Machine learning-assisted crystal engineering of a zeolite

Li, Xinyu; Han, He; Evangelou, Nikolaos; Wichrowski, Noah J.; Lu, Peng; Xu, Wenqian; Hwang, Son-Jong; Zhao, Wenyang; Song, Chunshan; Guo, Xinwen; Bhan, Aditya; Kevrekidis, Ioannis G.; Tsapatsis, Michael

doi:10.1038/s41467-023-38738-5

Title: Machine learning-assisted crystal engineering of a zeolite

Journal Article · Wed May 31 00:00:00 UTC 2023 · Nature Communications

DOI: https://doi.org/10.1038/s41467-023-38738-5 · OSTI ID:1975891

Li, Xinyu; Han, He; Evangelou, Nikolaos; Wichrowski, Noah J.; Lu, Peng;

; Hwang, Son-Jong;

;

; Kevrekidis, Ioannis G.;

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 Na ₂ O 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).

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States); University of Minnesota, Twin Cities, MN (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division (CSGB)

Grant/Contract Number:: AC02-06CH11357; SC0001004; SC0008688; SC0019028; SC0023403

OSTI ID:: 1975891

Journal Information:: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 14; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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Related Subjects

36 MATERIALS SCIENCE
42 ENGINEERING
Materials for energy and catalysis
Zeolite
machine learning
high silica material
crystallization
microstructure
catalyst stability
theory and computation

Title: Machine learning-assisted crystal engineering of a zeolite

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