Improving Density Functional Prediction of Molecular Thermochemical Properties with a Machine-Learning-Corrected Generalized Gradient Approximation

The past decade has seen an increasing interest in designing sophisticated density functional approximations (DFAs) by integrating the power of machine learning (ML) techniques. However, application of the ML-based DFAs is often confined to simple model systems. In this work, we construct an ML correction to the widely used Perdew-Burke-Ernzerhof (PBE) functional by establishing a semilocal mapping from the electron density and reduced gradient to the exchange-correlation energy density. The resulting ML-corrected PBE is immediately applicable to any real molecule and yields significantly improved heats of formation while preserving the accuracy for other thermochemical and kinetic properties. This work highlights the prospect of combining the power of data-driven ML methods with physics-inspired derivations for reaching the heaven of chemical accuracy.

Automated summary

In the past decade, there has been a growing interest in designing sophisticated density functional approximations (DFAs) by integrating machine learning techniques. This study presents a machine learning correction to the widely used Perdew-Burke-Ernzerhof (PBE) functional, which improves heats of formation while maintaining accuracy for other properties. The research highlights the potential of combining data-driven machine learning methods with physics-inspired derivations for achieving chemical accuracy.