Machine-Learned Bond-Order Potential for Exploring the Configuration Space of Carbon
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866909946591838208 |
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| author | Kohata, Ikuma Hisama, Kaoru Otsuka, Keigo Maruyama, Shigeo |
| author_facet | Kohata, Ikuma Hisama, Kaoru Otsuka, Keigo Maruyama, Shigeo |
| contents | Construction of transferable machine-learning interatomic potentials with a minimal number of parameters is important for their general applicability. Here, we present a machine-learning interatomic potential with the functional form of the bond-order potential for comprehensive exploration over the configuration space of carbon. The physics-based design of this potential enables robust and accurate description over a wide range of the potential energy surface with a small number of parameters. We demonstrate the versatility of this potential through validations across various tasks, including phonon dispersion calculations, global structure searches for clusters, phase diagram calculations, and enthalpy-volume mappings of local minima structures. We expect that this potential can contribute to the discovery of novel carbon materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11297 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine-Learned Bond-Order Potential for Exploring the Configuration Space of Carbon Kohata, Ikuma Hisama, Kaoru Otsuka, Keigo Maruyama, Shigeo Materials Science Construction of transferable machine-learning interatomic potentials with a minimal number of parameters is important for their general applicability. Here, we present a machine-learning interatomic potential with the functional form of the bond-order potential for comprehensive exploration over the configuration space of carbon. The physics-based design of this potential enables robust and accurate description over a wide range of the potential energy surface with a small number of parameters. We demonstrate the versatility of this potential through validations across various tasks, including phonon dispersion calculations, global structure searches for clusters, phase diagram calculations, and enthalpy-volume mappings of local minima structures. We expect that this potential can contribute to the discovery of novel carbon materials. |
| title | Machine-Learned Bond-Order Potential for Exploring the Configuration Space of Carbon |
| topic | Materials Science |
| url | https://arxiv.org/abs/2501.11297 |