Fully autonomous materials screening methodology combining first-principles calculations, machine learning and high-performance computing system

Materials screening by high-throughput first-principles calculations is a powerful tool for exploring novel materials with preferable properties. Machine learning techniques are expected to accelerate materials screening by constructing surrogate models and making fast predictions. Especially, black-box optimization methods such as Bayesian optimization, repeating the construction of a prediction model and the selection of data points, have attracted much attention. In this study, we constructed an autonomous materials screening system using first-principles calculations and machine learning working on high-performance computing systems. The performance of the system was evaluated by applying the system to the exploration of high-k dielectrics using band gaps by hybrid functional calculations and dielectric constants by density functional perturbation theory calculations, respectively. The developed system identified materials with anomalous properties, as well as materials with both wide band gaps and high dielectric constants by utilizing appropriate black-box optimization methods, much faster than random exploration. The code for the developed system is published on an open repository.