Interpretable Machine Learning for Solid-State Batteries

Solid-state batteries (SSBs) have emerged as promising candidates for next-generation energy storage systems due to their high energy density and enhanced safety. In recent years, machine learning (ML) has become a transformative tool in battery research to accelerate the discovery of new materials and predict cycle life. However, the widespread application of ML is hindered by the "black-box" nature of many models, which limits their interpretability and scientific credibility. We propose a structured framework for using ML in SSB research by encompassing five components: (i) solid electrolyte design, (ii) material characterization, (iii) electrode/electrolyte interface optimization, (iv) battery lifetime prediction, and (v) dendrite inhibition. For each component, we identify its specific requirements and recommend appropriate approaches to develop interpretable ML. Finally, we summarize current challenges and propose corresponding suggestions as well as open-source toolchains aimed at transitioning from "black-box" predictions to mechanism-driven design, which will accelerate the development of high-performance SSBs for energy storage.