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Hajiali, Faezeh; Ellis, Naoko; Gopaluni, Bhushan (2025) From biomass waste to CO2 capture: a multi-fidelity machine learning workflow for high-throughput screening of activated carbons. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01786-0

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Reference TypeJournal (article/letter/editorial)
TitleFrom biomass waste to CO2 capture: a multi-fidelity machine learning workflow for high-throughput screening of activated carbons
Journalnpj Computational Materials
AuthorsHajiali, FaezehAuthor
Ellis, NaokoAuthor
Gopaluni, BhushanAuthor
Year2025 (November 24)Volume11
Issue1
PublisherSpringer Science and Business Media LLC
DOIdoi:10.1038/s41524-025-01786-0Search in ResearchGate
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Mindat Ref. ID19272041Long-form Identifiermindat:1:5:19272041:4
GUID0
Full ReferenceHajiali, Faezeh; Ellis, Naoko; Gopaluni, Bhushan (2025) From biomass waste to CO2 capture: a multi-fidelity machine learning workflow for high-throughput screening of activated carbons. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01786-0
Plain TextHajiali, Faezeh; Ellis, Naoko; Gopaluni, Bhushan (2025) From biomass waste to CO2 capture: a multi-fidelity machine learning workflow for high-throughput screening of activated carbons. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01786-0
In(2025, January) npj Computational Materials Vol. 11 (1). Springer Science and Business Media LLC

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