Machine Learning Potential for Serpentines
Abstract Serpentines are layered hydrous magnesium silicates (MgO⋅SiO2⋅H2O) formed through serpentinization, a geochemical process that significantly alters the physical property of the mantle. They are hard to investigate experimentally and computationally due to the complexity of natural serpentin...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
| Online Access: | https://doi.org/10.1029/2024JH000434 |
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| author | Hongjin Wang Chenxing Luo Renata M. Wentzcovitch |
| author_facet | Hongjin Wang Chenxing Luo Renata M. Wentzcovitch |
| author_sort | Hongjin Wang |
| collection | DOAJ |
| description | Abstract Serpentines are layered hydrous magnesium silicates (MgO⋅SiO2⋅H2O) formed through serpentinization, a geochemical process that significantly alters the physical property of the mantle. They are hard to investigate experimentally and computationally due to the complexity of natural serpentine samples and the large number of atoms in the unit cell. We developed a machine learning (ML) potential for serpentine minerals based on density functional theory calculation with the r2SCAN meta‐GGA functional for molecular dynamics simulation. We illustrate the success of this ML potential model in reproducing the high‐temperature equation of states of several hydrous phases under the Earth's subduction zone conditions, including brucite, lizardite, and antigorite. In addition, we investigate the polysomes of antigorite with periodicity m = 13–24, which is believed to be all the naturally existent antigorite species. We found that antigorite with m larger than 21 appears more stable than lizardite at low temperatures. This ML potential can be further applied to investigate more complex antigorite superstructures with multiple coexisting periodic waves. |
| format | Article |
| id | doaj-art-377175fe936c41cca8ea2bea921951b8 |
| institution | Kabale University |
| issn | 2993-5210 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-377175fe936c41cca8ea2bea921951b82025-08-20T03:42:25ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102024-12-0114n/an/a10.1029/2024JH000434Machine Learning Potential for SerpentinesHongjin Wang0Chenxing Luo1Renata M. Wentzcovitch2Department of Applied Physics and Applied Mathematics Columbia University New York NY USADepartment of Applied Physics and Applied Mathematics Columbia University New York NY USADepartment of Applied Physics and Applied Mathematics Columbia University New York NY USAAbstract Serpentines are layered hydrous magnesium silicates (MgO⋅SiO2⋅H2O) formed through serpentinization, a geochemical process that significantly alters the physical property of the mantle. They are hard to investigate experimentally and computationally due to the complexity of natural serpentine samples and the large number of atoms in the unit cell. We developed a machine learning (ML) potential for serpentine minerals based on density functional theory calculation with the r2SCAN meta‐GGA functional for molecular dynamics simulation. We illustrate the success of this ML potential model in reproducing the high‐temperature equation of states of several hydrous phases under the Earth's subduction zone conditions, including brucite, lizardite, and antigorite. In addition, we investigate the polysomes of antigorite with periodicity m = 13–24, which is believed to be all the naturally existent antigorite species. We found that antigorite with m larger than 21 appears more stable than lizardite at low temperatures. This ML potential can be further applied to investigate more complex antigorite superstructures with multiple coexisting periodic waves.https://doi.org/10.1029/2024JH000434 |
| spellingShingle | Hongjin Wang Chenxing Luo Renata M. Wentzcovitch Machine Learning Potential for Serpentines Journal of Geophysical Research: Machine Learning and Computation |
| title | Machine Learning Potential for Serpentines |
| title_full | Machine Learning Potential for Serpentines |
| title_fullStr | Machine Learning Potential for Serpentines |
| title_full_unstemmed | Machine Learning Potential for Serpentines |
| title_short | Machine Learning Potential for Serpentines |
| title_sort | machine learning potential for serpentines |
| url | https://doi.org/10.1029/2024JH000434 |
| work_keys_str_mv | AT hongjinwang machinelearningpotentialforserpentines AT chenxingluo machinelearningpotentialforserpentines AT renatamwentzcovitch machinelearningpotentialforserpentines |