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Accelerating the prediction of CO2 capture at low partial pressures in metal-organic frameworks using new machine learning descriptors

Chemistry

Accelerating the prediction of CO2 capture at low partial pressures in metal-organic frameworks using new machine learning descriptors

I. B. Orhan, T. C. Le, et al.

This research conducted by Ibrahim B. Orhan, Tu C. Le, Ravichandar Babarao, and Aaron W. Thornton introduces innovative machine learning techniques to efficiently screen metal-organic frameworks for CO2 capture, significantly optimizing computation time and maintaining effective performance for direct air capture applications.

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Playback language: English
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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