Equivariant neural network force fields for magnetic materials

Autor: Zilong Yuan, Zhiming Xu, He Li, Xinle Cheng, Honggeng Tao, Zechen Tang, Zhiyuan Zhou, Wenhui Duan, Yong Xu
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Quantum Frontiers, Vol 3, Iss 1, Pp 1-10 (2024)
Druh dokumentu: article
ISSN: 2731-6106
DOI: 10.1007/s44214-024-00055-3
Popis: Abstract 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 CrI3, 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.
Databáze: Directory of Open Access Journals
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