Performance improvement of deorbitalized exchange-correlation functionals
Deorbitalization of a conventional meta-generalized-gradient exchange-correlation approximation replaces its dependence upon the Kohn-Sham (KS) kinetic energy density with a dependence on the density gradient and Laplacian. In principle, that simplification should provide improved computational performance relative to the original meta-generalized-gradient-approximation form because of the shift from an orbital-dependent generalized KS potential to a true KS local potential. Often that prospective gain is lost because of problematic roughness in the density caused by the density Laplacian and consequent roughness in the exchange-correlation potential from the resulting higher-order spatial derivatives of the density in it. We address the problem by constructing a deorbitalizer based on the ``${\mathrm{r}}^{2}\mathrm{SCAN}$ piecewise polynomial'' (RPP) deorbitalizer from A. D. Kaplan and J. P. Perdew [Phys. Rev. Mater. 6, 083803 (2022)] with comparative smoothness of the potential along with retention of constraint satisfaction as design goals. Applied to the ${\mathrm{r}}^{2}\mathrm{SCAN}$ exchange-correlation functional presented by J. W. Furness et al. [J. Phys. Chem. Lett. 11, 8208 (2020)], we find substantial timing improvements for solid-state calculations over both ${\mathrm{r}}^{2}\mathrm{SCAN}$ and its earlier deorbitalization for high-precision calculations of structural properties, while improving upon the accuracy of RPP deorbitalization for both solids and molecules.
Authors
- Samuel B. Trickey (ORCID: https://orcid.org/0000-0001-9224-6304)
- H. Francisco (ORCID: https://orcid.org/0000-0001-6163-8436)
- Antonio C. Cancio (ORCID: https://orcid.org/0000-0001-6313-0329)
Institutions
- George Mason University (US)
- Ball State University (US)
- University of Florida (US)
Publication Details
- Journal
- Physical Review Materials
- Published
- 2026-04-03
- DOI
- https://doi.org/10.1103/rbf1-cp89
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation