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Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation

A preprint version of the article is available at arXiv.

Abstract

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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Fig. 1: Learning the DFT Hamiltonian \({\hat{H}}_{{{{\rm{DFT}}}}}\) by virtue of locality.
Fig. 2: Crystal graph and MPNN including L layers employed by DeepH.
Fig. 3: Performance of DeepH on studying graphene.
Fig. 4: Performance of DeepH on studying monolayer MoS2.
Fig. 5: Generalization ability of DeepH, from flat sheets to curved nanotubes.
Fig. 6: Application of the DeepH method to study moiré-twisted materials.

Data availability

Source data are provided with this Paper. The dataset used to train the deep-learning model is available at Zenodo61.

Code availability

The code used in the current study is available at GitHub (https://github.com/mzjb/DeepH-pack) and Zenodo62.

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Acknowledgements

This work was supported by the Basic Science Center Project of NSFC (grant no. 51788104), the National Science Fund for Distinguished Young Scholars (grant no. 12025405), the National Natural Science Foundation of China (grant no. 11874035), the Ministry of Science and Technology of China (grant nos. 2018YFA0307100 and 2018YFA0305603), the Beijing Advanced Innovation Center for Future Chip (ICFC) and the Beijing Advanced Innovation Center for Materials Genome Engineering. M.Y. was supported by the Shuimu Tsinghua Scholar Program and Postdoctoral International Exchange Program. R.X. was funded by the China Postdoctoral Science Foundation (grant no. 2021TQ0187).

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Y.X. and W.D. proposed the project and supervised H.L., Z.W. and N.Z. in carrying out the research, with the help of M.Y., R.X. and X.G. All authors discussed the results. Y.X. and H.L. prepared the manuscript with input from the other co-authors.

Corresponding authors

Correspondence to Wenhui Duan or Yong Xu.

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Nature Computational Science thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Kaitlin McCardle, in collaboration with the Nature Computational Science team. Peer reviewer reports are available.

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Supplementary Information

Details of computational methods and results, Supplementary Figs. 1–21 and Tables 1–5.

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Supplementary Data 1

Atomic structures of the three distorted graphene supercells in crystallographic information file (CIF) format.

Supplementary Data 2

Atomic structures of the three distorted MoS2 supercells in CIF format.

Supplementary Data 3

The atomic structure of the distorted silicon supercell in CIF format.

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Li, H., Wang, Z., Zou, N. et al. Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation. Nat Comput Sci 2, 367–377 (2022). https://doi.org/10.1038/s43588-022-00265-6

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