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...

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Bibliographic Details
Main Authors: Isabel Cooley, Samuel Boobier, Jonathan D. Hirst, Elena Besley
Format: Article
Language:English
Published: Nature Portfolio 2024-05-01
Series:Communications Chemistry
Online Access:https://doi.org/10.1038/s42004-024-01166-7
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