IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-46276-x.html
   My bibliography  Save this article

A comprehensive transformer-based approach for high-accuracy gas adsorption predictions in metal-organic frameworks

Author

Listed:
  • Jingqi Wang

    (Tsinghua University
    DP Technology)

  • Jiapeng Liu

    (Sun Yat-Sen University
    AI for Science Institute)

  • Hongshuai Wang

    (DP Technology
    Soochow University)

  • Musen Zhou

    (University of California)

  • Guolin Ke

    (DP Technology)

  • Linfeng Zhang

    (DP Technology
    AI for Science Institute)

  • Jianzhong Wu

    (University of California)

  • Zhifeng Gao

    (DP Technology)

  • Diannan Lu

    (Tsinghua University)

Abstract

Gas separation is crucial for industrial production and environmental protection, with metal-organic frameworks (MOFs) offering a promising solution due to their tunable structural properties and chemical compositions. Traditional simulation approaches, such as molecular dynamics, are complex and computationally demanding. Although feature engineering-based machine learning methods perform better, they are susceptible to overfitting because of limited labeled data. Furthermore, these methods are typically designed for single tasks, such as predicting gas adsorption capacity under specific conditions, which restricts the utilization of comprehensive datasets including all adsorption capacities. To address these challenges, we propose Uni-MOF, an innovative framework for large-scale, three-dimensional MOF representation learning, designed for multi-purpose gas prediction. Specifically, Uni-MOF serves as a versatile gas adsorption estimator for MOF materials, employing pure three-dimensional representations learned from over 631,000 collected MOF and COF structures. Our experimental results show that Uni-MOF can automatically extract structural representations and predict adsorption capacities under various operating conditions using a single model. For simulated data, Uni-MOF exhibits remarkably high predictive accuracy across all datasets. Additionally, the values predicted by Uni-MOF correspond with the outcomes of adsorption experiments. Furthermore, Uni-MOF demonstrates considerable potential for broad applicability in predicting a wide array of other properties.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46276-x
    DOI: 10.1038/s41467-024-46276-x
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-46276-x
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-46276-x?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. David S. Sholl & Ryan P. Lively, 2016. "Seven chemical separations to change the world," Nature, Nature, vol. 532(7600), pages 435-437, April.
    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. Seyed Mohamad Moosavi & Aditya Nandy & Kevin Maik Jablonka & Daniele Ongari & Jon Paul Janet & Peter G. Boyd & Yongjin Lee & Berend Smit & Heather J. Kulik, 2020. "Understanding the diversity of the metal-organic framework ecosystem," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    4. Youshi Lan & Xianghao Han & Minman Tong & Hongliang Huang & Qingyuan Yang & Dahuan Liu & Xin Zhao & Chongli Zhong, 2018. "Materials genomics methods for high-throughput construction of COFs and targeted synthesis," Nature Communications, Nature, vol. 9(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. Zeyu Liu & Youshi Lan & Jianfeng Jia & Yiyun Geng & Xiaobin Dai & Litang Yan & Tongyang Hu & Jing Chen & Krzysztof Matyjaszewski & Gang Ye, 2022. "Multi-scale computer-aided design and photo-controlled macromolecular synthesis boosting uranium harvesting from seawater," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    2. Qingju Wang & Lifeng Yang & Tian Ke & Jianbo Hu & Xian Suo & Xili Cui & Huabin Xing, 2024. "Selective sorting of hexane isomers by anion-functionalized metal-organic frameworks with optimal energy regulation," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    3. Xiuling Chen & Yanfang Fan & Lei Wu & Linzhou Zhang & Dong Guan & Canghai Ma & Nanwen Li, 2021. "Ultra-selective molecular-sieving gas separation membranes enabled by multi-covalent-crosslinking of microporous polymer blends," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    4. 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).
    5. Bingbing Yuan & Yuhang Zhang & Pengfei Qi & Dongxiao Yang & Ping Hu & Siheng Zhao & Kaili Zhang & Xiaozhuan Zhang & Meng You & Jiabao Cui & Juhui Jiang & Xiangdong Lou & Q. Jason Niu, 2024. "Self-assembled dendrimer polyamide nanofilms with enhanced effective pore area for ion separation," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    6. Xueru Yan & Tianqi Song & Min Li & Zhi Wang & Xinlei Liu, 2024. "Sub-micro porous thin polymer membranes for discriminating H2 and CO2," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    7. Letitia Petrescu & Codruta-Maria Cormos, 2022. "Classical and Process Intensification Methods for Acetic Acid Concentration: Technical and Environmental Assessment," Energies, MDPI, vol. 15(21), pages 1-23, October.
    8. 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.
    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. Peixin Zhang & Lifeng Yang & Xing Liu & Jun Wang & Xian Suo & Liyuan Chen & Xili Cui & Huabin Xing, 2022. "Ultramicroporous material based parallel and extended paraffin nano-trap for benchmark olefin purification," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    11. Mariem Ferchichi & Laszlo Hegely & Peter Lang, 2021. "Decrease of energy demand of semi-batch distillation policies," Energy & Environment, , vol. 32(8), pages 1479-1503, December.
    12. Muhammad Abdul Qyyum & Yus Donald Chaniago & Wahid Ali & Hammad Saulat & Moonyong Lee, 2020. "Membrane-Assisted Removal of Hydrogen and Nitrogen from Synthetic Natural Gas for Energy-Efficient Liquefaction," Energies, MDPI, vol. 13(19), pages 1-18, September.
    13. Yongyang Song & Jiajia Zhou & Zhongpeng Zhu & Xiaoxia Li & Yue Zhang & Xinyi Shen & Padraic O’Reilly & Xiuling Li & Xinmiao Liang & Lei Jiang & Shutao Wang, 2023. "Heterostructure particles enable omnidispersible in water and oil towards organic dye recycle," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    14. Zhenggong Wang & Xiaofan Luo & Zejun Song & Kuan Lu & Shouwen Zhu & Yanshao Yang & Yatao Zhang & Wangxi Fang & Jian Jin, 2022. "Microporous polymer adsorptive membranes with high processing capacity for molecular separation," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    15. Fu, Pengbo & Yu, Hao & Li, Qiqi & Cheng, Tingting & Zhang, Fangzheng & Yang, Tao & Huang, Yuan & Li, Jianping & Fang, Xiangchen & Xiu, Guangli & Wang, Hualin, 2022. "Cyclone rotational drying of lignite based on particle high-speed self-rotation: Lower carrier gas temperature and shorter residence time," Energy, Elsevier, vol. 244(PB).
    16. Bruno Franco & Lieven Clarisse & Martin Van Damme & Juliette Hadji-Lazaro & Cathy Clerbaux & Pierre-François Coheur, 2022. "Ethylene industrial emitters seen from space," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    17. 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.
    18. Lei Zhang & Zhe Chen & Zhenpeng Liu & Jun Bu & Wenxiu Ma & Chen Yan & Rui Bai & Jin Lin & Qiuyu Zhang & Junzhi Liu & Tao Wang & Jian Zhang, 2021. "Efficient electrocatalytic acetylene semihydrogenation by electron–rich metal sites in N–heterocyclic carbene metal complexes," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    19. Jinqiu Yuan & Xinda You & Niaz Ali Khan & Runlai Li & Runnan Zhang & Jianliang Shen & Li Cao & Mengying Long & Yanan Liu & Zijian Xu & Hong Wu & Zhongyi Jiang, 2022. "Photo-tailored heterocrystalline covalent organic framework membranes for organics separation," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    20. Cheng-Rong Zhang & Wei-Rong Cui & Shun-Mo Yi & Cheng-Peng Niu & Ru-Ping Liang & Jia-Xin Qi & Xiao-Juan Chen & Wei Jiang & Xin Liu & Qiu-Xia Luo & Jian-Ding Qiu, 2022. "An ionic vinylene-linked three-dimensional covalent organic framework for selective and efficient trapping of ReO4− or 99TcO4−," Nature Communications, Nature, vol. 13(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:15:y:2024:i:1:d:10.1038_s41467-024-46276-x. 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.