Polynomial machine learning potential and its application to global structure search in the ternary Cu-Ag-Au alloy
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
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| _version_ | 1866916339310919680 |
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| author | Seko, Atsuto |
| author_facet | Seko, Atsuto |
| contents | Machine learning potentials (MLPs) have become indispensable for performing accurate large-scale atomistic simulations and predicting crystal structures. This study introduces the development of a polynomial MLP specifically for the ternary Cu-Ag-Au system. The MLP is formulated as a polynomial of polynomial invariants that remain unchanged under any rotation. The polynomial MLP facilitates not only comprehensive global structure searches within the Cu-Ag-Au alloy system but also reliable predictions of a wide variety of properties across the entire composition range. The developed MLP supports highly accurate and efficient atomistic simulations, thereby significantly advancing the understanding of the Cu-Ag-Au system. Furthermore, the methodology demonstrated in this study can be easily applied to other ternary alloy systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20630 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Polynomial machine learning potential and its application to global structure search in the ternary Cu-Ag-Au alloy Seko, Atsuto Materials Science Machine learning potentials (MLPs) have become indispensable for performing accurate large-scale atomistic simulations and predicting crystal structures. This study introduces the development of a polynomial MLP specifically for the ternary Cu-Ag-Au system. The MLP is formulated as a polynomial of polynomial invariants that remain unchanged under any rotation. The polynomial MLP facilitates not only comprehensive global structure searches within the Cu-Ag-Au alloy system but also reliable predictions of a wide variety of properties across the entire composition range. The developed MLP supports highly accurate and efficient atomistic simulations, thereby significantly advancing the understanding of the Cu-Ag-Au system. Furthermore, the methodology demonstrated in this study can be easily applied to other ternary alloy systems. |
| title | Polynomial machine learning potential and its application to global structure search in the ternary Cu-Ag-Au alloy |
| topic | Materials Science |
| url | https://arxiv.org/abs/2407.20630 |