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Machine learning-assisted crystal engineering of a zeolite

Author

Listed:
  • Xinyu Li

    (University of Minnesota)

  • He Han

    (University of Minnesota
    Dalian University of Technology)

  • Nikolaos Evangelou

    (Johns Hopkins University)

  • Noah J. Wichrowski

    (Johns Hopkins University)

  • Peng Lu

    (Johns Hopkins University)

  • Wenqian Xu

    (Advanced Photon Source, Argonne National Laboratory)

  • Son-Jong Hwang

    (California Institute of Technology)

  • Wenyang Zhao

    (University of Minnesota)

  • Chunshan Song

    (Dalian University of Technology)

  • Xinwen Guo

    (Dalian University of Technology)

  • Aditya Bhan

    (University of Minnesota)

  • Ioannis G. Kevrekidis

    (Johns Hopkins University
    Johns Hopkins University)

  • Michael Tsapatsis

    (University of Minnesota
    Johns Hopkins University
    Johns Hopkins University
    Johns Hopkins University)

Abstract

It is shown that Machine Learning (ML) algorithms can usefully capture the effect of crystallization composition and conditions (inputs) on key microstructural characteristics (outputs) of faujasite type zeolites (structure types FAU, EMT, and their intergrowths), which are widely used zeolite catalysts and adsorbents. The utility of ML (in particular, Geometric Harmonics) toward learning input-output relationships of interest is demonstrated, and a comparison with Neural Networks and Gaussian Process Regression, as alternative approaches, is provided. Through ML, synthesis conditions were identified to enhance the Si/Al ratio of high purity FAU zeolite to the hitherto highest level (i.e., Si/Al = 3.5) achieved via direct (not seeded), and organic structure-directing-agent-free synthesis from sodium aluminosilicate sols. The analysis of the ML algorithms’ results offers the insight that reduced Na2O content is key to formulating FAU materials with high Si/Al ratio. An acid catalyst prepared by partial ion exchange of the high-Si/Al-ratio FAU (Si/Al = 3.5) exhibits improved proton reactivity (as well as specific activity, per unit mass of catalyst) in propane cracking and dehydrogenation compared to the catalyst prepared from the previously reported highest Si/Al ratio (Si/Al = 2.8).

Suggested Citation

  • Xinyu Li & He Han & Nikolaos Evangelou & Noah J. Wichrowski & Peng Lu & Wenqian Xu & Son-Jong Hwang & Wenyang Zhao & Chunshan Song & Xinwen Guo & Aditya Bhan & Ioannis G. Kevrekidis & Michael Tsapatsi, 2023. "Machine learning-assisted crystal engineering of a zeolite," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38738-5
    DOI: 10.1038/s41467-023-38738-5
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    References listed on IDEAS

    as
    1. Koki Muraoka & Yuki Sada & Daiki Miyazaki & Watcharop Chaikittisilp & Tatsuya Okubo, 2019. "Linking synthesis and structure descriptors from a large collection of synthetic records of zeolite materials," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    2. Mark E. Davis, 2002. "Ordered porous materials for emerging applications," Nature, Nature, vol. 417(6891), pages 813-821, June.
    3. Joshua L. Lansford & Dionisios G. Vlachos, 2020. "Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
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