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Here we introduce USMNet, a unified structure\u2010aware multimodal neural network that combines crystal\u2010structure representations, compositional features, and physically motivated descriptors to predict lithium\u2010ion conductivity. We benchmark the model against composition\u2010only and structure\u2010based baselines, showing that explicit structural information improves prediction accuracy and enables data\u2010efficient and reliable exploration of candidate materials. We then apply USMNet to 168\u00a0675 candidate materials spanning 19 lithium solid\u2010state electrolyte frameworks, identifying 21 high\u2010potential inorganic electrolytes. Selected candidates are synthesized and experimentally validated, with Li\n                    <jats:sub>16<\/jats:sub>\n                    ZnSiP\n                    <jats:sub>2<\/jats:sub>\n                    S\n                    <jats:sub>16<\/jats:sub>\n                    enabling all\u2010solid\u2010state batteries that retain 80% capacity after 600 cycles under 0.5 C at room temperature. These results establish a generalizable structure\u2010aware learning strategy for electrochemical property prediction and demonstrate its utility in accelerating closed\u2010loop materials discovery under limited data regimes.\n                  <\/jats:p>","DOI":"10.1002\/smll.75978","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T13:04:42Z","timestamp":1791464682000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic Generation of Inorganic Solid\u2010State Electrolytes via Unified Multi\u2010Modal Network"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3630-5359","authenticated-orcid":false,"given":"Yihong","family":"Liu","sequence":"first","affiliation":[{"name":"Shanghai Key Laboratory For R&amp;D and Application of Metallic Functional Materials Institute of New Energy for Vehicles School of Materials Science and Engineering Tongji 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