MCML: Combining physical constraints with experimental data for a multi-purpose meta-generalized gradient approximation

Brown, Kristopher; Maimaiti, Yasheng; Trepte, Kai; Bligaard, Thomas; Voss, Johannes

doi:10.1002/jcc.26732

Title: MCML: Combining physical constraints with experimental data for a multi-purpose meta-generalized gradient approximation

Journal Article · 18 August 2021 · Journal of Computational Chemistry

DOI: https://doi.org/10.1002/jcc.26732 · OSTI ID:1818520

Brown, Kristopher ^[1]; Maimaiti, Yasheng ^[1];

^[2]; Bligaard, Thomas ^[3];

^[2]

Stanford Univ., CA (United States). SUNCAT Center for Interface Science and Catalysis; Stanford Univ., CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States)
Stanford Univ., CA (United States). SUNCAT Center for Interface Science and Catalysis; SLAC National Accelerator Lab., Menlo Park, CA (United States)
Technical Univ. of Denmark, Lyngby (Denmark)

The predictive power of density functional theory for materials properties can be improved without increasing the overall computational complexity by extending the generalized gradient approximation (GGA) for electronic exchange and correlation to density functionals depending on the electronic kinetic energy density in addition to the charge density and its gradient, resulting in a meta-GGA. Here, we propose an empirical meta-GGA model that is based both on physical constraints and on experimental and quantum chemistry reference data. Overall, the resulting optimized meta-GGA MCML yields improved surface and gas phase reaction energetics without sacrificing the accuracy of bulk property predictions of existing meta-GGA approaches.

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Research Organization:: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division

Grant/Contract Number:: AC02-76SF00515

OSTI ID:: 1818520

Journal Information:: Journal of Computational Chemistry, Journal Name: Journal of Computational Chemistry Journal Issue: 28 Vol. 42; ISSN 0192-8651

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
MCML
density functional theory
materials predictions
meta-generalized gradient approximation
surface chemistry

Title: MCML: Combining physical constraints with experimental data for a multi-purpose meta-generalized gradient approximation

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