Ab initio machine learning simulation of calcium carbonate from aqueous solutions to the solid state
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915202619932672 |
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| author | Piaggi, Pablo M. Gale, Julian D. Raiteri, Paolo |
| author_facet | Piaggi, Pablo M. Gale, Julian D. Raiteri, Paolo |
| contents | 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 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 semi-empirical 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 study of reactive crystallization pathways in calcium carbonate, which are currently poorly understood. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18562 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ab initio machine learning simulation of calcium carbonate from aqueous solutions to the solid state Piaggi, Pablo M. Gale, Julian D. Raiteri, Paolo Chemical Physics Statistical Mechanics 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 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 semi-empirical 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 study of reactive crystallization pathways in calcium carbonate, which are currently poorly understood. |
| title | Ab initio machine learning simulation of calcium carbonate from aqueous solutions to the solid state |
| topic | Chemical Physics Statistical Mechanics |
| url | https://arxiv.org/abs/2409.18562 |