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

doi:10.1002/anie.202114573

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 journal › Article › peer-review

43 Scopus citations

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.

Original language	English (US)
Journal	Angewandte Chemie International Edition
DOIs	https://doi.org/10.1002/anie.202114573
State	Published - Dec 8 2021
Externally published	Yes

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

General Chemistry
Catalysis

Access to Document

10.1002/anie.202114573

Cite this

@article{b865bc16c74e4d89a02ecbe25a7f7b6e,

title = "Machine Learning Prediction of Metal-Organic Framework Guest Accessibility from Linker and Metal Chemistry",

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{\textquoteright} 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.",

author = "R{\'e}mi P{\'e}tuya and Samantha Durdy and Dmytro Antypov and Gaultois, {Michael W} and Berry, {Neil G} and Darling, {George R} and Katsoulidis, {Alexandros P} and Dyer, {Matthew S} and Rosseinsky, {Matthew J.}",

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{\textquoteright}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.",

year = "2021",

month = dec,

day = "8",

doi = "10.1002/anie.202114573",

language = "English (US)",

journal = "Angewandte Chemie International Edition",

issn = "1433-7851",

publisher = "John Wiley and Sons Ltd",

}

TY - JOUR

T1 - Machine Learning Prediction of Metal-Organic Framework Guest Accessibility from Linker and Metal Chemistry

AU - Pétuya, Rémi

AU - Durdy, Samantha

AU - Antypov, Dmytro

AU - Gaultois, Michael W

AU - Berry, Neil G

AU - Darling, George R

AU - Katsoulidis, Alexandros P

AU - Dyer, Matthew S

AU - Rosseinsky, Matthew J.

N1 - 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.

PY - 2021/12/8

Y1 - 2021/12/8

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/674023

UR - https://onlinelibrary.wiley.com/doi/10.1002/anie.202114573

U2 - 10.1002/anie.202114573

DO - 10.1002/anie.202114573

M3 - Article

C2 - 34878706

SN - 1433-7851

JO - Angewandte Chemie International Edition

JF - Angewandte Chemie International Edition

ER -

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

Abstract

Bibliographical note

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this