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

Chemistry

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

I. Cooley, S. Boobier, et al.

This groundbreaking research by Isabel Cooley, Samuel Boobier, Jonathan D. Hirst, and Elena Besley leverages machine learning to revolutionize biogas fuel efficiency through enhanced separation of carbon dioxide and methane. Discover how carefully curated data from Monte Carlo simulations can drive innovation in metal-organic frameworks, achieving remarkable accuracy in gas uptake predictions.

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