A deep-learning method for studying magnetic superstructures

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

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DEEP-LEARNING FRAMEWORK TO REPRESENT THE DFT HAMILTONIAN USING ENNS. Download references ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional

claims in published maps and institutional affiliations. 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). RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE A deep-learning method for studying magnetic

superstructures. _Nat Comput Sci_ 3, 287–288 (2023). https://doi.org/10.1038/s43588-023-00425-2 Download citation * Published: 26 April 2023 * Issue Date: April 2023 * DOI:

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