Machine learning-assisted crystal engineering of a zeolite
Creators
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1.
University of Minnesota
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2.
Dalian University of Technology
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3.
Johns Hopkins University
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4.
Argonne National Laboratory
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5.
California Institute of Technology
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6.
Johns Hopkins University Applied Physics Laboratory
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).
Additional Information
© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. We are indebted to Prof. Constantine Frangakis of the Biostatistics Department of Johns Hopkins University for several useful conversations about the statistical analysis of our data. We acknowledge partial support from the Catalysis Center for Energy Innovation, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, and Office of Basic Energy Sciences under Award No. DE-SC0001004 and support from the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award No. DE-SC0023403 (Separation Science Program). Partial support was also provided by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences (Award DE-FG02-12ER16362), and by the U.S. Department of Energy, Office of Basic Energy Science, Catalysis Science Program (Award DE-SC00019028). Parts of this work were carried out in the Characterization Facility, University of Minnesota, which receives partial NSF support through the MRSEC and NNIN programs (DMR-1420013). Solid-state MAS NMR measurements were provided by the NMR facility at Caltech. The synchrotron XRD data were collected through the mail-in program at Beamline 17-BM of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility, operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. These authors contributed equally: Xinyu Li, He Han, Nikolaos Evangelou, Noah J. Wichrowski. Contributions. M.T. and I.G.K. conceived the project. M.T., I.G.K., and A.B. co-supervised the work with emphasis on synthesis and characterization, Machine Learning, and catalysis, respectively. X.L. performed most of the synthesis, characterization, and catalytic tests. Early synthesis experiments, mostly described in Supplementary Table 2, were performed by H.H., co-supervised by C.S., X.G., and M.T. W.X. performed synchrotron XRD experiments and contributed to data analysis. P.L. provided some of the synthesis experiments in Supplementary Table 1. S.-J.H. performed NMR and contributed to analysis of data. W.Z. collected TEM/SEM images for part of samples listed in Supplementary Table 1 and contributed to analysis of data. N.E. and N.J.W. performed all ML analysis and predictions, supervised by I.G.K. X.L., N.E., N.J.W., M.T., I.G.K., A.B. wrote the paper with contributions from all co-authors. Data availability. The data that support the findings of this study are provided in Supplementary Information and Source Data file. Source Data are provided as a Source Data file and enclosed with this paper. Details listed in the Supplementary Information consist of the synthesis procedures, characterization results (XRD patterns, Ar-adsorption isotherms, SEM/TEM images, ²⁹Si solid-state NMR), reactivity analysis, and machine learning methods and results. Source data are provided with this paper. Code availability. The codes used to train the Machine learning models can be accessed in the public Gitlab repository (https://gitlab.com/nicolasevangelou/zeolites_ml.git) and the Figshare Dataset. The authors declare no competing interests.Attached Files
Published - 41467_2023_Article_38738.pdf
Supplemental Material - 41467_2023_38738_MOESM1_ESM.pdf
Supplemental Material - 41467_2023_38738_MOESM3_ESM.xlsx
Files
41467_2023_Article_38738.pdf
Additional details
Identifiers
- PMCID
- PMC10232492
- Eprint ID
- 122018
- Resolver ID
- CaltechAUTHORS:20230628-257020000.11
Funding
- Department of Energy (DOE)
- DE-SC0001004
- Department of Energy (DOE)
- DE-SC0023403
- Department of Energy (DOE)
- DE-FG02-12ER16362
- Department of Energy (DOE)
- DE-SC00019028
- NSF
- DMR-1420013
- Department of Energy (DOE)
- DE-AC02-06CH11357
Dates
- Created
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2023-07-01Created from EPrint's datestamp field
- Updated
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2023-07-01Created from EPrint's last_modified field