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

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

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

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. 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.
Original languageEnglish
JournalJournal of Computational Chemistry
Volume42
Issue number28
Pages (from-to)2004-2013
Number of pages10
ISSN0192-8651
DOIs
Publication statusPublished - 2021

Keywords

  • Density functional theory
  • Materials prediction
  • MCML
  • Meta-generalized gradient approximation
  • Surface chemistry

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