A rotational and time-reversal equivariant neural network designed to represent the spin–orbital density functional theory Hamiltonian as a function of the atomic and magnetic structure enables ab initio electronic-structure calculations of magnetic superstructures. These calculations can efficiently and accurately predict subtle magnetic effects in various chemical environments.
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References
Nagaosa, N. & Tokura, Y. Topological properties and dynamics of magnetic skyrmions. Nat. Nanotechnol. 8, 899–911 (2013). A review article that presents the properties and potential applications of magnetic skyrmions.
Kulik, H. K. et al. Roadmap on machine learning in electronic structure. Electron. Struct. 4, 023004 (2022). A review article that presents key developments in the use of machine learning approaches for electronic-structure calculations and materials science.
Li, H. et al. Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation. Nat. Comput. Sci. 2, 367–377 (2022). An article that introduces a deep-learning approach that is used to efficiently study the electronic structures of non-magnetic materials.
Gong, X. et al. General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian. Preprint at https://arxiv.org/abs/2210.13955 (2022). A preprint article that proposes a general deep-learning framework to represent the DFT Hamiltonian using ENNs.
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This is a summary of: Li, H. et al. Deep-learning electronic-structure calculation of magnetic superstructures. Nat. Comput. Sci. https://doi.org/10.1038/s43588-023-00424-3 (2023).
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A deep-learning method for studying magnetic superstructures. Nat Comput Sci 3, 287–288 (2023). https://doi.org/10.1038/s43588-023-00425-2
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DOI: https://doi.org/10.1038/s43588-023-00425-2
