Machine Learning Prediction of Metal-Organic Framework Guest Accessibility from Linker and Metal Chemistry

Rémi Pétuya, Samantha Durdy, Dmytro Antypov, Michael W Gaultois, Neil G Berry, George R Darling, Alexandros P Katsoulidis, Matthew S Dyer, Matthew J. Rosseinsky

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


The choice of metal and linker together define the structure and therefore the guest accessibility of a metal-organic framework (MOF), but the large number of possible metal-linker combinations makes the selection of components for synthesis challenging. We predict the guest accessibility of a MOF with 80.5% certainty based solely on the identity of these two components as chosen by the experimentalist, by decomposing reported experimental three-dimensional MOF structures in the Cambridge Structural Database into metal and linker and then learning the connection between the components’ chemistry and the MOF porosity. Pore dimensions of the guest-accessible space are classified into four ranges with three sequential models. Both the dataset and the predictive models are available to download and offer simple guidance in prioritization of the choice of the components for exploratory MOF synthesis for separation and catalysis based on guest accessibility considerations.
Original languageEnglish (US)
JournalAngewandte Chemie International Edition
StatePublished - Dec 8 2021
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2021-12-15
Acknowledged KAUST grant number(s): OSR- 2016-RPP-3273
Acknowledgements: This work is supported by the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement number 692685) and by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) award OSR- 2016-RPP-3273. We thank Seth Wiggins, Aurelia Li and David Fairen-Jimenez for providing their list of 3D MOF. We thank the Leverhulme Trust for funding via the Leverhulme Research Centre for Functional Materials Design. We thank the CCDC for permission to share this data derived from curated information in the CSD regarding MOF crystal structures.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

  • Chemistry(all)
  • Catalysis


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