Materials informatics for discovery of ion conductive ceramics for batteries

In this review, we introduce our work in the field of materials informatics for prediction ionic conduction properties inorganic crystalline solids. Rational material development based on information-derived conductivity listed crystal structure database is attractive to reduce processing time and labor costs. For purpose, general descriptors a sufficient volume datasets are required. As an example, herein describe machine learning regression Bayes optimization schemes their results by using histogram bond valence-based force approach.