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

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

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).

Original languageEnglish (US)
Article number3152
JournalNature communications
Volume14
Issue number1
DOIs
StatePublished - Dec 2023

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© 2023, The Author(s).

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