Accurate and Numerically Efficient r<sup>2</sup>SCAN Meta-Generalized Gradient Approximation
James W. Furness, Aaron D. Kaplan, Jinliang Ning, John P. Perdew, Jianwei Sun
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.
Topics & Concepts
Constraint (computer-aided design)MathematicsApplied mathematicsConstruct (python library)PhysicsMathematical analysisComputer scienceGeometryProgramming languageAdvanced NMR Techniques and ApplicationsMachine Learning in Materials ScienceAdvanced Chemical Physics Studies