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; Michael Tsapatsis

doi:10.1038/s41467-023-38738-5

Machine learning-assisted crystal engineering of a zeolite

Nat Commun. 2023 May 31;14(1):3152. doi: 10.1038/s41467-023-38738-5.

Authors

Xinyu Li^#¹, He Han^#^{1

2}, Nikolaos Evangelou^#³, Noah J Wichrowski^#⁴, Peng Lu³, Wenqian Xu⁵, Son-Jong Hwang⁶, Wenyang Zhao¹, Chunshan Song², Xinwen Guo², Aditya Bhan⁷, Ioannis G Kevrekidis^{8

9}, Michael Tsapatsis^{10

11

12

13}

Affiliations

¹ Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455, USA.
² State Key Laboratory of Fine Chemicals, PSU-DUT Joint Center for Energy Research, School of Chemical Engineering, Dalian University of Technology, Dalian, 116024, Liaoning Province, China.
³ Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA.
⁴ Department of Applied Mathematics and Statistics, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA.
⁵ X-ray Science Division, Advanced Photon Source, Argonne National Laboratory, Lemont, IL, 60439, USA.
⁶ Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.
⁷ Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455, USA. abhan@umn.edu.
⁸ Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA. yannisk@jhu.edu.
⁹ Department of Applied Mathematics and Statistics, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA. yannisk@jhu.edu.
¹⁰ Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455, USA. tsapatsis@jhu.edu.
¹¹ Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA. tsapatsis@jhu.edu.
¹² Applied Physics Laboratory, Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD, 20723, USA. tsapatsis@jhu.edu.
¹³ Institute for NanoBioTechnology, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA. tsapatsis@jhu.edu.

^# Contributed equally.

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).

Abstract

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