Understanding the diversity of the metal-organic framework ecosystem
© 2020, The Author(s). Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal nodes and organic linkers. At present, over 90,000 MOFs have been synthesized and over 500,000 predicted. This raises the question whether a new experimental or predicted structure adds new inf...
| Main Authors: | , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Science and Business Media LLC
2021
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| Online Access: | https://hdl.handle.net/1721.1/135475 |
| _version_ | 1826196165773754368 |
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| author | Moosavi, Seyed Mohamad Nandy, Aditya Jablonka, Kevin Maik Ongari, Daniele Janet, Jon Paul Boyd, Peter G Lee, Yongjin Smit, Berend Kulik, Heather J |
| author2 | Massachusetts Institute of Technology. Department of Chemical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Chemical Engineering Moosavi, Seyed Mohamad Nandy, Aditya Jablonka, Kevin Maik Ongari, Daniele Janet, Jon Paul Boyd, Peter G Lee, Yongjin Smit, Berend Kulik, Heather J |
| author_sort | Moosavi, Seyed Mohamad |
| collection | MIT |
| description | © 2020, The Author(s). Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal nodes and organic linkers. At present, over 90,000 MOFs have been synthesized and over 500,000 predicted. This raises the question whether a new experimental or predicted structure adds new information. For MOF chemists, the chemical design space is a combination of pore geometry, metal nodes, organic linkers, and functional groups, but at present we do not have a formalism to quantify optimal coverage of chemical design space. In this work, we develop a machine learning method to quantify similarities of MOFs to analyse their chemical diversity. This diversity analysis identifies biases in the databases, and we show that such bias can lead to incorrect conclusions. The developed formalism in this study provides a simple and practical guideline to see whether new structures will have the potential for new insights, or constitute a relatively small variation of existing structures. |
| first_indexed | 2024-09-23T10:22:34Z |
| format | Article |
| id | mit-1721.1/135475 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:22:34Z |
| publishDate | 2021 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1354752023-09-19T20:07:09Z Understanding the diversity of the metal-organic framework ecosystem Moosavi, Seyed Mohamad Nandy, Aditya Jablonka, Kevin Maik Ongari, Daniele Janet, Jon Paul Boyd, Peter G Lee, Yongjin Smit, Berend Kulik, Heather J Massachusetts Institute of Technology. Department of Chemical Engineering Massachusetts Institute of Technology. Department of Chemical Engineering © 2020, The Author(s). Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal nodes and organic linkers. At present, over 90,000 MOFs have been synthesized and over 500,000 predicted. This raises the question whether a new experimental or predicted structure adds new information. For MOF chemists, the chemical design space is a combination of pore geometry, metal nodes, organic linkers, and functional groups, but at present we do not have a formalism to quantify optimal coverage of chemical design space. In this work, we develop a machine learning method to quantify similarities of MOFs to analyse their chemical diversity. This diversity analysis identifies biases in the databases, and we show that such bias can lead to incorrect conclusions. The developed formalism in this study provides a simple and practical guideline to see whether new structures will have the potential for new insights, or constitute a relatively small variation of existing structures. 2021-10-27T20:23:36Z 2021-10-27T20:23:36Z 2020 2021-06-11T16:43:24Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135475 en 10.1038/S41467-020-17755-8 Nature Communications Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC Nature |
| spellingShingle | Moosavi, Seyed Mohamad Nandy, Aditya Jablonka, Kevin Maik Ongari, Daniele Janet, Jon Paul Boyd, Peter G Lee, Yongjin Smit, Berend Kulik, Heather J Understanding the diversity of the metal-organic framework ecosystem |
| title | Understanding the diversity of the metal-organic framework ecosystem |
| title_full | Understanding the diversity of the metal-organic framework ecosystem |
| title_fullStr | Understanding the diversity of the metal-organic framework ecosystem |
| title_full_unstemmed | Understanding the diversity of the metal-organic framework ecosystem |
| title_short | Understanding the diversity of the metal-organic framework ecosystem |
| title_sort | understanding the diversity of the metal organic framework ecosystem |
| url | https://hdl.handle.net/1721.1/135475 |
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