A simple generalized gradient approximation for the noninteracting kinetic energy density functional
Abstract
A simple, novel, non-empirical, constraint-based orbital-free generalized gradient approximation (GGA) non-interacting kinetic energy density functional is presented along with illustrative applications. The innovation is adaptation of constraint-based construction to the essential properties of pseudo-densities from the pseudo-potentials that are essential in plane-wave-basis ab initio molecular dynamics. This contrasts with constraining to the qualitatively different Kato-cusp-condition densities. The single parameter in the new functional is calibrated by satisfying Pauli potential positivity constraints for pseudo-atom densities. Finally, in static lattice tests on simple metals and semiconductors, the new LKT functional outperforms the previous best constraint-based GGA functional, VT84F (Phys. Rev. B 88, 161108(R) (2013)), is generally superior to a recently proposed meta-GGA, is reasonably competitive with parametrized two-point functionals, and is substantially faster.
- Authors:
-
- Univ. of Florida, Gainesville, FL (United States). Quantum Theory Project, Dept. of Physics
- Univ. of Rochester, NY (United States). Lab. for Laser Energetics
- Univ. of Florida, Gainesville, FL (United States). Quantum Theory Project, Dept. of Physics and Dept. of Chemistry
- Publication Date:
- Research Org.:
- Univ. of Rochester, NY (United States). Lab. for Laser Energetics
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1465779
- Alternate Identifier(s):
- OSTI ID: 1461718
- Grant/Contract Number:
- NA0001944; SC0002139
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Physical Review B
- Additional Journal Information:
- Journal Volume: 98; Journal Issue: 4; Journal ID: ISSN 2469-9950
- Publisher:
- American Physical Society (APS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY
Citation Formats
Luo, Kai, Karasiev, Valentin V., and Trickey, S. B. A simple generalized gradient approximation for the noninteracting kinetic energy density functional. United States: N. p., 2018.
Web. doi:10.1103/PhysRevB.98.041111.
Luo, Kai, Karasiev, Valentin V., & Trickey, S. B. A simple generalized gradient approximation for the noninteracting kinetic energy density functional. United States. https://doi.org/10.1103/PhysRevB.98.041111
Luo, Kai, Karasiev, Valentin V., and Trickey, S. B. Thu .
"A simple generalized gradient approximation for the noninteracting kinetic energy density functional". United States. https://doi.org/10.1103/PhysRevB.98.041111. https://www.osti.gov/servlets/purl/1465779.
@article{osti_1465779,
title = {A simple generalized gradient approximation for the noninteracting kinetic energy density functional},
author = {Luo, Kai and Karasiev, Valentin V. and Trickey, S. B.},
abstractNote = {A simple, novel, non-empirical, constraint-based orbital-free generalized gradient approximation (GGA) non-interacting kinetic energy density functional is presented along with illustrative applications. The innovation is adaptation of constraint-based construction to the essential properties of pseudo-densities from the pseudo-potentials that are essential in plane-wave-basis ab initio molecular dynamics. This contrasts with constraining to the qualitatively different Kato-cusp-condition densities. The single parameter in the new functional is calibrated by satisfying Pauli potential positivity constraints for pseudo-atom densities. Finally, in static lattice tests on simple metals and semiconductors, the new LKT functional outperforms the previous best constraint-based GGA functional, VT84F (Phys. Rev. B 88, 161108(R) (2013)), is generally superior to a recently proposed meta-GGA, is reasonably competitive with parametrized two-point functionals, and is substantially faster.},
doi = {10.1103/PhysRevB.98.041111},
journal = {Physical Review B},
number = 4,
volume = 98,
place = {United States},
year = {Thu Jul 26 00:00:00 EDT 2018},
month = {Thu Jul 26 00:00:00 EDT 2018}
}
Web of Science
Figures / Tables:
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