A deep equivariant neural network approach for efficient hybrid density functional calculations

Zechen Tang, He Li, Peize Lin, Xiaoxun Gong, Gan Jin, Lixin He, Hong Jiang, Xinguo Ren (), Wenhui Duan () and Yong Xu ()
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Zechen Tang: Tsinghua University
He Li: Tsinghua University
Peize Lin: Chinese Academy of Sciences
Xiaoxun Gong: Tsinghua University
Gan Jin: University of Science and Technology of China
Lixin He: Hefei Comprehensive National Science Center
Hong Jiang: Peking University
Xinguo Ren: Chinese Academy of Sciences
Wenhui Duan: Tsinghua University
Yong Xu: Tsinghua University

Nature Communications, 2024, vol. 15, issue 1, 1-9

Abstract: Abstract Hybrid density functional calculations are essential for accurate description of electronic structure, yet their widespread use is restricted by the substantial computational cost. Here we develop DeepH-hybrid, a deep equivariant neural network method for learning the hybrid-functional Hamiltonian as a function of material structure, which circumvents the time-consuming self-consistent field iterations and enables the study of large-scale materials with hybrid-functional accuracy. Our extensive experiments demonstrate good reliability as well as effective transferability and efficiency of the method. As a notable application, DeepH-hybrid is applied to study large-supercell Moiré-twisted materials, offering the first case study on how the inclusion of exact exchange affects flat bands in magic-angle twisted bilayer graphene. The work generalizes deep-learning electronic structure methods to beyond conventional density functional theory, facilitating the development of deep-learning-based ab initio methods.

Date: 2024
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DOI: 10.1038/s41467-024-53028-4

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