General time-reversal equivariant neural network potential for magnetic materials
Fuente:
arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| Soggetti: | |
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| _version_ | 1866929757846765568 |
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| author | Yu, Hongyu Liu, Boyu Zhong, Yang Hong, Liangliang Ji, Junyi Xu, Changsong Gong, Xingao Xiang, Hongjun |
| author_facet | Yu, Hongyu Liu, Boyu Zhong, Yang Hong, Liangliang Ji, Junyi Xu, Changsong Gong, Xingao Xiang, Hongjun |
| contents | This study introduces time-reversal E(3)-equivariant neural network and SpinGNN++ framework for constructing a comprehensive interatomic potential for magnetic systems, encompassing spin-orbit coupling and noncollinear magnetic moments. SpinGNN++ integrates multitask spin equivariant neural network with explicit spin-lattice terms, including Heisenberg, Dzyaloshinskii-Moriya, Kitaev, single-ion anisotropy, and biquadratic interactions, and employs time-reversal equivariant neural network to learn high-order spin-lattice interactions using time-reversal E(3)-equivariant convolutions. To validate SpinGNN++, a complex magnetic model dataset is introduced as a benchmark and employed to demonstrate its capabilities. SpinGNN++ provides accurate descriptions of the complex spin-lattice coupling in monolayer CrI$_3$ and CrTe$_2$, achieving sub-meV errors. Importantly, it facilitates large-scale parallel spin-lattice dynamics, thereby enabling the exploration of associated properties, including the magnetic ground state and phase transition. Remarkably, SpinGNN++ identifies a new ferrimagnetic state as the ground magnetic state for monolayer CrTe2, thereby enriching its phase diagram and providing deeper insights into the distinct magnetic signals observed in various experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_11403 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | General time-reversal equivariant neural network potential for magnetic materials Yu, Hongyu Liu, Boyu Zhong, Yang Hong, Liangliang Ji, Junyi Xu, Changsong Gong, Xingao Xiang, Hongjun Materials Science Machine Learning Computational Physics This study introduces time-reversal E(3)-equivariant neural network and SpinGNN++ framework for constructing a comprehensive interatomic potential for magnetic systems, encompassing spin-orbit coupling and noncollinear magnetic moments. SpinGNN++ integrates multitask spin equivariant neural network with explicit spin-lattice terms, including Heisenberg, Dzyaloshinskii-Moriya, Kitaev, single-ion anisotropy, and biquadratic interactions, and employs time-reversal equivariant neural network to learn high-order spin-lattice interactions using time-reversal E(3)-equivariant convolutions. To validate SpinGNN++, a complex magnetic model dataset is introduced as a benchmark and employed to demonstrate its capabilities. SpinGNN++ provides accurate descriptions of the complex spin-lattice coupling in monolayer CrI$_3$ and CrTe$_2$, achieving sub-meV errors. Importantly, it facilitates large-scale parallel spin-lattice dynamics, thereby enabling the exploration of associated properties, including the magnetic ground state and phase transition. Remarkably, SpinGNN++ identifies a new ferrimagnetic state as the ground magnetic state for monolayer CrTe2, thereby enriching its phase diagram and providing deeper insights into the distinct magnetic signals observed in various experiments. |
| title | General time-reversal equivariant neural network potential for magnetic materials |
| topic | Materials Science Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2211.11403 |