# Accurate and Numerically Efficient r2SCAN Meta-Generalized Gradient Approximation

“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.

- Authors: James W. Furness, Aaron D. Kaplan, Jinliang Ning, John P. Perdew, Jianwei Sun
- Published: September 2, 2020
- Field: Physics and Astronomy
- DOI: https://doi.org/10.1021/acs.jpclett.0c02405
- Free PDF: https://arxiv.org/pdf/2008.03374

## 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.

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