Machine Learning Potential for Calcium Silicate Hydrates With Broad Compositional and Structural Diversity

ABSTRACT Calcium silicate hydrate (C–S–H), the primary hydration product of cement, governs the strength and durability of concrete but remains difficult to model due to its chemical heterogeneity and disordered nanostructure. Machine learning potentials (MLPs) offer a route to extend quantum accuracy to large‐scale simulations, yet progress has been limited by the absence of representative datasets. Here, we present a stoichiometry‐generalizable MLP, CSH‐MLP, trained on a broad and representative dataset containing over 50 000 DFT‐labeled configurations, which is derived from nearly 700 distinct C–S–H compositions spanning diverse compositions, defects, and hydration states. CSH‐MLP reproduces structural, dynamic, and mechanical properties with near‐DFT accuracy, resolves long‐standing discrepancies with high‐pressure X‐ray diffraction, and transfers reliably to large, disordered, and nanoporous models beyond its training set. Achieving substantially higher computational efficiency than ReaxFF, CSH‐MLP enables simulations at scales and timescales previously inaccessible to quantum‐mechanical methods. This work establishes a foundation for multiscale cement modeling, data‐driven “cement genome” construction, and design of next‐generation sustainable construction materials.