Equivariant Neural Network Force Fields for Magnetic Materials
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914670114242560 |
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| author | Yuan, Zilong Xu, Zhiming Li, He Cheng, Xinle Tao, Honggeng Tang, Zechen Zhou, Zhiyuan Duan, Wenhui Xu, Yong |
| author_facet | Yuan, Zilong Xu, Zhiming Li, He Cheng, Xinle Tao, Honggeng Tang, Zechen Zhou, Zhiyuan Duan, Wenhui Xu, Yong |
| contents | Neural network force fields have significantly advanced ab initio atomistic simulations across diverse fields. However, their application in the realm of magnetic materials is still in its early stage due to challenges posed by the subtle magnetic energy landscape and the difficulty of obtaining training data. Here we introduce a data-efficient neural network architecture to represent density functional theory total energy, atomic forces, and magnetic forces as functions of atomic and magnetic structures. Our approach incorporates the principle of equivariance under the three-dimensional Euclidean group into the neural network model. Through systematic experiments on various systems, including monolayer magnets, curved nanotube magnets, and moiré-twisted bilayer magnets of $\text{CrI}_{3}$, we showcase the method's high efficiency and accuracy, as well as exceptional generalization ability. The work creates opportunities for exploring magnetic phenomena in large-scale materials systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04864 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Equivariant Neural Network Force Fields for Magnetic Materials Yuan, Zilong Xu, Zhiming Li, He Cheng, Xinle Tao, Honggeng Tang, Zechen Zhou, Zhiyuan Duan, Wenhui Xu, Yong Materials Science Neural network force fields have significantly advanced ab initio atomistic simulations across diverse fields. However, their application in the realm of magnetic materials is still in its early stage due to challenges posed by the subtle magnetic energy landscape and the difficulty of obtaining training data. Here we introduce a data-efficient neural network architecture to represent density functional theory total energy, atomic forces, and magnetic forces as functions of atomic and magnetic structures. Our approach incorporates the principle of equivariance under the three-dimensional Euclidean group into the neural network model. Through systematic experiments on various systems, including monolayer magnets, curved nanotube magnets, and moiré-twisted bilayer magnets of $\text{CrI}_{3}$, we showcase the method's high efficiency and accuracy, as well as exceptional generalization ability. The work creates opportunities for exploring magnetic phenomena in large-scale materials systems. |
| title | Equivariant Neural Network Force Fields for Magnetic Materials |
| topic | Materials Science |
| url | https://arxiv.org/abs/2402.04864 |