LATTE: an atomic environment descriptor based on Cartesian tensor contractions
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911875932880896 |
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| author | Pellegrini, Franco de Gironcoli, Stefano Küçükbenli, Emine |
| author_facet | Pellegrini, Franco de Gironcoli, Stefano Küçükbenli, Emine |
| contents | We propose a new descriptor for local atomic environments, to be used in combination with machine learning models for the construction of interatomic potentials. The Local Atomic Tensors Trainable Expansion (LATTE) allows for the efficient construction of a variable number of many-body terms with learnable parameters, resulting in a descriptor that is efficient, expressive, and can be scaled to suit different accuracy and computational cost requirements. We compare this new descriptor to existing ones on several systems, showing it to be competitive with very fast potentials at one end of the spectrum, and extensible to an accuracy close to the state of the art. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_08137 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LATTE: an atomic environment descriptor based on Cartesian tensor contractions Pellegrini, Franco de Gironcoli, Stefano Küçükbenli, Emine Computational Physics Materials Science Machine Learning Chemical Physics We propose a new descriptor for local atomic environments, to be used in combination with machine learning models for the construction of interatomic potentials. The Local Atomic Tensors Trainable Expansion (LATTE) allows for the efficient construction of a variable number of many-body terms with learnable parameters, resulting in a descriptor that is efficient, expressive, and can be scaled to suit different accuracy and computational cost requirements. We compare this new descriptor to existing ones on several systems, showing it to be competitive with very fast potentials at one end of the spectrum, and extensible to an accuracy close to the state of the art. |
| title | LATTE: an atomic environment descriptor based on Cartesian tensor contractions |
| topic | Computational Physics Materials Science Machine Learning Chemical Physics |
| url | https://arxiv.org/abs/2405.08137 |