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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 environments.

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Fig. 1: The xDeepH method for efficient electronic-structure calculations of magnetic materials.


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This is a summary of: Li, H. et al. Deep-learning electronic-structure calculation of magnetic superstructures. Nat. Comput. Sci. (2023).

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A deep-learning method for studying magnetic superstructures. Nat Comput Sci 3, 287–288 (2023).

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