Machine learning drives a new paradigm in inorganic solid-state electrolytes research

The development of next-generation solid-state batteries (SSBs) relies heavily on the discovery of high-performance inorganic solid-state electrolytes (SSEs). However, conventional experimental and computational methods are often time-consuming and inefficient in exploring the vast compositional and structural space. Machine learning (ML) has emerged as a powerful and transformative tool, driving a paradigm shift in SSE research. This review systematically summarizes recent advances in ML applications for inorganic SSEs, focusing on its role in predicting and optimizing key properties such as thermodynamic stability, electronic transport, electrochemical stability windows, ionic conductivity, migration barriers, phonon dynamics, mechanical strength, and interfacial stability. By enabling high-throughput screening, establishing structure-property relationships, and guiding inverse design, ML significantly accelerates the discovery and rational design of promising SSE candidates. Perspectives on the challenges such as data quality, model interpretability, and multi-property trade-offs are supplied. Finally, we recommend that future efforts prioritize the development of standardized databases, interpretable and physics-informed ML models, and closed-loop frameworks integrating computation, synthesis, and validation. This review will guide future directions to advance SSE technologies toward practical application in high-energy, safe solid-state batteries.