Understanding the diversity of the metal-organic framework ecosystem
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Abstract
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.
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Moosavi et al. noted that KRR models trained to predict gas separation characteristics on different data sets were not transferable.
“Moosavi et al. (40) noted that KRR models trained to predict gas separation characteristics (e.g., CO 2 adsorption) on different data sets [e.g., experimental CoRE-MOF (29) versus hypothetical BW-DB (38) or ToBaCCo (28)] were not transferable.”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [40]
In Results, “Applications of diversity analysis” (page not provided), the paper reports poor cross-database transferability for models trained on biased datasets, but those models are random forests, not KRR. In “Predicting adsorption properties of MOFs” (page not provided), it reports good KRR predictions for adsorption properties within the CoRE-2019 and BW-20K databases; it does not establish that KRR models failed to transfer between them. (LLM, conf=0.88)
Experimental — accuracy not yet measuredOne-shot models such as LASSO or random forests are widely used both as ML models for property prediction and for selection of important features.
“In a similar fashion, one-shot models such as least absolute shrinkage and selection operator (LASSO) or random forests are widely used both as ML models for property prediction (61) and for selection of important features (22,32,40,43) (Figure 4).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [40]
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 [40]
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 [40]
Extracted design principles show a significant difference when obtained from feature importance analysis of machine learning models trained on experimental versus hypothetical sets.
“As a result, extracted design principles show a significant difference when obtained from feature importance analysis of ML models trained on these different sets (40) (Figure 5).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [40]
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- Authors
- Moosavi, S. M., Nandy, A., Jablonka, K. M., Ongari, D., Janet, J. P., Boyd, P. G., Lee, Y., Smit, B., & Kulik, H. J.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Massachusetts Institute of Technology (US)
- ShanghaiTech University (CN)
- Corresponding author
- Smit, Berend
- Kulik, Heather J.
- ORCID
- Venue
- Nature Communications
- Published
- 2020-08-13
- Volume
- 11
- Issue
- 1
- Pages
- 4068-4068
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC7426948
- OpenAlex
- W3048908832
- Cited by
- 622
- References
- 73
- Citations by year
- 2026: 103 · 2025: 117 · 2024: 121 · 2023: 112 · 2022: 101 · 2021: 57 · 2020: 11
- FWCI
- 44.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by-nc-nd)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- National Science Foundation (1122374)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (184404)
- Advanced Research Projects Agency
- H2020 European Research Council (666983)
- 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, chemical diversity, machine learning, MOF database, database bias, metal nodes, organic linkers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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