MCML: Combining physical constraints with experimental data for a multi‐purpose meta‐generalized gradient approximation. Issue 28 (18th August 2021)

• Record Type: Journal Article
• Authors: Brown, Kristopher; Maimaiti, Yasheng; Trepte, Kai; Bligaard, Thomas; Voss, Johannes
• Is Part Of: Journal of computational chemistry. Volume 42:Issue 28(2021)
• Page Start: 2004
• Page End: 2013
• Publication Date: 2021-08-18
• Subjects: density functional theory -- materials predictions -- MCML -- meta‐generalized gradient approximation -- surface chemistry; Chemistry -- Data processing -- Periodicals; 542.85
• DOI: 10.1002/jcc.26732
• Languages: English
• ISSNs: 0192-8651

Abstract

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. Abstract : MCML is an empirical exchange‐correlation density functional which obeys physical constraints and was trained against experimental data. This combined physical modeling and data scientific approach leads to simultaneous improvements of predictions for crystal structural properties and bulk, surface, and gas phase reaction energetics. The spread of the shown ensemble of perturbations to the MCML exchange enhancement accounts for potential uncertainties in the empirical functional form, enabling the estimation of errors of computed materials predictions.