Accurate and Numerically Efficient r<sup>2</sup>SCAN Meta-Generalized Gradient Approximation
James W. FurnessDepartment of Physics and Engineering Physics, Tulane University, New Orleans, Louisiana 70118, United StatesAaron D. KaplanDepartment of Physics, Temple University, Philadelphia, Pennsylvania 19122, United StatesJinliang NingDepartment of Physics and Engineering Physics, Tulane University, New Orleans, Louisiana 70118, United StatesJohn P. PerdewDepartment of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United StatesJianwei SunDepartment of Physics and Engineering Physics, Tulane University, New Orleans, Louisiana 70118, United States
2020en
ABI
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.
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