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A deep-learning method for studying magnetic superstructures
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AI
DOI：10.1038/s43588-023-00425-2.png)
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摘要
En 中文
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.
期刊
IF:
18.3
论文数:
3.1K
被引数:
4.2K
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