Tang Zechen, Li He, Lin Peize, Gong Xiaoxun, Jin Gan, He Lixin, Jiang Hong, Ren Xinguo, Duan Wenhui, Xu Yong
State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University, 100084, Beijing, China.
Institute for Advanced Study, Tsinghua University, 100084, Beijing, China.
Nat Commun. 2024 Oct 11;15(1):8815. doi: 10.1038/s41467-024-53028-4.
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.
杂化密度泛函计算对于准确描述电子结构至关重要，然而其广泛应用受到巨大计算成本的限制。在此，我们开发了DeepH-hybrid，这是一种深度等变神经网络方法，用于学习作为材料结构函数的杂化泛函哈密顿量，它规避了耗时的自洽场迭代，并能够以杂化泛函精度研究大规模材料。我们广泛的实验证明了该方法具有良好的可靠性以及有效的可转移性和效率。作为一个显著的应用，DeepH-hybrid被应用于研究大超胞莫尔扭曲材料，提供了关于包含精确交换如何影响魔角扭曲双层石墨烯中平带的首个案例研究。这项工作将深度学习电子结构方法推广到超越传统密度泛函理论的范畴，促进了基于深度学习的从头算方法的发展。
