Machine learning-assisted crystal engineering of a zeolite

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 () and Michael Tsapatsis ()
Additional contact information
Xinyu Li: University of Minnesota
He Han: University of Minnesota
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
Michael Tsapatsis: University of Minnesota

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: 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).

Date: 2023
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DOI: 10.1038/s41467-023-38738-5

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