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Abstract
Metal-organic frameworks (MOFs) are promising materials for CO2 capture. This paper explores using machine learning (ML) to rapidly screen MOFs for CO2 adsorption at low partial pressures, relevant to direct air capture (DAC). New descriptors, Effective Point Charge (EPoCh), are introduced, significantly reducing computation time compared to traditional methods like Henry coefficient calculations while achieving comparable performance in identifying top CO2 capture candidates.
Publisher
Communications Chemistry
Published On
Oct 03, 2023
Authors
Ibrahim B. Orhan, Tu C. Le, Ravichandar Babarao, Aaron W. Thornton
Tags
Metal-organic frameworks
CO2 capture
machine learning
adsorption
direct air capture
Effective Point Charge
computational efficiency

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