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

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 predic

SUBMITTER: Cooley I 

PROVIDER: S-EPMC11549324 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature