospheric Tomographic inversion are presented, using the Improved Abel Transform on the COSMIC/FORMOSAT-3 constellation of 6 LEO satellites, carrying on-board GPS receivers.[- 4mm] The Abel transform inversion is a wide used technique which in the ionospheric context makes it possible to retrieve electron densities as a function of height based of STEC (Slant Total Electron Content) data gathered from GPS receivers on board of LEO (Low Earth Orbit) satellites. Within this precise use, the classical approach of the Abel inversion is based on the assumption of spherical symmetry of the electron density in the vicinity of an occultation, meaning that the electron content varies in height but not horizontally. In particular, one implication of this assumption is that the VTEC (Vertical Total Electron Content) is a constant value for the occultation region. This assumption may not always be valid since horizontal ionospheric gradients (a very frequent feature in some ionosphere problematic areas such as the Equatorial region) could significantly affect the electron profiles. [- 4mm] In order to overcome this limitation/problem of the classical Abel inversion, a studied improvement of this technique can be obtained by assuming separability in the electron density (see Hernández-Pajares et al. 2000). This means that the electron density can be expressed by the multiplication of VTEC data and a shape function which assumes all the height dependency in it while the VTEC data keeps the horizontal dependency. Actually, it is more realistic to assume that this shape fuction depends only on the height and to use VTEC information to take into account the horizontal variation rather than considering spherical symmetry in the electron density function as it has been carried out in the classical approach of the Abel inversion.[-4mm] Since the above mentioned improved Abel inversion technique has already been tested and proven to be a useful tool to obtain a vertical description of the ionospheric electron density (see García-Fernández et al. 2003), a natural following step would be to extend the use of this technique to the recently available COSMIC data. The COSMIC satellite constellation, formed by 6 micro-satellites, is being deployed since April 2006 in circular orbit around the Earth, with a final altitude of about 700-800 kilometers. Its global and almost uniform coverage will overcome one of the main limitations of this technique which is the sparcity of data, related to lack of GPS receivers in some regions. This can significantly stimulate the development of radio occultation techniques with the use of the huge volume of data provided by the COSMIC constellation to be processed and analysed updating the current knowledge of the Ionospheres nature and behaviour. In this context a summary of the Improvel Abel transform inversion technique and the first results based on COSMIC constellation data will be presented. Moreover, future improvements, taking into account the higher temporal and global spatial coverage, will be discussed. [-4mm] References:M. Hernández-Pajares, J. M. Juan and J. Sanz, Improving the Abel inversion by adding ground GPS data to LEO radio occultations in ionospheric sounding, GEOPHYSICAL RESEARCH LETTERS, VOL. 27, NO. 16, PAGES 2473-2476, AUGUST 15, 2000.M. Garcia-Fernández, M. Hernández-Pajares, M. Juan, and J. Sanz, Improvement of ionospheric electron density estimation with GPSMET occultations using Abel inversion and VTEC Information, JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 108, NO. A9, 1338, doi:10.1029/2003JA009952, 2003

Materials Data on Ba21Al40 (SG:157) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Tl6S (SG:157) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-04-22

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Zr5Sb3 (SG:193) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Zr5Sb4 (SG:193) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-05

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on KP(HO2)2 (SG:9) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on KP(HO2)2 (SG:122) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on KP(HO2)2 (SG:82) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on KP(HO2)2 (SG:14) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on KP(HO2)2 (SG:13) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on KP(OF)2 (SG:62) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on KP(HO)2 (SG:15) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on KP(HO2)2 (SG:2) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on KP(HO2)2 (SG:43) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on KP(HO2)2 (SG:19) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on H2SO4 (SG:14) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-05

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on NaZn(HO)3 (SG:106) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-04

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ThB6 (SG:221) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Mg2PHO5 (SG:62) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Mg2PHO5 (SG:157) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CeSb(SBr)2 (SG:14) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Zn8Cu5 (SG:217) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on La5Si3 (SG:140) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on LaSb(SBr)2 (SG:14) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-04

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(GeRh)2 (SG:139) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(BC)2 (SG:131) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(GePd)2 (SG:139) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(NiGe)2 (SG:139) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(MnGe)2 (SG:139) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(AlZn)2 (SG:139) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(NO3)2 (SG:205) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(BeN)2 (SG:140) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(NiP)2 (SG:139) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(BeGe)2 (SG:129) by Materials Project

DOE Data Explorer

Kristin Persson

2016-04-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(GeIr)2 (SG:139) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(ZnGe)2 (SG:139) by Materials Project

DOE Data Explorer

Kristin Persson

2015-03-24

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(BO2)2 (SG:60) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(BS2)2 (SG:205) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(BO2)2 (SG:205) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(PIr)2 (SG:154) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(MgBi)2 (SG:164) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(GeRu)2 (SG:139) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(YS2)2 (SG:62) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(CoP)2 (SG:139) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(IO3)2 (SG:14) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(BIr)2 (SG:70) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-18

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(MnSb)2 (SG:164) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-05

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(AlGe)2 (SG:164) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-05

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(MnBi)2 (SG:164) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-05

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ca(ZnSi)2 (SG:139) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ba(AsPd)2 (SG:123) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Sm(BO2)3 (SG:15) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on KCd(NO2)3 (SG:146) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Pr(BO2)3 (SG:15) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on V2(OF)3 (SG:3) by Materials Project

DOE Data Explorer

Kristin Persson

2016-04-22

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on LaCoO3 (SG:167) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on LaCoO3 (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-04-22

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on LaCoO3 (SG:2) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TmSb2 (SG:21) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-04-07

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ErSb2 (SG:21) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-04-07

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TbSb2 (SG:21) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-04-07

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on DySb2 (SG:21) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-04-07

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CeInIr (SG:189) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Si3H (SG:166) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-07-18

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on RbOsO3 (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-07-18

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on MoWSeS3 (SG:156) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-05-25

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on NaMoO3 (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-07-17

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on KMoO3 (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-07-17

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TeMoSe (SG:156) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-05-25

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on NaOsO3 (SG:221) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-07-18

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TeMoWSe3 (SG:156) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-05-25

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on LaSiIr (SG:198) by Materials Project

DOE Data Explorer

Kristin Persson

2015-01-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CsTiF4 (SG:129) by Materials Project

DOE Data Explorer

Kristin Persson

2015-01-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on PbSO4 (SG:62) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CeInAu2 (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ZrSnRh (SG:190) by Materials Project

DOE Data Explorer

Kristin Persson

2015-01-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Al5Co2 (SG:194) by Materials Project

DOE Data Explorer

Kristin Persson

2015-01-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on NdMg3 (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TbIr2 (SG:227) by Materials Project

DOE Data Explorer

Kristin Persson

2015-05-16

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Pb(CO2)2 (SG:2) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Pr3GaC (SG:221) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CsIO3 (SG:221) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TaMn2 (SG:194) by Materials Project

DOE Data Explorer

Kristin Persson

2015-01-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on AgAuS2 (SG:49) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-02-10

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Tm(FeSi)2 (SG:139) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CaNiF5 (SG:15) by Materials Project

DOE Data Explorer

Kristin Persson

2014-09-30

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CaAs(HO)7 (SG:61) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TaSe2 (SG:42) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-07-14

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on NbS2 (SG:42) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2016-07-14

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on CeSe2 (SG:42) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

2017-07-17

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Pd(SCl3)2 (SG:2) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Nb(SeCl)2 (SG:2) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on YCu(WO4)2 (SG:2) by Materials Project

DOE Data Explorer

Kristin Persson

2014-07-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Au(OF3)2 (SG:2) by Materials Project

DOE Data Explorer

Kristin Persson

2016-02-11

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ni(WO4)2 (SG:2) by Materials Project

DOE Data Explorer

Kristin Persson

2016-04-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Pt(SCl3)2 (SG:2) by Materials Project

DOE Data Explorer

Kristin Persson

2016-05-20

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Sb(IF3)2 (SG:2) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Co(WO4)2 (SG:2) by Materials Project

DOE Data Explorer

Kristin Persson

2016-04-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Rh(OF3)2 (SG:2) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Pr(WO4)2 (SG:2) by Materials Project

DOE Data Explorer

Kristin Persson

2016-04-23

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Li2Ca (SG:227) by Materials Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kristin Persson

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Er(NiGe)2 (SG:139) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ho2SO2 (SG:164) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Na2BHO3 (SG:62) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Pu2Co (SG:189) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Dy2SO2 (SG:164) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on SrSnP (SG:129) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ErNbO4 (SG:15) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ti2Be17 (SG:166) by Materials Project

DOE Data Explorer

Kristin Persson

2015-01-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Pr2SO2 (SG:164) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Yb2SO2 (SG:164) by Materials Project

DOE Data Explorer

Kristin Persson

2015-01-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TiCo2Sn (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2015-02-09

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ni2P (SG:189) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on PuCo3 (SG:166) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TbGaPd (SG:62) by Materials Project

DOE Data Explorer

Kristin Persson

2015-01-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on BaTiO3 (SG:123) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on PrNbO4 (SG:15) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Er2SO2 (SG:164) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on LuB6 (SG:221) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Bi2O3 (SG:224) by Materials Project

DOE Data Explorer

Kristin Persson

2015-01-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Cd3In (SG:221) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Lu2SO2 (SG:164) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Mn3O4 (SG:141) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on FeClO (SG:59) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on U2Se3 (SG:62) by Materials Project

DOE Data Explorer

Kristin Persson

2015-01-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Tb2SO2 (SG:164) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on AlPt3 (SG:221) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Mn2Nb (SG:194) by Materials Project

DOE Data Explorer

Kristin Persson

2015-01-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on TaBe12 (SG:139) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on FeS2 (SG:58) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on Ni12P5 (SG:87) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on ThRe2 (SG:194) by Materials Project

DOE Data Explorer

Kristin Persson

2015-01-27

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on PuGe2 (SG:141) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Materials Data on YCrO3 (SG:62) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations