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

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 nonlocal 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.

Automated summary

This paper presents an empirical approach to improve the estimation of surface chemical reaction energetics. By optimizing the exchange-correlation functional with a combination of reference data and physical model constraints, the proposed method achieves accurate predictions not only for surface chemistry simulations but also for gas phase reactions and bulk lattice constants and elastic properties.