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Scalable crystal structure relaxation using an iteration-free deep generative model with uncertainty quantification

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  • Ziduo Yang

    (National University of Singapore
    Shenzhen Campus of Sun Yat-sen University
    Peking University Shenzhen Graduate School)

  • Yi-Ming Zhao

    (National University of Singapore)

  • Xian Wang

    (National University of Singapore)

  • Xiaoqing Liu

    (National University of Singapore)

  • Xiuying Zhang

    (National University of Singapore)

  • Yifan Li

    (National University of Singapore)

  • Qiujie Lv

    (National University of Singapore
    Shenzhen Campus of Sun Yat-sen University)

  • Calvin Yu-Chian Chen

    (Peking University Shenzhen Graduate School
    Peking University Shenzhen Graduate School
    China Medical University Hospital
    Asia University)

  • Lei Shen

    (National University of Singapore
    National University of Singapore (Chongqing) Research Institute)

Abstract

In computational molecular and materials science, determining equilibrium structures is the crucial first step for accurate subsequent property calculations. However, the recent discovery of millions of new crystals and super large twisted structures has challenged traditional computational methods, both ab initio and machine-learning-based, due to their computationally intensive iterative processes. To address these scalability issues, here we introduce DeepRelax, a deep generative model capable of performing geometric crystal structure relaxation rapidly and without iterations. DeepRelax learns the equilibrium structural distribution, enabling it to predict relaxed structures directly from their unrelaxed ones. The ability to perform structural relaxation at the millisecond level per structure, combined with the scalability of parallel processing, makes DeepRelax particularly useful for large-scale virtual screening. We demonstrate DeepRelax’s reliability and robustness by applying it to five diverse databases, including oxides, Materials Project, two-dimensional materials, van der Waals crystals, and crystals with point defects. DeepRelax consistently shows high accuracy and efficiency, validated by density functional theory calculations. Finally, we enhance its trustworthiness by integrating uncertainty quantification. This work significantly accelerates computational workflows, offering a robust and trustworthy machine-learning method for material discovery and advancing the application of AI for science.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52378-3
    DOI: 10.1038/s41467-024-52378-3
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    References listed on IDEAS

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