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

TL;DR

This paper introduces a new meta-GGA functional that combines the numerical efficiency of rSCAN with the constraint adherence and accuracy of the original SCAN functional, improving computational reliability.

Contribution

The authors develop a constraint-adhering meta-GGA functional that retains rSCAN's numerical advantages and SCAN's accuracy, bridging the gap between efficiency and fidelity.

Findings

Maintains numerical efficiency of rSCAN

Restores the exact constraint adherence of SCAN

Achieves transferable accuracy comparable to SCAN

Abstract

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 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.