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Accurate and Numerically Efficient r<sup>2</sup>SCAN Meta-Generalized Gradient Approximation

Furness, JW
Kaplan, AD
Ning, J
Perdew, JP
Sun, J
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Pre-print
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2020-10-01
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10.1021/acs.jpclett.0c02405
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Copyright © 2020 American Chemical Society. The recently proposed rSCAN functional [ J. Chem. Phys. 2019 150, 161101 ] is a regularized form of the SCAN functional [ Phys. Rev. Lett. 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.
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Journal of Physical Chemistry Letters
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