Ab initio machine-learning simulation of calcium carbonate from aqueous solutions to the solid state.

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 benc

SUBMITTER: Piaggi PM 

PROVIDER: S-EPMC12541341 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature