“Accurate and Numerically Efficient r2SCAN Meta-Generalized Gradient Approximation” (2020), by James W. Furness et al. Physics and Astronomy research paper. Original abstract below, unchanged. A free PDF is available.

Accurate and Numerically Efficient r2SCAN Meta-Generalized Gradient Approximation

James W. Furness, Aaron D. Kaplan, Jinliang Ning et al. · September 2, 2020 · Physics and Astronomy

Abstract

The recently proposed rSCAN functional [J. Chem. Phys.2019150, 161101. ] is a regularized form of the SCAN functional [ Phys. Rev. Lett.2015115, 036402.] that improves SCAN’s numerical performance at the expense of breaking constraints known from the exact exchange–correlation functional. We construct a new meta-generalized gradient approximation by restoring exact constraint adherence to rSCAN. The resulting functional maintains rSCAN’s numerical performance while restoring the transferable accuracy of SCAN.

Free PDF DOI: 10.1021/acs.jpclett.0c02405