Computational Design of Stable and Highly Ion-conductive Materials using Multi-objective Bayesian Optimization: Case Studies on Diffusion of Oxygen and Lithium

TL;DR

This paper presents a computational framework that uses multi-objective Bayesian optimization to efficiently identify ion-conductive materials with high conductivity and stability, demonstrated through case studies on oxygen and lithium diffusion.

Contribution

It introduces a novel multi-objective Bayesian optimization approach combining theoretical calculations to optimize both conductivity and stability simultaneously.

Findings

Efficient identification of high-performance ion conductors

Successful application to oxygen and lithium diffusion case studies

Framework outperforms traditional exhaustive search methods

Abstract

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.