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Data-Driven Atomistic Modeling of Crystalline and Glassy Solid-State Electrolytes
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DOI：10.1039/D5CC04921K.png)
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Abstract
En 中文
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.
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