ArticleProceedings of the National Academy of Sciences of the United States of America2025
Ab initio machine-learning simulation of calcium carbonate from aqueous solutions to the solid state.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Data-Efficient and Fast Machine Learning Molecular Dynamics through Integrated Active Learning and Knowledge Distillation.Journal of chemical theory and computation · 2026Article
- Transfer Learning Meets Embedded Correlated Wavefunction Theory for Chemically Accurate Molecular Simulations: Application to Calcium Carbonate Ion Pairing.Journal of chemical theory and computation · 2026Article
- Committors without Descriptors.Journal of chemical theory and computation · 2026Article
- Machine learning in chemistry.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Ab initio machine-learning simulation of calcium carbonate from aqueous solutions to the solid state.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
A first-principles machine-learning model has been developed aimed at studying the formation of calcium carbonate from aqueous solution using molecular dynamics simulations. The model, dubbed strongly constrained and appropriately normed-machine learning (SCAN-ML), reproduces accurately the potential energy surface derived from ab initio density-functional theory within the SCAN approximation for the exchange and correlation functional. A broad range of properties have been calculated relevant to ions in solution, solid phases, and the calcite/water interface. Careful comparison with results from experiments and semiempirical force fields shows that SCAN-ML provides an excellent description of this system, surpassing state-of-the-art force fields for many properties, while providing a benchmark for many quantities that are currently beyond the reach of direct ab initio molecular dynamics. A key feature of SCAN-ML is its ability to capture chemical reactions, which reveals that calcium carbonate ion pair formation occurs predominantly via binding of calcium to bicarbonate, with the subsequent loss of a proton to water, rather than by direct association. Our model thus paves the way for the ab initio study of reactive crystallization pathways in biominerals, which are currently poorly understood.
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