IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-51006-4.html
   My bibliography  Save this article

Deep learning tight-binding approach for large-scale electronic simulations at finite temperatures with ab initio accuracy

Author

Listed:
  • Qiangqiang Gu

    (AI for Science Institute
    Peking University)

  • Zhanghao Zhouyin

    (AI for Science Institute
    Tianjin University)

  • Shishir Kumar Pandey

    (AI for Science Institute
    Pilani-Dubai Campus)

  • Peng Zhang

    (Tianjin University)

  • Linfeng Zhang

    (AI for Science Institute
    DP Technology)

  • Weinan E

    (AI for Science Institute
    Peking University
    Peking University)

Abstract

Simulating electronic behavior in materials and devices with realistic large system sizes remains a formidable task within the ab initio framework due to its computational intensity. Here we show DeePTB, an efficient deep learning-based tight-binding approach with ab initio accuracy to address this issue. By training on structural data and corresponding ab initio eigenvalues, the DeePTB model can efficiently predict tight-binding Hamiltonians for unseen structures, enabling efficient simulations of large-size systems under external perturbations such as finite temperatures and strain. This capability is vital for semiconductor band gap engineering and materials design. When combined with molecular dynamics, DeePTB facilitates efficient and accurate finite-temperature simulations of both atomic and electronic behavior simultaneously. This is demonstrated by computing the temperature-dependent electronic properties of a gallium phosphide system with 106 atoms. The availability of DeePTB bridges the gap between accuracy and scalability in electronic simulations, potentially advancing materials science and related fields by enabling large-scale electronic structure calculations.

Suggested Citation

  • Qiangqiang Gu & Zhanghao Zhouyin & Shishir Kumar Pandey & Peng Zhang & Linfeng Zhang & Weinan E, 2024. "Deep learning tight-binding approach for large-scale electronic simulations at finite temperatures with ab initio accuracy," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51006-4
    DOI: 10.1038/s41467-024-51006-4
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-51006-4
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-51006-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. K. T. Schütt & M. Gastegger & A. Tkatchenko & K.-R. Müller & R. J. Maurer, 2019. "Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Laura Lewis & Hsin-Yuan Huang & Viet T. Tran & Sebastian Lehner & Richard Kueng & John Preskill, 2024. "Improved machine learning algorithm for predicting ground state properties," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    2. Amin Alibakhshi & Bernd Hartke, 2022. "Implicitly perturbed Hamiltonian as a class of versatile and general-purpose molecular representations for machine learning," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. Yuanming Bai & Leslie Vogt-Maranto & Mark E. Tuckerman & William J. Glover, 2022. "Machine learning the Hohenberg-Kohn map for molecular excited states," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    4. Xiaoxun Gong & He Li & Nianlong Zou & Runzhang Xu & Wenhui Duan & Yong Xu, 2023. "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    5. Chao Liang & Yilimiranmu Rouzhahong & Caiyuan Ye & Chong Li & Biao Wang & Huashan Li, 2023. "Material symmetry recognition and property prediction accomplished by crystal capsule representation," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    6. Zechen Tang & He Li & Peize Lin & Xiaoxun Gong & Gan Jin & Lixin He & Hong Jiang & Xinguo Ren & Wenhui Duan & Yong Xu, 2024. "A deep equivariant neural network approach for efficient hybrid density functional calculations," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    7. Yi Yang & Xinwei Li & Huamin Li & Dongyin Li & Ruifu Yuan, 2020. "Deep Q-Network for Optimal Decision for Top-Coal Caving," Energies, MDPI, vol. 13(7), pages 1-14, April.
    8. Oliver T. Unke & Stefan Chmiela & Michael Gastegger & Kristof T. Schütt & Huziel E. Sauceda & Klaus-Robert Müller, 2021. "SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    9. Michael Scherbela & Leon Gerard & Philipp Grohs, 2024. "Towards a transferable fermionic neural wavefunction for molecules," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51006-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.