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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Excitation energies, photoionization cross sections, and asymmetry parameters of the methyl and silyl radicals

Vertical excitation energies of the methyl and silyl radicals were inferred from ab initio electron propagator calculations on the electron affinities of CH{sub 3}{sup +} and SiH{sub 3}{sup +}. Photoionization cross sections and angular distribution of photoelectrons for the outermost orbitals of both CH{sub 3} and SiH{sub 3} radicals have been obtained with the Molecular Quantum Defect Orbital method. The individual ionization cross sections corresponding to the Rydberg channels to which the excitation of the ground state's outermost electron gives rise are reported. Despite the relevance of methyl radical in atmospheric chemistry and combustion processes, only data for the photon energy range of 10–11 eV seem to be available. Good agreement has been found with experiment for photoionization cross section of this radical. To our knowledge, predictions of the above mentioned photoionization parameters on silyl radical are made here for the first time, and we are not aware of any reported experimental measurements. An analysis of our results reveals the presence of a Cooper minimum in the photoionization of the silyl radical. The adequacy of the two theoretical procedures employed in the present work is discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics

Conventional machine-learning (ML) models in computational chemistry learn to directly predict molecular properties using quantum chemistry only for reference data. While these heuristic ML methods show quantum-level accuracy with speeds several orders of magnitude faster than traditional quantum chemistry methods, they suffer from poor extensibility and transferability; i.e., their accuracy degrades on large or new chemical systems. Incorporating quantum chemistry frameworks into the ML models directly solves this problem. Here we take the structure of semiempirical quantum mechanics (SEQM) methods to construct dynamically responsive Hamiltonians. SEQM methods use empirical parameters fitted to experimental properties to construct reduced-order Hamiltonians, facilitating much faster calculations than ab initio methods but with compromised accuracy. By replacing these static parameters with machine-learned dynamic values inferred from the local environment, we greatly improve the accuracy of the SEQM methods. Trained on molecular energies and atomic forces, these dynamically generated Hamiltonian parameters show a strong correlation with atomic hybridization and bonding. Trained with only about 60,000 small organic molecular conformers, the resulting model retains interpretability, extensibility, and transferability when testing on much larger chemical systems and predicting various molecular properties. Overall, this work demonstrates the virtues of incorporating physics-based descriptions with ML to develop models that are simultaneously accurate, transferable, and interpretable.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

First-Principles Investigation of Phase Transitions in RuNb, RuTa, and Ru-Nb-Ta Ultrahigh Temperature Shape Memory Alloys

Ultrahigh temperature shape memory alloys (UHT-SMAs) have transition temperatures above 600 C, and they have found applications for sensing and actuating devices in aerospace industry. Among very few such UHT-SMAs currently known are Ru-based alloys such as RuNb and RuTa, whose martensite structures and phase transitions are totally different from those of NiTi-based SMAs and were poorly understood. In this work, we carried out a systematical study of RuNb using first-principles total energy calculations and molecular dynamics (MD) simulations. The transition paths and mechanisms in cubic → tetragonal →monoclinic transitions are revealed. and the transition sequence and martensitic transition temperatures are determined (MTTs) by evaluating the Gibbs free energies using thermodynamic integration. The calculated MTTs are in very good agreement with the experimental data. We found that the monoclinic phase at the second transition has the 𝑃21/m symmetry instead of experimentally identified 𝑃2/m. Our calculations demonstrate that RuTa has very similar phase transitions to those of RuTa. Furthermore, we studied the Ru0.5Nb0.25Ta0.25 ternary. Our results can explain the measured significant drop of MTT (~ 200 C) in the second transition for Ru0.5Nb0.25Ta0.25 compared with those of RuNb and RuTa, while in the first transition its MTT is between MTTs of RuNb and RuTa. The insights gained by this study and the verified ab initio methods for accurate MTT calculations can be applied to fast screen and quantitatively design novel UHT-SMAs having similar properties with desirable MTTs and much reduced cost.

Zhigang Wu↗
DOE OSTI2022

express: Extensible, high-level workflows for swifter ab initio materials modeling

In this work, we introduce an open-source Julia project, express, an extensible, lightweight, high-throughput, high-level workflow framework that aims to automate ab initio calculations for the materials science community. express is shipped with well-tested workflow templates, including structure optimization, equation of state (EOS) fitting, phonon spectrum (lattice dynamics) calculation, and thermodynamic property calculation in the framework of the quasi-harmonic approximation (QHA). It is designed to be highly modularized so that its components can be reused across various occasions, and customized workflows can be built on top of that. Users can also track the status of workflows in real-time, and rerun failed jobs thanks to the data lineage feature express provides. Finally, two working examples, i.e., all workflows applied to lime and akimotoite, are also presented in the code and this paper.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deformed spin- 1 2 square lattice in antiferromagnetic NaZnVOPO 4 ( HPO 4 )

In this work, we report the structural and magnetic properties of a new spin-$\frac{1}{2}$ antiferromagnet NaZnVOPO 4 (HPO 4 ) studied via x-ray diffraction, magnetic susceptibility, high-field magnetization, specific heat, and 31 P nuclear magnetic resonance (NMR) measurements, as well as density-functional band-structure calculations. While thermodynamic properties of this compound are well described by the J 1 – J 2 square-lattice model, ab initio calculations suggest a significant deformation of the spin lattice. From fits to the magnetic susceptibility we determine the averaged nearest-neighbor and second-neighbor exchange couplings of $\bar{J}$ 1 ≃ – 1.3 K and $\bar{J}$ 2 ≃ 5.6 K , respectively, resulting in the effective frustration ratio α = $\bar{J}$ 2 – $\bar{J}$ 1 ≃ – 4.3 that implies columnar antiferromagnetic order as the ground state. Experimental saturation field of 15.3 T is consistent with these estimates if 20% spatial anisotropy in J 1 is taken into account. Specific heat data signal the onset of a magnetic long-range order at T N ≃ 2.1 K , which is further supported by a sharp peak in the NMR spin-lattice relaxation rate. The NMR spectra mark the superposition of two P lines due to two nonequivalent P sites where the broad line with the strong hyperfine coupling and short T 1 is identified as the P(1) site located within the magnetic planes, while the narrow line with the weak hyperfine coupling and long T 1 is designated as the P(2) site located between the planes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

NbZr_BCC_SolidSolution_128atoms_VASP6

We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys niobium-zirconium (Nb-Zr). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach \cite{PAW}. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the elements Nb and Zr. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect bcc lattice cites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,100 randomized atomic structures over 31 chemical compositions. The calculations have been collected on NERSC-Perlmutter and OLCF-Summit using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Cases 1-10 have been run without generating the file 'vaspout.h5'. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for binary alloys is AXBY, where A and B refer to the constituents, whereas X and Y are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: FILES contained in each subdirectory with name "case-N" where N ranges between 11 and 100, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. 0.POSCAR: input file that defines the atomic structure of a system 4. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 7. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. OUTCAR: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system FILES contained in each subdirectory with name "case-N" where N ranges between 1 and 10, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. {ID}.POSCAR: input file that defines the atomic structure of a system at the beginning of ID execution of geometry optimization with PREC=LOW 4. {ID}.CONTCAR: output file that provides the atomic positions and cell parameters at the end of ID execution of geometry optimization with PREC=LOW in the INCAR file 5. {ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of geometry optimization that has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.{ID}.out: file with diagnostic information about the execution of the ID execution of the geometry optimization with precision variable set to PREC=Low in the INCAR file 7. N{ID}.POSCAR: input file that defines the atomic structure of a system after the geometry optimization run at low precision. This represents the input for the ID execution of the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. N{ID}.CONTCAR: output file that provides the atomic positions and cell parameters after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 9. N{ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.{ID}.out: file with diagnostic information about the ID execution of geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216. REFERENCES (1) Kresse, G. & Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. review B 47, 558 (1993). (2) Kresse, G. & Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal–amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251 (1994) (3) Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. materials science 6, 15–50 (1996) (4) Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. review B 54, 11169 (1996) (5) Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. review b 59, 1758 (1999)

36 MATERIALS SCIENCE↗
DOE OSTI2024

TaZr_BCC_SolidSolution_128atoms_VASP6

We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys tantalum-zirconium (Ta-Zr). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach \cite{PAW}. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the elements Ta and Zr. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect bcc lattice cites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,100 randomized atomic structures over 31 chemical compositions. The calculations have been collected on NERSC-Perlmutter and OLCF-Summit using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Cases 1-10 have been run without generating the file 'vaspout.h5'. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for binary alloys is AXBY, where A and B refer to the constituents, whereas X and Y are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: FILES contained in each subdirectory with name "case-N" where N ranges between 11 and 80, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. 0.POSCAR: input file that defines the atomic structure of a system 4. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 7. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. OUTCAR: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system FILES contained in each subdirectory with name "case-N" where N ranges between 1 and 10 and between 81 and 100, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. {ID}.POSCAR: input file that defines the atomic structure of a system at the beginning of ID execution of geometry optimization with PREC=LOW 4. {ID}.CONTCAR: output file that provides the atomic positions and cell parameters at the end of ID execution of geometry optimization with PREC=LOW in the INCAR file 5. {ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of geometry optimization that has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.{ID}.out: file with diagnostic information about the execution of the ID execution of the geometry optimization with precision variable set to PREC=Low in the INCAR file 7. N{ID}.POSCAR: input file that defines the atomic structure of a system after the geometry optimization run at low precision. This represents the input for the ID execution of the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. N{ID}.CONTCAR: output file that provides the atomic positions and cell parameters after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 9. N{ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.{ID}.out: file with diagnostic information about the ID execution of geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216. REFERENCES (1) Kresse, G. & Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. review B 47, 558 (1993). (2) Kresse, G. & Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal–amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251 (1994) (3) Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. materials science 6, 15–50 (1996) (4) Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. review B 54, 11169 (1996) (5) Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. review b 59, 1758 (1999)

36 MATERIALS SCIENCE↗
DOE OSTI2023

Parallel Implementation of Nonadditive Gaussian Process Potentials for Monte Carlo Simulations

A strategy is presented to implement Gaussian process potentials in molecular simulations through parallel programming. Attention is focused on the three-body nonadditive energy, though all algorithms extend straightforwardly to the additive energy. The method to distribute pairs and triplets between processes is general to all potentials. Results are presented for a simulation box of argon, including full box and atom displacement calculations, which are relevant to Monte Carlo simulation. Data on speed-up are presented for up to 120 processes across four nodes. A 4-fold speed-up is observed over five processes, extending to 20-fold over 40 processes and 30-fold over 120 processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Atomistic k ⋅ p theory

Pseudopotentials, tight-binding models, and k ⋅ p theory have stood for many years as the standard techniques for computing electronic states in crystalline solids. Here, we present the first new method in decades, which we call atomistic k ⋅ p theory. In its usual formulation, k ⋅ p theory has the advantage of depending on parameters that are directly related to experimentally measured quantities, however, it is insensitive to the locations of individual atoms. We construct an atomistic k ⋅ p theory by defining envelope functions on a grid matching the crystal lattice. The model parameters are matrix elements which are obtained from experimental results or ab initio wave functions in a simple way. This is in contrast to the other atomistic approaches in which parameters are fit to reproduce a desired dispersion and are not expressible in terms of fundamental quantities. This fitting is often very difficult. We illustrate our method by constructing a four-band atomistic model for a diamond/zincblende crystal and show that it is equivalent to the sp{sup 3} tight-binding model. We can thus directly derive the parameters in the sp{sup 3} tight-binding model from experimental data. We then take the atomistic limit of the widely used eight-band Kane model and compute the band structures for all III–V semiconductors not containing nitrogen or boron using parameters fit to experimental data. Our new approach extends k ⋅ p theory to problems in which atomistic precision is required, such as impurities, alloys, polytypes, and interfaces. It also provides a new approach to multiscale modeling by allowing continuum and atomistic k ⋅ p models to be combined in the same system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Energetic and Electronic Properties of AcX and LaX (X = O and F)

The bonding and spectroscopic properties of LaX and AcX (X = O and F) diatomic molecules were studied by highlevel ab initio CCSD(T) and SO-CASPT2 electronic structure calculations. Bond dissociation energies (BDEs) were calculated at the Feller−Peterson−Dixon (FPD) level. Potential energy curves and spectroscopic constants for the lowest-lying spin−orbit Ω states were obtained at the SO-CASPT2/aQ-DK level. A dense manifold of excited states was described for the monofluorides with the ground states well separated from the excited states. The spectroscopic parameters were in good agreement with those reported experimentally for LaO and LaF. For the diatomic molecules containing actinides, no experimental data of these parameters was found, but the results were consistent with other high-level calculations. The BDEs calculated at the FPD level were 791.3 (LaO), 705.2 (AcO), 650.0 (LaF), and 678.6 (AcF) kJ/ mol. The NBO analysis showed that the monofluorides are essentially ionic, which explains why the BDE(AcF) is higher than BDE(LaF); for the monoxides, covalent contributions involving the d orbitals of the metal and the p orbitals of the oxygen are stronger for LaO than AcO, which explains the higher BDE for LaO. Finally, the bond orders are predicted to be 2 for LaF and AcF, 3 for AcO, and higher than 3 for LaO.

Basis sets↗
DOE OSTI2013

Comparison between Gaussian-type orbitals and plane wave ab initio density functional theory modeling of layer silicates: Talc [Mg{sub 3}Si{sub 4}O{sub 10}(OH){sub 2}] as model system

The quantum chemical characterization of solid state systems is conducted with many different approaches, among which the adoption of periodic boundary conditions to deal with three-dimensional infinite condensed systems. This method, coupled to the Density Functional Theory (DFT), has been proved successful in simulating a huge variety of solids. Only in relatively recent years this ab initio quantum-mechanic approach has been used for the investigation of layer silicate structures and minerals. In the present work, a systematic comparison of different DFT functionals (GGA-PBEsol and hybrid B3LYP) and basis sets (plane waves and all-electron Gaussian-type orbitals) on the geometry, energy, and phonon properties of a model layer silicate, talc [Mg{sub 3}Si{sub 4}O{sub 10}(OH){sub 2}], is presented. Long range dispersion is taken into account by DFT+D method. Results are in agreement with experimental data reported in literature, with minimal deviation given by the GTO/B3LYP-D* method regarding both axial lattice parameters and interaction energy and by PW/PBE-D for the unit-cell volume and angular values. All the considered methods adequately describe the experimental talc infrared spectrum.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

High pressure behaviour of uranium dicarbide (UC{sub 2}): Ab-initio study

The structural stability of uranium dicarbide has been examined under hydrostatic compression employing evolutionary structure search algorithm implemented in the universal structure predictor: evolutionary Xtallography (USPEX) code in conjunction with ab-initio electronic band structure calculation method. The ab-initio total energy calculations involved for this purpose have been carried out within both generalized gradient approximations (GGA) and GGA + U approximations. Our calculations under GGA approximation predict the high pressure structural sequence of tetragonal → monoclinic → orthorhombic for this material with transition pressures of ∼8 GPa and 42 GPa, respectively. The same transition sequence is predicted by calculations within GGA + U also with transition pressures placed at ∼24 GPa and ∼50 GPa, respectively. Further, on the basis of comparison of zero pressure equilibrium volume and equation of state with available experimental data, we find that GGA + U approximation with U = 2.5 eV describes this material better than the simple GGA approximation. The theoretically predicted high pressure structural phase transitions are in disagreement with the only high experimental study by Dancausse et al. [J. Alloys. Compd. 191, 309 (1993)] on this compound which reports a tetragonal to hexagonal phase transition at a pressure of ∼17.6 GPa. Interestingly, during lowest enthalpy structure search using USPEX, we do not see any hexagonal phase to be closer to the predicted monoclinic phase even within 0.2 eV/f. unit. More experiments with varying carbon contents in UC{sub 2} sample are required to resolve this discrepancy. The existence of these high pressure phases predicted by static lattice calculations has been further substantiated by analyzing the elastic and lattice dynamic stability of these structures in the pressure regimes of their structural stability. Additionally, various thermo-physical quantities such as equilibrium volume, bulk modulus, Debye temperature, thermal expansion coefficient, Gruneisen parameter, and heat capacity at ambient conditions have been determined from these calculations and compared with the available experimental data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Insights and challenges of applying the GW method to transition metal oxides

The ab initio GW approach is considered as the most accurate approach for calculating the band gaps of semiconductors and insulators. However, its application to transition metal oxides (TMOs) has been hindered by the failure of traditional approximations developed for conventional semiconductors. In this work, we examine the effects of these approximations on the values of band gaps for ZnO, Cu 2 O, and TiO 2 . Specifically, we explore the origin of the differences between the two widely used plasmon-pole models. Based on the comparison of our results with the experimental data and previously published calculations, we discuss which approximations are suitable for TMOs and why.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

No-core shell model calculations of the photonuclear cross section of 10B

Results of ab initio no-core, shell model calculations for the photonuclear cross section of 10B are presented using realistic two-nucleon (NN) chiral forces up to next-to-next-to-next-order (N3LO) softened by the similarity renormalization group method (SRG) with λ=2.02 fm -1 . The electric-dipole response function is calculated using the Lanczos method, with the effects of the continuum included via neutron escape widths derived from R-matrix theory and using the Lorentz integral transform method. The calculated cross section agrees well with experimental data in terms of structure as well as in absolute peak height, σ max =4.85 mb at photon energy ω=23.61 MeV, and integrated cross section 85.36 MeV. mb. We also test the Brink hypothesis by calculating the electric-dipole response for the first nine positive-parity states with J≠0 in 10 B and verify that dipole excitations built upon the ground and excited states have similar characteristics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1988

Oscillator strengths for Si II

New accurate results are presented for absorption oscillator strengths of Si II resonance lines. In these ab initio calculations, an extensive basis set of 67 electronic configuration are employed, including fine-structure states and relativistic effects in the Breit-Pauli approximation. For the strong Si II lines, good agreement is found with previous LS-coupling f-values of Dufton et al. (1983). Oscillator strengths of selected weaker lines disagree in some cases with empirically determined f-values by Shull, Snow, and York (1981) and by Van Buren (1986). The theoretical results are combined with curve-of-growth analyses of Copernicus and IUE data to yield a list of recommended Si II oscillator strengths consistent with the errors in both theoretical and observational analyses. These f-values have been used (Van Steenberg and Shull) for a Galactic survey of interstellar silicon abundances with the International Ultraviolet Explorer satellite.

Luo, Ding↗
NASA NTRS1997

An Accurate Potential Energy Surface for H2O

We have carried out extensive high quality ab initio electronic structure calculations of the ground state potential energy surface (PES) and dipole moment function (DMF) for H2O. A small adjustment is made to the PES to improve the agreement of line positions from theory and experiment. The theoretical line positions are obtained from variational ro-vibrational calculations using the exact kinetic energy operator. For the lines being fitted, the root-mean-square error was reduced from 6.9 to 0.08 /cm. We were then able to match 30,092 of the 30,117 lines from the HITRAN 96 data base to theoretical lines, and 80% of the line positions differed less than 0.1 /cm. About 3% of the line positions in the experimental data base appear to be incorrect. Theory predicts the existence of many additional weak lines with intensities above the cutoff used in the data base. To obtain results of similar accuracy for HDO, a mass dependent correction to the PH is introduced and is parameterized by simultaneously fitting line positions for HDO and D2O. The mass dependent PH has good predictive value for T2O and HTO. Nonadiabatic effects are not explicitly included. Line strengths for vibrational bands summed over rotational levels usually agree well between theory and experiment, but individual line strengths can differ greatly. A high temperature line list containing about 380 million lines has been generated using the present PES and DMF

Schwenke, David W.↗
NASA NTRS2002

Theoretical Study of Decomposition Pathways for HArF and HKrF

To provide theoretical insights into the stability and dynamics of the new rare gas compounds HArF and HKrF, reaction paths for decomposition processes HRgF to Rg + HF and HRgF to H + Rg + F (Rg = Ar, Kr) are calculated using ab initio electronic structure methods. The bending channels, HRgF to Rg + HF, are described by single-configurational MP2 and CCSD(T) electronic structure methods, while the linear decomposition paths, HRgF to H + Rg + F, require the use of multi-configurational wave functions that include dynamic correlation and are size extensive. HArF and HKrF molecules are found to be energetically stable with respect to atomic dissociation products (H + Rg + F) and separated by substantial energy barriers from Rg + HF products, which ensure their kinetic stability. The results are compatible with experimental data on these systems.

Chaban, Galina M.↗
DOE OSTI2020

Merging ab initio theory and few-body approach for (d, p) reactions

A framework for A(d, p)B reactions is introduced by merging the microscopic approach to computing the properties of the nucleon-target systems and the three-body n + p + A reaction formalism, thus providing a consistent link between the reaction cross sections and the underlying microscopic structure. In this first step toward a full microscopic description, we focus on the inclusion of the neutron-target microscopic properties. The properties of the neutron-target subsystem are encapsulated in the Green's function which is computed with the coupled Cluster theory using a chiral nucleon-nucleon and three-nucleon interactions. Subsequently, this many-body information is introduced in the few-body Green's Function Transfer approach to (d, p) reactions. Our benchmarks on stable targets 40,48 Ca show an excellent agreement with the data. We then proceed to make specific predictions for (d, p) on neutron rich 52,54 Ca isotopes. These predictions are directly relevant to testing the new magic numbers N = 32, 34 and are expected to be feasible in the first campaign of the projected FRIB facility.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗