ZeoSyn: A Comprehensive Zeolite Synthesis Dataset Enabling Machine-Learning Rationalization of Hydrothermal Parameters
Name
zeosyn-a-comprehensive-zeolite-synthesis-dataset-enabling-machine-learning-rationalization-of-hydrothermal-parameters.pdf
Description
Published version
Size
6.55 MB
Format
Adobe PDF
Checksum (MD5)
7903d4453410ecc472b765d7a49e1824
Author(s)
Pan, Elton
Kwon, Soonhyoung
Jensen, Zach
Xie, Mingrou
Gómez-Bombarelli, Rafael
Moliner, Manuel
Román-Leshkov, Yuriy
Olivetti, Elsa
Date Issued
March 6, 2024
Journal
ACS Central Science
Publisher
American Chemical Society
Citation
Elton Pan, Soonhyoung Kwon, Zach Jensen, Mingrou Xie, Rafael Gómez-Bombarelli, Manuel Moliner, Yuriy Román-Leshkov, and Elsa Olivetti. ACS Central Science 2024 10 (3), 729-743.
Version
Final published version
Abstract
Zeolites, nanoporous aluminosilicates with well-defined porous structures, are versatile materials with applications in catalysis, gas separation, and ion exchange. Hydrothermal synthesis is widely used for zeolite production, offering control over composition, crystallinity, and pore size. However, the intricate interplay of synthesis parameters necessitates a comprehensive understanding of synthesis-structure relationships to optimize the synthesis process. Hitherto, public zeolite synthesis databases only contain a subset of parameters and are small in scale, comprising up to a few thousand synthesis routes. We present ZeoSyn, a dataset of 23,961 zeolite hydrothermal synthesis routes, encompassing 233 zeolite topologies and 921 organic structure-directing agents (OSDAs). Each synthesis route comprises comprehensive synthesis parameters: 1) gel composition, 2) reaction conditions, 3) OSDAs, and 4) zeolite products. Using ZeoSyn, we develop a machine learning classifier to predict the resultant zeolite given a synthesis route with >70% accuracy. We employ SHapley Additive exPlanations (SHAP) to uncover key synthesis parameters for >200 zeolite frameworks. We introduce an aggregation approach to extend SHAP to all building units. We demonstrate applications of this approach to phase-selective and intergrowth synthesis. This comprehensive analysis illuminates the synthesis parameters pivotal in driving zeolite crystallization, offering the potential to guide the synthesis of desired zeolites. The dataset is available at https://github.com/eltonpan/zeosyn_dataset.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Department of Chemical Engineering
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1021/acscentsci.3c01615
