Accurate and numerically efficient r$^2$SCAN meta-generalized gradient approximation
By J. Furness, Aaron D. Kaplan, J. Ning, J. Perdew, Jianwei Sun Tulane University, N. Orleans., T. University, Philadelphia. 2020
337 citations recorded by the source corpus.
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.
Subfields: Geophysics, Nuclear and High Energy Physics, Spectroscopy
View Paper: https://www.semanticscholar.org/paper/40a44f27864f4dce670d431879b15bdcda395cdf
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Citations in the corpus
- A foundation model for atomistic materials chemistry. (2023, positive): “Recent developments in DFT are beginning to supersede it by achieving improved accuracy at comparable computational cost ( 103, 104 ), and methods beyond DFT such as hybrid functionals ( 105 ) and the random phase approximation ( 106 ) improve upon this even further but at much larger computational…”
- Scaling deep learning for materials discovery (2023, background): “Standard projector augmented wave (PAW)-Perdew– Burke–Ernzerhof (PBE) potentials provided a speed–accuracy trade-off suited for large-scale discovery 40,41 , but the r 2 SCAN functional provides a more accurate meta-generalized gradient approximation 29,42,43 .”