IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v16y2025i1d10.1038_s41467-025-57328-1.html
   My bibliography  Save this article

Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltonians

Author

Listed:
  • Changwei Zhang

    (Fudan University)

  • Yang Zhong

    (Fudan University)

  • Zhi-Guo Tao

    (Fudan University)

  • Xinming Qin

    (University of Science and Technology of China)

  • Honghui Shang

    (University of Science and Technology of China)

  • Zhenggang Lan

    (South China Normal University)

  • Oleg V. Prezhdo

    (University of Southern California)

  • Xin-Gao Gong

    (Fudan University)

  • Weibin Chu

    (Fudan University)

  • Hongjun Xiang

    (Fudan University)

Abstract

Non-adiabatic molecular dynamics (NAMD) simulations have become an indispensable tool for investigating excited-state dynamics in solids. In this work, we propose a general framework, N2AMD (Neural-Network Non-Adiabatic Molecular Dynamics), which employs an E(3)-equivariant deep neural Hamiltonian to boost the accuracy and efficiency of NAMD simulations. Distinct from conventional machine learning methods that predict key quantities in NAMD, N2AMD computes these quantities directly with a deep neural Hamiltonian, ensuring excellent accuracy, efficiency, and consistency. N2AMD not only achieves impressive efficiency in performing NAMD simulations at the hybrid functional level within the framework of the classical path approximation (CPA), but also demonstrates great potential in predicting non-adiabatic coupling vectors and suggests a method to go beyond CPA. Furthermore, N2AMD demonstrates excellent generalizability and enables seamless integration with advanced NAMD techniques and infrastructures. Taking several extensively investigated semiconductors as the prototypical system, we successfully simulate carrier recombination in both pristine and defective systems at large scales where conventional NAMD often significantly underestimates or even qualitatively incorrectly predicts lifetimes. This framework offers a reliable and efficient approach for conducting accurate NAMD simulations across various condensed materials.

Suggested Citation

  • Changwei Zhang & Yang Zhong & Zhi-Guo Tao & Xinming Qin & Honghui Shang & Zhenggang Lan & Oleg V. Prezhdo & Xin-Gao Gong & Weibin Chu & Hongjun Xiang, 2025. "Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltonians," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-57328-1
    DOI: 10.1038/s41467-025-57328-1
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-025-57328-1
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-025-57328-1?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. Simon Axelrod & Eugene Shakhnovich & Rafael Gómez-Bombarelli, 2022. "Excited state non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Albert Musaelian & Simon Batzner & Anders Johansson & Lixin Sun & Cameron J. Owen & Mordechai Kornbluth & Boris Kozinsky, 2023. "Learning local equivariant representations for large-scale atomistic dynamics," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    3. 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.
    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. Ziduo Yang & Yi-Ming Zhao & Xian Wang & Xiaoqing Liu & Xiuying Zhang & Yifan Li & Qiujie Lv & Calvin Yu-Chian Chen & Lei Shen, 2024. "Scalable crystal structure relaxation using an iteration-free deep generative model with uncertainty quantification," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    2. 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.
    3. Keke Song & Rui Zhao & Jiahui Liu & Yanzhou Wang & Eric Lindgren & Yong Wang & Shunda Chen & Ke Xu & Ting Liang & Penghua Ying & Nan Xu & Zhiqiang Zhao & Jiuyang Shi & Junjie Wang & Shuang Lyu & Zezhu, 2024. "General-purpose machine-learned potential for 16 elemental metals and their alloys," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    4. Yusong Wang & Tong Wang & Shaoning Li & Xinheng He & Mingyu Li & Zun Wang & Nanning Zheng & Bin Shao & Tie-Yan Liu, 2024. "Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    5. Cameron J. Owen & Yu Xie & Anders Johansson & Lixin Sun & Boris Kozinsky, 2024. "Low-index mesoscopic surface reconstructions of Au surfaces using Bayesian force fields," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    6. Jonathan P. Mailoa & Xin Li & Shengyu Zhang, 2024. "3T-VASP: fast ab-initio electrochemical reactor via multi-scale gradient energy minimization," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    7. J. Thorben Frank & Oliver T. Unke & Klaus-Robert Müller & Stefan Chmiela, 2024. "A Euclidean transformer for fast and stable machine learned force fields," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    8. Bowen Hou & Jinyuan Wu & Diana Y. Qiu, 2024. "Unsupervised representation learning of Kohn–Sham states and consequences for downstream predictions of many-body effects," Nature Communications, Nature, vol. 15(1), pages 1-11, 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:16:y:2025:i:1:d:10.1038_s41467-025-57328-1. 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.