Predictive power of polynomial machine learning potentials for liquid states in 22 elemental systems
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866908634423754752 |
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| author | Wakai, Hayato Seko, Atsuto Izuta, Hirosato Nishiyama, Takayuki Tanaka, Isao |
| author_facet | Wakai, Hayato Seko, Atsuto Izuta, Hirosato Nishiyama, Takayuki Tanaka, Isao |
| contents | The polynomial machine learning potentials (MLPs) described by polynomial rotational invariants have been systematically developed for various systems and used in diverse applications in crystalline states. In this study, we systematically investigate the predictive power of the polynomial MLPs for liquid structural properties in 22 elemental systems with diverse chemical bonding properties, including those showing anomalous melting behavior, such as Si, Ge, and Bi. We compare liquid structural properties obtained from molecular dynamics simulations using the density functional theory (DFT) calculation, the polynomial MLPs, and other interatomic potentials in the literature. The current results demonstrate that the polynomial MLPs consistently exhibit high predictive power for liquid structural properties with the same accuracy as that of typical DFT calculations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14877 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predictive power of polynomial machine learning potentials for liquid states in 22 elemental systems Wakai, Hayato Seko, Atsuto Izuta, Hirosato Nishiyama, Takayuki Tanaka, Isao Materials Science The polynomial machine learning potentials (MLPs) described by polynomial rotational invariants have been systematically developed for various systems and used in diverse applications in crystalline states. In this study, we systematically investigate the predictive power of the polynomial MLPs for liquid structural properties in 22 elemental systems with diverse chemical bonding properties, including those showing anomalous melting behavior, such as Si, Ge, and Bi. We compare liquid structural properties obtained from molecular dynamics simulations using the density functional theory (DFT) calculation, the polynomial MLPs, and other interatomic potentials in the literature. The current results demonstrate that the polynomial MLPs consistently exhibit high predictive power for liquid structural properties with the same accuracy as that of typical DFT calculations. |
| title | Predictive power of polynomial machine learning potentials for liquid states in 22 elemental systems |
| topic | Materials Science |
| url | https://arxiv.org/abs/2401.14877 |