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.
Cite
@article{arxiv.2008.03374,
title = {Accurate and numerically efficient r$^2$SCAN meta-generalized gradient approximation},
author = {James W. Furness and Aaron D. Kaplan and Jinliang Ning and John P. Perdew and Jianwei Sun},
journal= {arXiv preprint arXiv:2008.03374},
year = {2020}
}
Comments
To appear in the Journal of Physical Chemistry Letters. Updates to the main text for clarity regarding the interpolation functions. TPSS data added throughout for comparison. VASP real-space convergence test, determination of regularization parameter eta, additional figures and data added to Supplementary Material