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

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

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.

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

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

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