A deep-learning method for studying magnetic superstructures.

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Abstract

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References

  1. 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. [DOI: 10.1038/nnano.2013.243]
  2. 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. [DOI: 10.1088/2516-1075/ac572f]
  3. 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. [DOI: 10.1038/s43588-022-00265-6]
  4. 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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