arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2008.03374cond-mat.mtrl-sciphysics.chem-ph
收藏

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

James W. Furness, Aaron D. Kaplan, Jinliang Ning, John P. Perdew, Jianwei Sun

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑