Metal–Organic Framework Stability in Water and Harsh Environments from Data-Driven Models Trained on the Diverse WS24 Data Set
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Terrones_JACS_2024.pdf
Description
Accepted version
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4.85 MB
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Checksum (MD5)
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Author(s)
Terrones, Gianmarco G
Huang, Shih-Peng
Rivera, Matthew P
Yue, Shuwen
Hernandez, Alondra
Kulik, Heather J
Date Issued
July 10, 2024
Journal
Journal of the American Chemical Society
Publisher
American Chemical Society
Citation
Terrones, Gianmarco G, Huang, Shih-Peng, Rivera, Matthew P, Yue, Shuwen, Hernandez, Alondra et al. 2024. "Metal–Organic Framework Stability in Water and Harsh Environments from Data-Driven Models Trained on the Diverse WS24 Data Set." Journal of the American Chemical Society, 146 (29).
Version
Author's final manuscript
Abstract
Metal-organic frameworks (MOFs) are porous materials with applications in gas separations and catalysis, but a lack of water stability often limits their practical use given the ubiquity of water. Consequently, it is useful to predict whether a MOF is water-stable before investing time and resources into synthesis. Existing heuristics for designing water-stable MOFs lack generality and limit the diversity of explored chemistry due to narrowly defined criteria. Machine learning (ML) models offer the promise to improve the generality of predictions but require data. In an improvement on previous efforts, we enlarge the available training data for MOF water stability prediction by over 400%, adding 911 MOFs with water stability labels assigned through semiautomated manuscript analysis to curate the new data set WS24. The additional data are shown to improve ML model performance (test ROC-AUC > 0.8) over diverse chemistry for the prediction of both water stability and stability in harsher acidic conditions. We illustrate how the expanded data set and models can be used with a previously developed activation stability model in combination with genetic algorithms to quickly screen ∼10,000 MOFs from a space of hundreds of thousands for candidates with multivariate stability (upon activation, in water, and in acid). We uncover metal- and geometry-specific design rules for robust MOFs. The data set and ML models developed in this work, which we disseminate through an easy-to-use web interface, are expected to contribute toward the accelerated discovery of novel, water-stable MOFs for applications such as direct air gas capture and water treatment.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Chemistry
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
10.1021/jacs.4c05879
