Applications and Advances of Machine Learning in the Development of Solid-State Electrolytes for Lithium-Ion Batteries
Tiantian Gao, Yufeng Wu
Abstract
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 selection on the model's predictive performance of SSE properties, and then highlights the use of various ML algorithms, such as predictive models and generative models, in predicting key SSE properties, including ionic conductivity, elastic moduli, and thermodynamic stability. Additionally, we systemically analyze and compare the interpretability and evaluation metrics of the ML models. We hope this review can provide researchers with a comprehensive perspective, promote the deeper integration of ML in SSE development, and facilitate rapid next-generation SSE discovery and design.