Machine learning-assisted crystal engineering of a zeolite
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 zeol...
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-05-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-38738-5 |
| _version_ | 1827933323163336704 |
|---|---|
| author | 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 |
| author_facet | 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 |
| author_sort | Xinyu Li |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-03-13T07:23:12Z |
| format | Article |
| id | doaj.art-5d4c7b3390d94511a9fa92a2b79465be |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-13T07:23:12Z |
| publishDate | 2023-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-5d4c7b3390d94511a9fa92a2b79465be2023-06-04T11:32:52ZengNature PortfolioNature Communications2041-17232023-05-0114111210.1038/s41467-023-38738-5Machine learning-assisted crystal engineering of a zeoliteXinyu Li0He Han1Nikolaos Evangelou2Noah J. Wichrowski3Peng Lu4Wenqian Xu5Son-Jong Hwang6Wenyang Zhao7Chunshan Song8Xinwen Guo9Aditya Bhan10Ioannis G. Kevrekidis11Michael Tsapatsis12Department of Chemical Engineering and Materials Science, University of MinnesotaDepartment of Chemical Engineering and Materials Science, University of MinnesotaDepartment of Chemical and Biomolecular Engineering, Johns Hopkins UniversityDepartment of Applied Mathematics and Statistics, Johns Hopkins UniversityDepartment of Chemical and Biomolecular Engineering, Johns Hopkins UniversityX-ray Science Division, Advanced Photon Source, Argonne National LaboratoryDivision of Chemistry and Chemical Engineering, California Institute of TechnologyDepartment of Chemical Engineering and Materials Science, University of MinnesotaState Key Laboratory of Fine Chemicals, PSU-DUT Joint Center for Energy Research, School of Chemical Engineering, Dalian University of TechnologyState Key Laboratory of Fine Chemicals, PSU-DUT Joint Center for Energy Research, School of Chemical Engineering, Dalian University of TechnologyDepartment of Chemical Engineering and Materials Science, University of MinnesotaDepartment of Chemical and Biomolecular Engineering, Johns Hopkins UniversityDepartment of Chemical Engineering and Materials Science, University of MinnesotaAbstract 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).https://doi.org/10.1038/s41467-023-38738-5 |
| spellingShingle | 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 Machine learning-assisted crystal engineering of a zeolite Nature Communications |
| title | Machine learning-assisted crystal engineering of a zeolite |
| title_full | Machine learning-assisted crystal engineering of a zeolite |
| title_fullStr | Machine learning-assisted crystal engineering of a zeolite |
| title_full_unstemmed | Machine learning-assisted crystal engineering of a zeolite |
| title_short | Machine learning-assisted crystal engineering of a zeolite |
| title_sort | machine learning assisted crystal engineering of a zeolite |
| url | https://doi.org/10.1038/s41467-023-38738-5 |
| work_keys_str_mv | AT xinyuli machinelearningassistedcrystalengineeringofazeolite AT hehan machinelearningassistedcrystalengineeringofazeolite AT nikolaosevangelou machinelearningassistedcrystalengineeringofazeolite AT noahjwichrowski machinelearningassistedcrystalengineeringofazeolite AT penglu machinelearningassistedcrystalengineeringofazeolite AT wenqianxu machinelearningassistedcrystalengineeringofazeolite AT sonjonghwang machinelearningassistedcrystalengineeringofazeolite AT wenyangzhao machinelearningassistedcrystalengineeringofazeolite AT chunshansong machinelearningassistedcrystalengineeringofazeolite AT xinwenguo machinelearningassistedcrystalengineeringofazeolite AT adityabhan machinelearningassistedcrystalengineeringofazeolite AT ioannisgkevrekidis machinelearningassistedcrystalengineeringofazeolite AT michaeltsapatsis machinelearningassistedcrystalengineeringofazeolite |