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arXiv · 2008.03374

Accurate and numerically efficient r$^2$SCAN meta-generalized gradient approximation

James W. Furness·Aaron D. Kaplan·Jinliang Ning·John P. Perdew·Jianwei Sun
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Abstract

The recently proposed rSCAN functional [J. Chem. Phys. 150, 161101 (2019)] is a regularized form of the SCAN functional [Phys. Rev. Lett. 115, 036402 (2015)] 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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James W. Furness, Aaron D. Kaplan, Jinliang Ning, John P. Perdew, Jianwei Sun. 2020-08-07. Accurate and numerically efficient r$^2$SCAN meta-generalized gradient approximation. https://arxiv.org/abs/2008.03374

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