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Machine learning potentials (MLPs) offer a route to extend quantum accuracy to large\u2010scale simulations, yet progress has been limited by the absence of representative datasets. Here, we present a stoichiometry\u2010generalizable MLP, CSH\u2010MLP, trained on a broad and representative dataset containing over 50\u00a0000 DFT\u2010labeled configurations, which is derived from nearly 700 distinct C\u2013S\u2013H compositions spanning diverse compositions, defects, and hydration states. CSH\u2010MLP reproduces structural, dynamic, and mechanical properties with near\u2010DFT accuracy, resolves long\u2010standing discrepancies with high\u2010pressure X\u2010ray diffraction, and transfers reliably to large, disordered, and nanoporous models beyond its training set. Achieving substantially higher computational efficiency than ReaxFF, CSH\u2010MLP enables simulations at scales and timescales previously inaccessible to quantum\u2010mechanical methods. This work establishes a foundation for multiscale cement modeling, data\u2010driven \u201ccement genome\u201d construction, and design of next\u2010generation sustainable construction materials.<\/jats:p>","DOI":"10.1002\/advs.77942","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T15:49:02Z","timestamp":1790610542000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Potential for Calcium Silicate Hydrates With Broad Compositional and Structural Diversity"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0333-9734","authenticated-orcid":false,"given":"Yunjian","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Climate Resilience for Coastal Cities, Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9771-7130","authenticated-orcid":false,"given":"Cheng","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Climate Resilience for Coastal Cities, Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roland J.\u2010M.","family":"Pellenq","sequence":"additional","affiliation":[{"name":"Institut Europ\u00e9en des Membranes CNRS Universit\u00e9 De Montpellier  Montpellier France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8477-6863","authenticated-orcid":false,"given":"Zongjin","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Innovation Engineering Macau University of Science and Technology  Taipa Macao People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaping","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Materials Science and Engineering Southeast University  Nanjing People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,28]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781119806219"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2016.04.145"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586\u2010024\u201007338\u20108"},{"key":"e_1_2_9_5_1","volume-title":"Concrete: Microstructure, Properties, and Materials","author":"Mehta P. 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