Applications and Advances of Machine Learning in the Development of Solid-State Electrolytes for Lithium-Ion Batteries

Solid-state electrolytes (SSEs) have attracted considerable attention for their ability to effectively suppress lithium dendrite growth and enhance the safety and life cycle of lithium-ion batteries (LIBs). However, the commercialization of SSEs has been hindered by low ionic conductivity, limited mechanical strength, and poor interfacial compatibility. Recently, machine learning (ML) has arisen as a helpful tool in SSE studies owing to its efficient data processing and pattern recognition capabilities. This paper reviews recent progress in the application of ML techniques to SSE development for LIBs. It first discusses SSE database creation strategies, then examines the strong influence of descriptor…