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While machine learning (ML) techniques offer a promising solution, their application is hindered by the lack of large, unified databases. Herein, constant\u2010potential molecular simulation is used to construct a unified supercapacitor database with hundreds of metal\u2013organic framework (MOF) electrodes. Leveraging this database, well\u2010trained decision\u2010tree\u2010based ML models achieve fast, accurate, and interpretable predictions of capacitance and charging rate, experimentally validated by a representative case. SHAP analyses reveal that specific surface area (SSA) governs gravimetric capacitance while pore size effects are minimal, attributed to the strong dependence of electrode\u2010ion coordination on SSA rather than pore size. SSA and porosity, respectively, dominate volumetric capacitance in 1D\u2010pore and 3D\u2010pore MOFs, pinnacling the indispensable effects of pore dimensionality. Meanwhile, porosity is found to be the most decisive factor in the charging rate for both 1D\u2010pore and 3D\u2010pore MOFs. Especially for 3D\u2010pore MOFs, an exponential increase in porosity is observed in both ionic conductance and in\u2010pore ion diffusion coefficient, ascribed to loosened ion packing. These findings provide profound insights for the design of high\u2010performance supercapacitor electrodes.<\/jats:p>","DOI":"10.1002\/adma.202500943","type":"journal-article","created":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T11:45:33Z","timestamp":1741693533000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Machine Learning Relationships Between Nanoporous Structures and Electrochemical Performance in MOF Supercapacitors"],"prefix":"10.1002","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9463-3885","authenticated-orcid":false,"given":"Zhenxiang","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Coal Combustion School of Energy and Power Engineering Huazhong University of Science and Technology  Wuhan 430074 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