ArticleCommunications chemistry2024
Machine learning insights into predicting biogas separation in metal-organic frameworks.
Article in Communications chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Integrated Machine Learning with Molecular Simulation for Screening MOF Adsorbents toward High-Performance Separation of COACS omega · 2026Article
- TAMOF-1 for capture and separation of the main flue gas components.Journal of materials chemistry. A · 2025Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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). 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. The models show excellent performance in predicting gas uptake and classifying MOFs according to the trade-off between gas uptake and selectivity, with R
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Registered trials
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