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However, the performance of such energy devices is impeded by the relatively low conductivity of AEMs. The conventional trial\u2010and\u2010error approach to designing membrane structures has proven to be both inefficient and costly. To address this challenge, a fully connected neural network (FCNN) model is developed based on acid\u2010catalyzed AEMs to analyze the relationship between structure and conductivity among 180,000 AEM variations. Under machine learning guidance, anilinium cation\u2010type membranes are designed and synthesized. Molecular dynamics simulations and Mulliken charge population analysis validated that the presence of a large anilinium cation domain is a result of the inductive effect of N\n                    <jats:sup>+<\/jats:sup>\n                    and benzene rings. The interconnected anilinium cation domains facilitated the formation of a continuous ion transport channel within the AEMs. Additionally, the incorporation of the benzyl electron\u2010withdrawing group heightened the inductive effect, leading to high conductivity AEM variant as screened by the machine learning model. Furthermore, based on the highly active and low\u2010cost monomers given by machine learning, the large\u2010scale synthesis of anilinium\u2010based AEMs confirms the potential for commercial applications.\n                  <\/jats:p>","DOI":"10.1002\/adma.202404981","type":"journal-article","created":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T01:40:51Z","timestamp":1722303651000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Machine Learning\u2010Aided Design of Highly Conductive Anion Exchange Membranes for Fuel Cells and Water Electrolyzers"],"prefix":"10.1002","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-1500-4536","authenticated-orcid":false,"given":"Qiuhuan","family":"Zhang","sequence":"first","affiliation":[{"name":"Jiangsu Engineering Laboratory of Novel Functional Polymeric Materials Jiangsu Key Laboratory of Advanced Negative Carbon Technologies Suzhou Key Laboratory of 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