Large-scale screening of hypothetical metal–organic frameworks
Abstract
Metal-organic frameworks (MOFs) are porous materials constructed from modular molecular building blocks, typically metal clusters and organic linkers. These can, in principle, be assembled to form an almost unlimited number of MOFs, yet materials reported to date represent only a tiny fraction of the possible combinations. Here, we demonstrate a computational approach to generate all conceivable MOFs from a given chemical library of building blocks (based on the structures of known MOFs) and rapidly screen them to find the best candidates for a specific application. From a library of 102 building blocks we generated 137,953 hypothetical MOFs and for each one calculated the pore-size distribution, surface area and methane-storage capacity. We identified over 300 MOFs with a predicted methane-storage capacity better than that of any known material, and this approach also revealed structure-property relationships. Methyl-functionalized MOFs were frequently top performers, so we selected one such promising MOF and experimentally confirmed its predicted capacity.
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Combinatorial enumeration with small changes in linker chemistry and metal SBUs can result in large data sets of 100k or more MOFs, although how well such sets represent the diversity of synthetically accessible materials has been questioned.
“Combinatorial enumeration with small changes in linker chemistry (e.g., functional groups) and metal SBUs can result in large data sets of 100k or more MOFs (27,28,38,39), although how well such sets represent the diversity of synthetically accessible materials has been questioned (40)”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [27]
Experimental data sets do not necessarily have the same characteristics as hypothetical sets.
“It has been observed (40) that experimental data sets [e.g., of MOFs in CoRE-MOF (29)] also do not necessarily have the same characteristics as hypothetical sets [e.g., hMOF (27) or ToBaCCo (28)].”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [27]
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- Authors
- Wilmer, C. E., Leaf, M., Lee, C. Y., Farha, O. K., Hauser, B. G., Hupp, J. T., & Snurr, R. Q.
- Affiliations
- Northwestern University (US)
- Corresponding author
- Snurr, Randall Q.
- ORCID
- Venue
- Nature Chemistry
- Published
- 2011-11-06
- Volume
- 4
- Issue
- 2
- Pages
- 83-89
- ISSN
- 1755-4330 (ISSN-L)
- 1755-4349
- OpenAlex
- W2100716186
- Cited by
- 1,439
- References
- 51
- Citations by year
- 2026: 97 · 2025: 117 · 2024: 128 · 2023: 117 · 2022: 97 · 2021: 111 · 2020: 115 · 2019: 108 · 2018: 78 · 2017: 82 · 2016: 89 · 2015: 87 · 2014: 96 · 2013: 72 · 2012: 44
- FWCI
- 41.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Chemistry (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Energy
- Northwestern University
- Defense Threat Reduction Agency
- International Institute for Nanotechnology, Northwestern University
- Topics
- Physical Sciences › Chemistry › Inorganic Chemistry › Metal-Organic Frameworks: Synthesis and Applications
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Materials Science › Materials Chemistry › Covalent Organic Framework Applications
- Keywords
- metal-organic frameworks, computational screening, methane storage, pore size distribution, surface area, structure-property relationships, porous media, combinatorial materials design
- MeSH terms
- Metals, Models, Chemical, Organic Chemicals
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