Understanding the diversity of the metal-organic framework ecosystem
Name
s41467-020-17755-8.pdf
Description
Published version
Size
3.05 MB
Format
Adobe PDF
Checksum (MD5)
27f8524ae7d93a5764d59d4e82c026bf
Author(s)
Moosavi, Seyed Mohamad
Nandy, Aditya
Jablonka, Kevin Maik
Ongari, Daniele
Janet, Jon Paul
Boyd, Peter G
Lee, Yongjin
Smit, Berend
Kulik, Heather J
Date Issued
2020
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Version
Final published version
Abstract
© 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.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/S41467-020-17755-8
