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Here, an interpretable machine\u2010learning framework is developed that extracts chemically meaningful structure\u2013performance relationships from literature data and uses them to guide molecular screening. Using approximately 100 data points, the model quantitatively relates six molecular and material features to photovoltaic performance, while SHapley Additive exPlanations (SHAP) analysis ranks their relative contributions and identifies hydrogen\u2010bonding capacity and adsorption strength as the two most influential factors. Guided by these data\u2010derived relationships, nine previously unexplored molecules were screened, with 2\u2010amino\u2010thiazole\u20104\u2010carboxamide (ATC) and 1\u2010(2\u2010aminoethyl)\u20101H\u2010pyrrole\u20102,5\u2010dione (MPD) emerging as leading candidates. Theoretical and experimental investigations show that MPD exhibits stronger Pb\n                    <jats:sup>2<\/jats:sup>\n                    <jats:sup>+<\/jats:sup>\n                    coordination, whereas ATC forms stronger geometry\u2010matched multisite N\u2500H\u00b7\u00b7\u00b7I interactions, increasing the calculated iodide\u2010migration barrier from 0.39 to 0.67\u00a0eV and suppressing interfacial iodide redistribution. ATC\u2010modified cells achieve a PCE of 27.13% and a certified stabilized efficiency of 26.59%, while retaining 90% of their initial efficiency after 1460\u00a0h of unencapsulated operation. The strategy further delivers efficiencies of 24.01% and 21.14% in 20.98 cm\n                    <jats:sup>2<\/jats:sup>\n                    rigid and 21.0 cm\n                    <jats:sup>2<\/jats:sup>\n                    flexible modules, respectively, providing a data\u2010to\u2010mechanism route for rational and scalable perovskite interface engineering.\n                  <\/jats:p>","DOI":"10.1002\/anie.9718249","type":"journal-article","created":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T03:49:01Z","timestamp":1792554541000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Decoding Perovskite Interfaces: Machine Learning Uncovers Geometry\u2010Matched Dual\u2010Site Hydrogen Bonding"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7881-102X","authenticated-orcid":false,"given":"Can","family":"Cheng","sequence":"first","affiliation":[{"name":"State Key Laboratory of Chemistry for NBC Hazards Protection College of Chemistry Fuzhou University  Fuzhou P. R. China"},{"name":"College of Physics and Information Engineering Institute of Micro\u2010Nano Devices and Solar Cells Fuzhou University  Fuzhou P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Gu","sequence":"additional","affiliation":[{"name":"Center of Future Photovoltaics Research Global Institute of Future Technology (GIFT) Shanghai Jiao Tong University  Shanghai P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute for Advanced Materials &amp; Guangdong Provincial Key Laboratory of Optical Information Materials and Technology South China Academy of Advanced Optoelectronics South China Normal University  Guangzhou P. R. 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