Accurate and Numerically Efficient r²SCAN Meta-Generalized Gradient Approximation

2020/09/02 by James W. Furness, Aaron D. Kaplan, Jinliang Ning +2 · 16 citations
Chemistry · Materials Science · Physics and Astronomy · #Advanced Chemical Physics Studies #Advanced NMR Techniques and Applications #Machine Learning in Materials Science

paper · doi:10.1021/acs.jpclett.0c02405

openalex publication_date 2020/09/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

2015 115, 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.

Citations

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