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

Cooley, Isabel; Boobier, Samuel; Hirst, Jonathan D.; Besley, Elena

Abstract

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.

Citation

Cooley, I., Boobier, S., Hirst, J. D., & Besley, E. (2024). Machine learning insights into predicting biogas separation in metal-organic frameworks. Communications Chemistry, 7(1), Article 102. https://doi.org/10.1038/s42004-024-01166-7

• Journal Article Type: Article
• Acceptance Date: Apr 2, 2024
• Online Publication Date: May 8, 2024
• Publication Date: May 8, 2024
• Deposit Date: May 13, 2024
• Publicly Available Date: May 14, 2024
• Journal: Communications Chemistry
• Print ISSN: 2399-3669
• Electronic ISSN: 2399-3669
• Publisher: Nature Research
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Issue: 1
• Article Number: 102
• DOI: https://doi.org/10.1038/s42004-024-01166-7
• Keywords: Computational chemistry; Metal–organic frameworks; Porous materials
• Public URL: https://nottingham-repository.worktribe.com/output/34633710
• Publisher URL: https://www.nature.com/articles/s42004-024-01166-7