Data-driven atomistic modeling of crystalline and glassy solid-state electrolytes

Abstract

All-solid-state batteries promise safer, more stable, and higher-energy-density storage, but progress hinges on atomistic insight into solid electrolytes. Machine-learning force fields (ML-FFs) offer near-first-principles accuracy at molecular-dynamics scales, enabling simulations that are orders of magnitude larger and longer than ab initio approaches (e.g. density funcitonal theory). In this Review, we discuss recent ML-FF frameworks and the application of them on studying both crystalline and glassy solid electrolytes. Particually, we compare various ML-FF models and training strategies, examine transferability and uncertainty quantification, and outline best practices for data generation and validation. The applications of ML-FF on battery systems reveal advances in illustrating ionic-transport pathways, defect-mediated conduction, structure–property relationships, phase stability and transformations, and interfacial phenomena at grain boundaries and electrode|electrolyte contacts. Then we conclude with perspectives on key challenges—including long-range electrostatics, chemical reactivity, and multi-component complexity. Together, these developments position ML-FFs to accelerate the discovery and optimization of robust, high-performance solid electrolytes for practical all-solid-state batteries.