Chemical Composition-Driven Machine Learning Models for Predicting Ionic Conductivity in Lithium-Containing Oxides (Supporting Information)

A machine learning model that can predict the ionic conductivity of lithium-containing oxides using chemical composition and ionic conductivity data was previously developed. However, this model revealed several limitations, leading to less-than-ideal prediction accuracy. Thus, new models demonstrating improved prediction ability must be developed. This study presents the development of machine learning models for the accurate prediction of ionic conductivity in lithium-containing materials based solely on their chemical composition. The models constructed using the NGBoost and LightGBM algorithms show high compatibility with the training and test data, resulting in high predictive accuracy. The constructed models identify “entropy,” which is considered a key factor in developing ionic conductors, as an important feature. This finding highlights the potential utility of this property from a solid-state chemistry perspective. The developed models demonstrate high predictive accuracy even for previously reported lithium superionic conductor-type materials that were not included in the training dataset. The established models are expected to facilitate efficient material discovery for the development of all-solid-state lithium batteries.