MOF-GRU: A MOFid-Aided Deep Learning Model for Predicting the Gas Separation Performance of Metal-Organic Frameworks.

Li, Wenxuan; Situ, Yizhen; Ding, Lifeng; Chen, Yanling; Yang, Qingyuan

Li, Wenxuan; Situ, Yizhen; Ding, Lifeng; Chen, Yanling; Yang, Qingyuan.

Afiliación

Li W; State Key Laboratory of Organic-Inorganic Composites, College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, China.
Situ Y; State Key Laboratory of Organic-Inorganic Composites, College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, China.
Ding L; Department of Chemistry, School of Science, Xi'an Jiaotong-Liverpool University, Suzhou 215123, Jiangsu, China.
Chen Y; State Key Laboratory of Organic-Inorganic Composites, College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, China.
Yang Q; State Key Laboratory of Organic-Inorganic Composites, College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, China.

ACS Appl Mater Interfaces ; 15(51): 59887-59894, 2023 Dec 27.

Article en En | MEDLINE | ID: mdl-38087435

Palabras clave

CH4/N2 separation; MOF-GRU model; deep learning; metal−organic frameworks; molecular simulation

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Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: ACS Appl Mater Interfaces Asunto de la revista: BIOTECNOLOGIA / ENGENHARIA BIOMEDICA Año: 2023 Tipo del documento: Article

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