IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-42863-6.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-42863-6
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-023-42863-6?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. Jian-Rong Li & Jiamei Yu & Weigang Lu & Lin-Bing Sun & Julian Sculley & Perla B. Balbuena & Hong-Cai Zhou, 2013. "Porous materials with pre-designed single-molecule traps for CO2 selective adsorption," Nature Communications, Nature, vol. 4(1), pages 1-8, June.
    2. Patrick Nugent & Youssef Belmabkhout & Stephen D. Burd & Amy J. Cairns & Ryan Luebke & Katherine Forrest & Tony Pham & Shengqian Ma & Brian Space & Lukasz Wojtas & Mohamed Eddaoudi & Michael J. Zaworo, 2013. "Porous materials with optimal adsorption thermodynamics and kinetics for CO2 separation," Nature, Nature, vol. 495(7439), pages 80-84, March.
    3. Jonas Degrave & Federico Felici & Jonas Buchli & Michael Neunert & Brendan Tracey & Francesco Carpanese & Timo Ewalds & Roland Hafner & Abbas Abdolmaleki & Diego de las Casas & Craig Donner & Leslie F, 2022. "Magnetic control of tokamak plasmas through deep reinforcement learning," Nature, Nature, vol. 602(7897), pages 414-419, February.
    4. Sheng Zhou & Osama Shekhah & Adrian Ramírez & Pengbo Lyu & Edy Abou-Hamad & Jiangtao Jia & Jiantang Li & Prashant M. Bhatt & Zhiyuan Huang & Hao Jiang & Tian Jin & Guillaume Maurin & Jorge Gascon & Mo, 2022. "Asymmetric pore windows in MOF membranes for natural gas valorization," Nature, Nature, vol. 606(7915), pages 706-712, June.
    5. Benjamin Burger & Phillip M. Maffettone & Vladimir V. Gusev & Catherine M. Aitchison & Yang Bai & Xiaoyan Wang & Xiaobo Li & Ben M. Alston & Buyi Li & Rob Clowes & Nicola Rankin & Brandon Harris & Rei, 2020. "A mobile robotic chemist," Nature, Nature, vol. 583(7815), pages 237-241, July.
    6. Florian Moreau & Ivan da Silva & Nada H. Al Smail & Timothy L. Easun & Mathew Savage & Harry G. W. Godfrey & Stewart F. Parker & Pascal Manuel & Sihai Yang & Martin Schröder, 2017. "Unravelling exceptional acetylene and carbon dioxide adsorption within a tetra-amide functionalized metal-organic framework," Nature Communications, Nature, vol. 8(1), pages 1-9, April.
    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. Huang, Ruchen & He, Hongwen & Gao, Miaojue, 2023. "Training-efficient and cost-optimal energy management for fuel cell hybrid electric bus based on a novel distributed deep reinforcement learning framework," Applied Energy, Elsevier, vol. 346(C).
    2. Al-Qahtani, Amjad & Parkinson, Brett & Hellgardt, Klaus & Shah, Nilay & Guillen-Gosalbez, Gonzalo, 2021. "Uncovering the true cost of hydrogen production routes using life cycle monetisation," Applied Energy, Elsevier, vol. 281(C).
    3. Zhang, Tianhao & Dong, Zhe & Huang, Xiaojin, 2024. "Multi-objective optimization of thermal power and outlet steam temperature for a nuclear steam supply system with deep reinforcement learning," Energy, Elsevier, vol. 286(C).
    4. Jingqi Wang & Jiapeng Liu & Hongshuai Wang & Musen Zhou & Guolin Ke & Linfeng Zhang & Jianzhong Wu & Zhifeng Gao & Diannan Lu, 2024. "A comprehensive transformer-based approach for high-accuracy gas adsorption predictions in metal-organic frameworks," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    5. Maryam Ghalkhani & Saeid Habibi, 2022. "Review of the Li-Ion Battery, Thermal Management, and AI-Based Battery Management System for EV Application," Energies, MDPI, vol. 16(1), pages 1-16, December.
    6. Susan Erikson, 2021. "COVID‐Apps: Misdirecting Public Health Attention in a Pandemic," Global Policy, London School of Economics and Political Science, vol. 12(S6), pages 97-100, July.
    7. Qingju Wang & Jianbo Hu & Lifeng Yang & Zhaoqiang Zhang & Tian Ke & Xili Cui & Huabin Xing, 2022. "One-step removal of alkynes and propadiene from cracking gases using a multi-functional molecular separator," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    8. Andrea Murari & Riccardo Rossi & Teddy Craciunescu & Jesús Vega & Michela Gelfusa, 2024. "A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    9. Budzianowski, Wojciech M., 2016. "A review of potential innovations for production, conditioning and utilization of biogas with multiple-criteria assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1148-1171.
    10. Yifan Xie & Shuo Feng & Linxiao Deng & Aoran Cai & Liyu Gan & Zifan Jiang & Peng Yang & Guilin Ye & Zaiqing Liu & Li Wen & Qing Zhu & Wanjun Zhang & Zhanpeng Zhang & Jiahe Li & Zeyu Feng & Chutian Zha, 2023. "Inverse design of chiral functional films by a robotic AI-guided system," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    11. Yang, Kaiyuan & Huang, Houjing & Vandans, Olafs & Murali, Adithya & Tian, Fujia & Yap, Roland H.C. & Dai, Liang, 2023. "Applying deep reinforcement learning to the HP model for protein structure prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    12. Weifan Long & Taixian Hou & Xiaoyi Wei & Shichao Yan & Peng Zhai & Lihua Zhang, 2023. "A Survey on Population-Based Deep Reinforcement Learning," Mathematics, MDPI, vol. 11(10), pages 1-17, May.
    13. Amanda A. Volk & Robert W. Epps & Daniel T. Yonemoto & Benjamin S. Masters & Felix N. Castellano & Kristofer G. Reyes & Milad Abolhasani, 2023. "AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    14. Hajkowicz, Stefan & Naughtin, Claire & Sanderson, Conrad & Schleiger, Emma & Karimi, Sarvnaz & Bratanova, Alexandra & Bednarz, Tomasz, 2022. "Artificial intelligence for science – adoption trends and future development pathways," MPRA Paper 115464, University Library of Munich, Germany.
    15. Zi-Jing Zhang & Shu-Wen Li & João C. A. Oliveira & Yanjun Li & Xinran Chen & Shuo-Qing Zhang & Li-Cheng Xu & Torben Rogge & Xin Hong & Lutz Ackermann, 2023. "Data-driven design of new chiral carboxylic acid for construction of indoles with C-central and C–N axial chirality via cobalt catalysis," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    16. Bin Ouyang & Yan Zeng, 2024. "The rise of high-entropy battery materials," Nature Communications, Nature, vol. 15(1), pages 1-5, December.
    17. Wenhao Gao & Priyanka Raghavan & Connor W. Coley, 2022. "Autonomous platforms for data-driven organic synthesis," Nature Communications, Nature, vol. 13(1), pages 1-4, December.
    18. Hao Xu & Jinglong Lin & Dongxiao Zhang & Fanyang Mo, 2023. "Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    19. Hong-Hua Qiu & Lu-Ge Liu, 2018. "A Study on the Evolution of Carbon Capture and Storage Technology Based on Knowledge Mapping," Energies, MDPI, vol. 11(5), pages 1-25, May.
    20. JianHao Qian & HengAn Wu & FengChao Wang, 2023. "A generalized Knudsen theory for gas transport with specular and diffuse reflections," Nature Communications, Nature, vol. 14(1), pages 1-8, 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:14:y:2023:i:1:d:10.1038_s41467-023-42863-6. 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.