Rapid discovery of promising materials via active learning with multi-objective optimization

Developing efficient methods to find materials that satisfy multiple properties simultaneously is an important task so that the material screening process can be accelerated with reduced resources. In this study, the performance of active learning based on multi-objective Bayesian optimization is demonstrated for two well-known inorganic material databases. First, this method is applied to two-dimensional materials to optimize their electronic and mechanical properties. This indicates that the hypervolume-based method is more effective than the other methods of exploitation, exploration, and random selection. All optimal Pareto front (PF) can be found within sampling only from 16% to 23% of the entire search space using the expected hypervolume improvement (EHVI). To ensure the generality of the developed platform, we implemented this method on a general inorganic material database. Again, the EHVI performed better than the other methods in all cases. In the most datadeficient case, where the initially known data comprise 0.1% of the total data, 61% of the data in the entire search space are required to find the optimal PF. This is 36%p less than that of the random selection or exploitation methods. However, exploitation and exploration methods do not always perform worse than the EHVI. These methods are more effective than the EHVI in searching for more than a certain number of PFs. The results clearly demonstrate the applicability and efficacy of the multi-objective active learning platform. We believe that even with a small number of initial training databases, this method can accelerate the materialscreening process with significantly reduced resources.