Journal
NATURE COMPUTATIONAL SCIENCE
Volume 2, Issue 6, Pages 367-377Publisher
SPRINGERNATURE
DOI: 10.1038/s43588-022-00265-6
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Funding
- Basic Science Center Project of NSFC [51788104]
- National Science Fund for Distinguished Young Scholars [12025405]
- National Natural Science Foundation of China [11874035]
- Ministry of Science and Technology of China [2018YFA0307100, 2018YFA0305603]
- Beijing Advanced Innovation Center for Future Chip (ICFC)
- Beijing Advanced Innovation Center for Materials Genome Engineering
- Shuimu Tsinghua Scholar Program
- Postdoctoral International Exchange Program - China Postdoctoral Science Foundation [2021TQ0187]
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The research team developed a deep learning method DeepH to represent the DFT Hamiltonian of crystalline materials, aiming to bypass the computationally demanding self-consistent field iterations of DFT and significantly improve the efficiency of electronic-structure calculations.
The marriage of density functional theory (DFT) and deep-learning methods has the potential to revolutionize modern computational materials science. Here we develop a deep neural network approach to represent the DFT Hamiltonian (DeepH) of crystalline materials, aiming to bypass the computationally demanding self-consistent field iterations of DFT and substantially improve the efficiency of ab initio electronic-structure calculations. A general framework is proposed to deal with the large dimensionality and gauge (or rotation) covariance of the DFT Hamiltonian matrix by virtue of locality, and this is realized by a message-passing neural network for deep learning. High accuracy, high efficiency and good transferability of the DeepH method are generally demonstrated for various kinds of material system and physical property. The method provides a solution to the accuracy-efficiency dilemma of DFT and opens opportunities to explore large-scale material systems, as evidenced by a promising application in the study of twisted van der Waals materials.
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