Porous Molecular Materials

Bennett, Steven; Jelfs, Kim E.

doi:10.1002/9781119819783.ch10

AI‐Guided Design and Property Prediction for Zeolites and Nanoporous Materials 2023

DOI: 10.1002/9781119819783.ch10

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Porous Molecular Materials

Steven Bennett¹,

Kim E. Jelfs²

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2024

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Machine learning insights into predicting biogas separation in metal-organic frameworks

Cooley,

Boobier,

Hirst

et al. 2024

Commun Chem

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Breakthroughs in efficient use of biogas fuel depend on successful separation of carbon dioxide/methane streams and identification of appropriate separation materials. In this work, machine learning models are trained to predict biogas separation properties of metal-organic frameworks (MOFs). Training data are obtained using grand canonical Monte Carlo simulations of experimental MOFs which have been carefully curated to ensure data quality and structural viability. The models show excellent performance in predicting gas uptake and classifying MOFs according to the trade-off between gas uptake and selectivity, with R2 values consistently above 0.9 for the validation set. We make prospective predictions on an independent external set of hypothetical MOFs, and examine these predictions in comparison to the results of grand canonical Monte Carlo calculations. The best-performing trained models correctly filter out over 90% of low-performing unseen MOFs, illustrating their applicability to other MOF datasets.

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Machine learning insights into predicting biogas separation in metal-organic frameworks

Cooley,

Boobier,

Hirst

et al. 2024

Commun Chem

View full text Add to dashboard Cite

show abstract

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Porous Molecular Materials

Cited by 1 publication

References 194 publications

Machine learning insights into predicting biogas separation in metal-organic frameworks

Machine learning insights into predicting biogas separation in metal-organic frameworks

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