Density functional theory and material databases in the era of machine learning

Abstract

This perspective article presents the density functional theory and traces its evolution. With the advancement in density functional theory-based computations and the efforts to collate the data generated through density functional theory, the field now has a good repository/database of materials and their properties. This repository, though not as substantial as generally used for machine learning, has nonetheless made it possible to combine density functional theory and machine learning. This article highlights current research challenges and presents an optimistic outlook for the future of “Density Functional Theory with Machine Learning” by discussing some specific examples.