Unraveling the Complexity of Divalent Hydride Electrolytes in Solid-State Batteries via a Data-Driven Framework with Large Language Model

Solid-state electrolytes (SSEs) are essential for next-generation energy storage technologies. However, the exploration of divalent hydrides is hindered by complex ionic migration mechanisms and reliance on “trial-and-error” methodologies. Conventional approaches, which focus on individual materials and predefined pathways, remain inefficient. Herein, we present a data-driven artificial intelligence framework that integrates a comprehensive SSE database with large language models and ab initio metadynamics (MetaD) simulations to accelerate the discovery of hydride SSEs. Our study reveals that hydrides incorporating neutral molecules have great potential, with MetaD revealing novel “two-step” ion migration mechanisms. Predictive models developed using both experimental and computational data accurately forecast ionic migration activation energies for various types of hydride SSEs. In particular, some SSEs with carbon-containing neutral molecules exhibit notably low activation energy, with barriers as low as 0.62 eV. This framework enables the rapid identification of optimized SSE candidates and establishes a transformative tool for advancing sustainable energy storage technologies.