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Understanding the diversity of the metal-organic framework ecosystem

Author

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  • Seyed Mohamad Moosavi

    (Institut des Sciences et Ingénierie Chimiques, École, Polytechnique Fédérale de Lausanne (EPFL)
    Massachusetts Institute of Technology)

  • Aditya Nandy

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Kevin Maik Jablonka

    (Institut des Sciences et Ingénierie Chimiques, École, Polytechnique Fédérale de Lausanne (EPFL))

  • Daniele Ongari

    (Institut des Sciences et Ingénierie Chimiques, École, Polytechnique Fédérale de Lausanne (EPFL))

  • Jon Paul Janet

    (Massachusetts Institute of Technology)

  • Peter G. Boyd

    (Institut des Sciences et Ingénierie Chimiques, École, Polytechnique Fédérale de Lausanne (EPFL))

  • Yongjin Lee

    (ShanghaiTech University)

  • Berend Smit

    (Institut des Sciences et Ingénierie Chimiques, École, Polytechnique Fédérale de Lausanne (EPFL))

  • Heather J. Kulik

    (Massachusetts Institute of Technology)

Abstract

Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal nodes and organic linkers. At present, over 90,000 MOFs have been synthesized and over 500,000 predicted. This raises the question whether a new experimental or predicted structure adds new information. For MOF chemists, the chemical design space is a combination of pore geometry, metal nodes, organic linkers, and functional groups, but at present we do not have a formalism to quantify optimal coverage of chemical design space. In this work, we develop a machine learning method to quantify similarities of MOFs to analyse their chemical diversity. This diversity analysis identifies biases in the databases, and we show that such bias can lead to incorrect conclusions. The developed formalism in this study provides a simple and practical guideline to see whether new structures will have the potential for new insights, or constitute a relatively small variation of existing structures.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17755-8
    DOI: 10.1038/s41467-020-17755-8
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    Cited by:

    1. Shahvari, Saba Zakeri & Clark, Jordan D., 2023. "Approaching theoretical maximum energy performance for desiccant dehumidification using staged and optimized metal-organic frameworks," Applied Energy, Elsevier, vol. 331(C).
    2. Yeonghun Kang & Jihan Kim, 2024. "ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    3. 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.
    4. Dengping Lyu & Wei Xu & Jae Elise L. Payong & Tianran Zhang & Yufeng Wang, 2022. "Low-dimensional assemblies of metal-organic framework particles and mutually coordinated anisotropy," Nature Communications, Nature, vol. 13(1), pages 1-11, December.

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