Computational design of stable and highly ion-conductive materials using multi-objective bayesian optimization: Case studies on diffusion of oxygen and lithium

Ion-conducting solid electrolytes are widely used for a variety of purposes. Therefore, there is a high demand for the design of highly ion-conductive materials. Theoretical simulations have become effective tools for investigating the performance of ion-conductive materials because of advancements of computers and computational codes, respectively. However, it can be significantly expensive to conduct an extensive search using theoretical computations. Further, dynamic conductivity and static stability must be simultaneously satisfied for practical applications. Therefore, in this study, we propose a computational framework that simultaneously optimizes dynamic conductivity and static stability; this is achieved by combining theoretical calculations and the Pareto hyper-volume criterion-based Bayesian multi-objective optimization. In our framework, we iteratively select the candidate material that maximizes the expected increase in the Pareto hyper-volume criterion; this is a standard optimality criterion of multi-objective optimization. We show that ion-conductive materials with high dynamic conductivity and static stability can be efficiently identified by our framework via two case studies on diffusion of oxygen and lithium.