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

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.

Automated summary

By extending the generalized gradient approximation to density functionals depending on the electronic kinetic energy density, the predictive power of density functional theory for materials properties can be improved without increased computational complexity. The proposed empirical meta-GGA model is based on physical constraints and experimental data, resulting in improved surface and gas phase reaction energetics without sacrificing the accuracy of bulk property predictions.