Advancing CH4/H2 separation with covalent organic frameworks by combining molecular simulations and machine learningML scripts are available at https://github.com/gokhanonderaksu/COFS_CH4H2_ML.Electronic supplementary information (ESI) available: R%–APS relations of CoRE COFs; topology distributions among the top CoRE COFs and hypoCOFs; bond and linker type distributions of the top hypoCOFs; schematic representations of the most frequent linker types in the top hypoCOFs; a snapshot showing the adsorption of the gas mixture in the best hypoCOFs; correlation matrix between the structural and chemical properties of the trained hypoCOF set; comparisons of the predicted CH4 and H2 uptakes of trained hypoCOFs by ML models constructed with group A, B, and C descriptors and by simulations; feature importance distributions for group C models; comparisons of predicted and simulated CH4 and H2 uptakes, SCH4/H2, and APSs of unseen hypoCOFs using original and extended ML models. See DOI: https://doi.org/10.1039/d3ta02433d
In: Journal of materials chemistry.A, Jg. 11 (2023), Heft 27, S. 14788-14799
serialPeriodical
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Titel: |
Advancing CH4/H2 separation with covalent organic frameworks by combining molecular simulations and machine learningML scripts are available at https://github.com/gokhanonderaksu/COFS_CH4H2_ML.Electronic supplementary information (ESI) available: R%–APS relations of CoRE COFs; topology distributions among the top CoRE COFs and hypoCOFs; bond and linker type distributions of the top hypoCOFs; schematic representations of the most frequent linker types in the top hypoCOFs; a snapshot showing the adsorption of the gas mixture in the best hypoCOFs; correlation matrix between the structural and chemical properties of the trained hypoCOF set; comparisons of the predicted CH4 and H2 uptakes of trained hypoCOFs by ML models constructed with group A, B, and C descriptors and by simulations; feature importance distributions for group C models; comparisons of predicted and simulated CH4 and H2 uptakes, SCH4/H2, and APSs of unseen hypoCOFs using original and extended ML models. See DOI: https://doi.org/10.1039/d3ta02433d
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Autor/in / Beteiligte Person: | Aksu, Gokhan Onder ; Keskin, Seda |
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Zeitschrift: | Journal of materials chemistry.A, Jg. 11 (2023), Heft 27, S. 14788-14799 |
Veröffentlichung: | 2023 |
Medientyp: | serialPeriodical |
ISSN: | 2050-7488 (print) |
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