Deep dive into machine learning density functional theory for materials science and chemistry

With the growth of computational resources, the scope of electronic structure simulations has increased greatly. Artificial intelligence and robust data analysis hold the promise to accelerate large-scale simulations and their analysis to hitherto unattainable scales. Machine learning is a rapidly growing field for the processing of such complex data sets. It has recently gained traction in the domain of electronic structure simulations, where density functional theory (DFT) takes the prominent role of the most widely used electronic structure method. Thus, DFT calculations represent one of the largest loads on academic high-performance computing systems across the world. Accelerating these with machine learning can reduce the resources required and enables simulations of larger systems. Hence, the combination of DFT and machine learning has the potential to rapidly advance electronic structure applications such as in silico materials discovery and the search for new chemical reaction pathways. We provide the theoretical background of both DFT and machine learning on a generally accessible level. This serves as the basis of our comprehensive review, including research articles up to December 2020 in chemistry and materials science that employ machine-learning techniques. In our analysis, we categorize the body of research into main threads and extract impactful results. We conclude our review with an outlook on exciting research directions in terms of a citation analysis.

Automated summary

With the growth of computational resources, machine learning has gained traction in electronic structure simulations, offering the potential to reduce resource requirements and accelerate materials discovery and chemical reaction pathway research.