Machine‐Learning Prediction of Metal–Organic Framework Guest Accessibility from Linker and Metal Chemistry. (12th January 2022)

• Record Type: Journal Article
• Authors: Pétuya, Rémi; Durdy, Samantha; Antypov, Dmytro; Gaultois, Michael W.; Berry, Neil G.; Darling, George R.; Katsoulidis, Alexandros P.; Dyer, Matthew S.; Rosseinsky, Matthew J.
• Is Part Of: Angewandte Chemie. Volume 134:Number 9(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-12
• Subjects: Database -- Guest accessibility -- Machine learning -- Metal-organic frameworks -- Porosity; Chemistry -- Periodicals; 540
• DOI: 10.1002/ange.202114573
• Languages: English
• ISSNs: 0044-8249

Abstract

Abstract: 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. Abstract : We present a machine‐learning tool that predicts the porosity of metal‐organic framework (MOF) materials based solely on chemical properties of their constituents: the metal and the inorganic linker. Our method leverages data available for thousands of synthesised MOF in prediction of properties of interest in potential synthetic targets without requiring knowledge of their structure but only requiring the knowledge of their constituents.