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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
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    References listed on IDEAS

    as
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    2. 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.
    3. 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.
    4. 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.
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