Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks
Organic structure directing agents (OSDAs) play a crucial role in the synthesis of micro- and mesoporous materials especially in the case of zeolites. Despite the wide use of OSDAs, their interaction with zeolite frameworks is poorly understood, with researchers relying on synthesis heuristics or co...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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American Chemical Society (ACS)
2021
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| Online Access: | https://hdl.handle.net/1721.1/133423 |
| _version_ | 1826201594369146880 |
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| author | Jensen, Zach Kwon, Soonhyoung Schwalbe-Koda, Daniel Paris, Cecilia Gómez-Bombarelli, Rafael Román-Leshkov, Yuriy Corma, Avelino Moliner, Manuel Olivetti, Elsa A |
| author2 | Massachusetts Institute of Technology. Department of Materials Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Materials Science and Engineering Jensen, Zach Kwon, Soonhyoung Schwalbe-Koda, Daniel Paris, Cecilia Gómez-Bombarelli, Rafael Román-Leshkov, Yuriy Corma, Avelino Moliner, Manuel Olivetti, Elsa A |
| author_sort | Jensen, Zach |
| collection | MIT |
| description | Organic structure directing agents (OSDAs) play a crucial role in the synthesis of micro- and mesoporous materials especially in the case of zeolites. Despite the wide use of OSDAs, their interaction with zeolite frameworks is poorly understood, with researchers relying on synthesis heuristics or computationally expensive techniques to predict whether an organic molecule can act as an OSDA for a certain zeolite. In this paper, we undertake a data-driven approach to unearth generalized OSDA-zeolite relationships using a comprehensive database comprising of 5,663 synthesis routes for porous materials. To generate this comprehensive database, we use natural language processing and text mining techniques to extract OSDAs, zeolite phases, and gel chemistry from the scientific literature published between 1966 and 2020. Through structural featurization of the OSDAs using weighted holistic invariant molecular (WHIM) descriptors, we relate OSDAs described in the literature to different types of cage-based, small-pore zeolites. Lastly, we adapt a generative neural network capable of suggesting new molecules as potential OSDAs for a given zeolite structure and gel chemistry. We apply this model to CHA and SFW zeolites generating several alternative OSDA candidates to those currently used in practice. These molecules are further vetted with molecular mechanics simulations to show the model generates physically meaningful predictions. Our model can automatically explore the OSDA space, reducing the amount of simulation or experimentation needed to find new OSDA candidates. |
| first_indexed | 2024-09-23T11:54:11Z |
| format | Article |
| id | mit-1721.1/133423 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:54:11Z |
| publishDate | 2021 |
| publisher | American Chemical Society (ACS) |
| record_format | dspace |
| spelling | mit-1721.1/1334232023-03-01T15:26:58Z Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks Jensen, Zach Kwon, Soonhyoung Schwalbe-Koda, Daniel Paris, Cecilia Gómez-Bombarelli, Rafael Román-Leshkov, Yuriy Corma, Avelino Moliner, Manuel Olivetti, Elsa A Massachusetts Institute of Technology. Department of Materials Science and Engineering Massachusetts Institute of Technology. Department of Chemical Engineering Organic structure directing agents (OSDAs) play a crucial role in the synthesis of micro- and mesoporous materials especially in the case of zeolites. Despite the wide use of OSDAs, their interaction with zeolite frameworks is poorly understood, with researchers relying on synthesis heuristics or computationally expensive techniques to predict whether an organic molecule can act as an OSDA for a certain zeolite. In this paper, we undertake a data-driven approach to unearth generalized OSDA-zeolite relationships using a comprehensive database comprising of 5,663 synthesis routes for porous materials. To generate this comprehensive database, we use natural language processing and text mining techniques to extract OSDAs, zeolite phases, and gel chemistry from the scientific literature published between 1966 and 2020. Through structural featurization of the OSDAs using weighted holistic invariant molecular (WHIM) descriptors, we relate OSDAs described in the literature to different types of cage-based, small-pore zeolites. Lastly, we adapt a generative neural network capable of suggesting new molecules as potential OSDAs for a given zeolite structure and gel chemistry. We apply this model to CHA and SFW zeolites generating several alternative OSDA candidates to those currently used in practice. These molecules are further vetted with molecular mechanics simulations to show the model generates physically meaningful predictions. Our model can automatically explore the OSDA space, reducing the amount of simulation or experimentation needed to find new OSDA candidates. 2021-10-27T19:52:47Z 2021-10-27T19:52:47Z 2021 2021-06-17T18:29:02Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/133423 en 10.1021/acscentsci.1c00024 ACS Central Science Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf American Chemical Society (ACS) ACS |
| spellingShingle | Jensen, Zach Kwon, Soonhyoung Schwalbe-Koda, Daniel Paris, Cecilia Gómez-Bombarelli, Rafael Román-Leshkov, Yuriy Corma, Avelino Moliner, Manuel Olivetti, Elsa A Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks |
| title | Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks |
| title_full | Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks |
| title_fullStr | Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks |
| title_full_unstemmed | Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks |
| title_short | Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks |
| title_sort | discovering relationships between osdas and zeolites through data mining and generative neural networks |
| url | https://hdl.handle.net/1721.1/133423 |
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