Data-driven and constrained optimization of semi-local exchange and non-local correlation functionals for materials and surface chemistry

TL;DR

This paper introduces the VCML-rVV10 functional, an empirically optimized density functional that improves predictions of surface chemistry, bulk properties, and gas-phase reactions by combining data-driven and physical constraints.

Contribution

It develops a new exchange-correlation functional optimized through empirical data and physical constraints, enhancing accuracy across multiple material and surface chemistry applications.

Findings

Improved surface reaction energetics predictions.

Accurate bulk lattice constants and elastic properties.

Enhanced gas-phase reaction energetics.

Abstract

Reliable predictions of surface chemical reaction energetics require an accurate description of both chemisorption and physisorption. Here, we present an empirical approach to simultaneously optimize semi-local exchange and non-local correlation of a density functional approximation to improve these energetics. A combination of reference data for solid bulk, surface, and gas-phase chemistry and physical exchange-correlation model constraints leads to the VCML-rVV10 exchange-correlation functional. Owing to the variety of training data, the applicability of VCML-rVV10 extends beyond surface chemistry simulations. It provides optimized gas phase reaction energetics and an accurate description of bulk lattice constants and elastic properties.