  Ion-conducting solid electrolytes are widely used for a variety of purposes.
Therefore, designing highly ion-conductive materials is in strongly demand.
Because of advancement in computers and enhancement of computational codes,
theoretical simulations have become effective tools for investigating the
performance of ion-conductive materials. However, an exhaustive search
conducted by theoretical computations can be prohibitively expensive. Further,
for practical applications, both dynamic conductivity as well as static
stability must be satisfied at the same time. Therefore, we propose a
computational framework that simultaneously optimizes dynamic conductivity and
static stability; this is achieved by combining theoretical calculations and
the Bayesian multi-objective optimization that is based on the Pareto
hyper-volume criterion. Our framework iteratively selects the candidate
material, which maximizes the expected increase in the Pareto hyper-volume
criterion; this is a standard optimality criterion of multi-objective
optimization. Through two case studies on oxygen and lithium diffusions, we
show that ion-conductive materials with high dynamic conductivity and static
stability can be efficiently identified by our framework.
