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

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 نشر من قبل Aaron Kaplan
 تاريخ النشر 2020
  مجال البحث فيزياء
والبحث باللغة English
 تأليف James W. Furness




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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 SCANs 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 rSCANs numerical performance while restoring the transferable accuracy of SCAN.



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