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Despite advancing scientific discovery by uncovering complex patterns, machine learning remains challenging for electrolyte design, owing to intricate additive formulations, solvation interactions, and coupled performance metrics, demanding chemically interpretable workflows. Herein, a multilevel artificial intelligent (AI) framework is developed for accelerated electrolyte design for ABs. The multi\u2010task neural network is first applied to establish the elemental features of aqueous electrolytes with wide ESW, followed by a classification\u2010regression model to identify additives with strong HER inhibition effect. Unsupervised learning combined with molecular dynamics simulations further provides a chemical explanation. Particularly, the elaborated additives with large polar topological structures reduce HER activity and expand ESW by enhancing the water confinement effect. As a proof of concept, experimental analyses further validate the long lifespan, evidenced by symmetric\u2010cell cycling beyond 1100\u00a0h and more than 2500 cycles in Zn||VO\n                    <jats:sub>2<\/jats:sub>\n                    full cells, along with the reliable operation of a 1.66\u00a0Ah punch\u2010type device. This multilevel AI framework integrated with experimental validations should accelerate and rationalize the development of ABs.\n                  <\/jats:p>","DOI":"10.1002\/anie.5593105","type":"journal-article","created":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T03:18:34Z","timestamp":1775445514000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Multilevel Artificial Intelligent Framework Accelerates Electrolytes Design for Aqueous Batteries"],"prefix":"10.1002","volume":"65","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0124-036X","authenticated-orcid":false,"given":"Gaoyang","family":"Li","sequence":"first","affiliation":[{"name":"Laboratory of Advanced Materials Aqueous Battery Center Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials Collaborative Innovation Center of Chemistry for Energy Materials Shanghai Wusong Laboratory of Materials Science State Key Laboratory of Porous Materials for Separation and Conversion College of Smart Materials and Future Energy, Fudan University Shanghai P. 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China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shixiang","family":"Ding","sequence":"additional","affiliation":[{"name":"Laboratory of Advanced Materials Aqueous Battery Center Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials Collaborative Innovation Center of Chemistry for Energy Materials Shanghai Wusong Laboratory of Materials Science State Key Laboratory of Porous Materials for Separation and Conversion College of Smart Materials and Future Energy, Fudan University Shanghai P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanli","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering China Agricultural University  Beijing P. R. 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