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Direct prediction of gas adsorption via spatial atom interaction learning

Author

Listed:
  • Jiyu Cui

    (Zhejiang University)

  • Fang Wu

    (Zhejiang University
    ZJU-Hangzhou Global Scientific and Technological Innovation Center
    Columbia University)

  • Wen Zhang

    (Zhejiang University)

  • Lifeng Yang

    (Zhejiang University
    ZJU-Hangzhou Global Scientific and Technological Innovation Center)

  • Jianbo Hu

    (Zhejiang University
    ZJU-Hangzhou Global Scientific and Technological Innovation Center)

  • Yin Fang

    (Zhejiang University
    ZJU-Hangzhou Global Scientific and Technological Innovation Center
    Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies)

  • Peng Ye

    (Zhejiang University
    ZJU-Hangzhou Global Scientific and Technological Innovation Center
    Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies)

  • Qiang Zhang

    (Zhejiang University
    ZJU-Hangzhou Global Scientific and Technological Innovation Center
    Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies)

  • Xian Suo

    (Zhejiang University
    ZJU-Hangzhou Global Scientific and Technological Innovation Center)

  • Yiming Mo

    (Zhejiang University
    ZJU-Hangzhou Global Scientific and Technological Innovation Center)

  • Xili Cui

    (Zhejiang University
    ZJU-Hangzhou Global Scientific and Technological Innovation Center)

  • Huajun Chen

    (Zhejiang University
    ZJU-Hangzhou Global Scientific and Technological Innovation Center
    Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies)

  • Huabin Xing

    (Zhejiang University
    ZJU-Hangzhou Global Scientific and Technological Innovation Center)

Abstract

Physisorption relying on crystalline porous materials offers prospective avenues for sustainable separation processes, greenhouse gas capture, and energy storage. However, the lack of end-to-end deep learning model for adsorption prediction confines the rapid and precise screen of crystalline porous materials. Here, we present DeepSorption, a spatial atom interaction learning network that realizes accurate, fast, and direct structure-adsorption prediction with only information of atomic coordinate and chemical element types. The breakthrough in prediction is attributed to the awareness of global structure and local spatial atom interactions endowed by the developed Matformer, which provides the intuitive visualization of atomic-level thinking and executing trajectory in crystalline porous materials prediction. Complete adsorption curves prediction could be performed using DeepSorption with a higher accuracy than Grand canonical Monte Carlo simulation and other machine learning models, a 20-35% decline in the mean absolute error compared to graph neural network CGCNN and machine learning models based on descriptors. Since the established direct associations between raw structure and target functions are based on the understanding of the fundamental chemistry of interatomic interactions, the deep learning network is rationally universal in predicting the different physicochemical properties of various crystalline materials.

Suggested Citation

  • Jiyu Cui & Fang Wu & Wen Zhang & Lifeng Yang & Jianbo Hu & Yin Fang & Peng Ye & Qiang Zhang & Xian Suo & Yiming Mo & Xili Cui & Huajun Chen & Huabin Xing, 2023. "Direct prediction of gas adsorption via spatial atom interaction learning," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42863-6
    DOI: 10.1038/s41467-023-42863-6
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