تقرير
Equivariant Neural Network Force Fields for Magnetic Materials
| العنوان: | Equivariant Neural Network Force Fields for Magnetic Materials |
|---|---|
| المؤلفون: | Yuan, Zilong, Xu, Zhiming, Li, He, Cheng, Xinle, Tao, Honggeng, Tang, Zechen, Zhou, Zhiyuan, Duan, Wenhui, Xu, Yong |
| سنة النشر: | 2024 |
| المجموعة: | Condensed Matter |
| مصطلحات موضوعية: | Condensed Matter - Materials Science |
| الوصف: | 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 moir\'e-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. Comment: 10 pages, 4 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2402.04864 |
| رقم الأكسشن: | edsarx.2402.04864 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |