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Cited by 1,438Advanced Chemical Physics StudiesMachine Learning in Materials ScienceAdvanced Physical and Chemical Molecular Interactions

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Accurate and Numerically Efficient r2SCAN Meta-Generalized Gradient Approximation

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Furness, James W., Kaplan, Aaron D., Ning, Jinliang, Perdew, John P., Sun, Jianwei·2020·The Journal of Physical Chemistry Letters
DOI 10.1021/acs.jpclett.0c02405
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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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Metadata

Authors
Furness, J. W., Kaplan, A. D., Ning, J., Perdew, J. P., & Sun, J.
Affiliations
  • Tulane University (US)
  • Temple University (US)
Corresponding author
  • Furness, James W.
  • Sun, Jianwei
ORCID
Venue
The Journal of Physical Chemistry Letters
Publisher
American Chemical Society (ACS)
Published
2020-09-02
Volume
11
Issue
19
Pages
8208-8215
ISSN
1948-7185 (ISSN-L)
DOI
10.1021/acs.jpclett.0c02405
OpenAlex
W3082215798
Cited by
1,438
References
4
Citations by year
2026: 440 · 2025: 395 · 2024: 287 · 2023: 171 · 2022: 101 · 2021: 40 · 2020: 4
FWCI
32.30
Type
journal article
Language
en
Open access
Licence
Locations
  • The Journal of Physical Chemistry Letters (publishedVersion, other-oa)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
  • TUScholarShare (Temple University) (submittedVersion)
  • arXiv (Cornell University)
Funders
Basic Energy Sciences (DE-SC0012575, DE-SC0019350 (core research))
Topics
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
Keywords
r2SCAN, meta-GGA, SCAN functional, exchange-correlation functionals, numerical stability, density functional theory

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CoChat. (2026). Citation check: Accurate and Numerically Efficient r2SCAN Meta-Generalized Gradient Approximation. https://app.cochat.ai/papers/10.1021/acs.jpclett.0c02405
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Furness, J. W., Kaplan, A. D., Ning, J., Perdew, J. P., & Sun, J. (2020). Accurate and Numerically Efficient r2SCAN Meta-Generalized Gradient Approximation. The Journal of Physical Chemistry Letters. https://doi.org/10.1021/acs.jpclett.0c02405