Automatic Generation of Inorganic Solid‐State Electrolytes via Unified Multi‐Modal Network

ABSTRACT Accurate prediction of ion transport properties remains a key bottleneck in computational discovery of solid‐state electrolytes, particularly because experimentally measured ionic conductivities are sparse and strongly dependent on crystal structure. Here we introduce USMNet, a unified structure‐aware multimodal neural network that combines crystal‐structure representations, compositional features, and physically motivated descriptors to predict lithium‐ion conductivity. We benchmark the model against composition‐only and structure‐based baselines, showing that explicit structural information improves prediction accuracy and enables data‐efficient and reliable exploration of candidate materials. We then apply USMNet to 168 675 candidate materials spanning 19 lithium solid‐state electrolyte frameworks, identifying 21 high‐potential inorganic electrolytes. Selected candidates are synthesized and experimentally validated, with Li 16 ZnSiP 2 S 16 enabling all‐solid‐state batteries that retain 80% capacity after 600 cycles under 0.5 C at room temperature. These results establish a generalizable structure‐aware learning strategy for electrochemical property prediction and demonstrate its utility in accelerating closed‐loop materials discovery under limited data regimes.