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

Wenxuan Li, Yizhen Situ, Lifeng Ding^*, Yanling Chen, Qingyuan Yang^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Original language	English
Pages (from-to)	59887-59894
Journal	ACS Applied Materials and Interfaces
Volume	15
Issue number	51
DOIs	https://doi.org/10.1021/acsami.3c11790
Publication status	Published - Dec 2023

Access to Document

@article{9001c2bcb16b48ec9d6fdd6d22bb455a,

title = "MOF-GRU: A MOFid-Aided Deep Learning Model for Predicting the Gas Separation Performance of Metal–Organic Frameworks",

author = "Wenxuan Li and Yizhen Situ and Lifeng Ding and Yanling Chen and Qingyuan Yang",

year = "2023",

month = dec,

doi = "10.1021/acsami.3c11790",

language = "English",

volume = "15",

pages = "59887--59894",

journal = "ACS Applied Materials and Interfaces",

issn = "1944-8244",

publisher = "American Chemical Society",

number = "51",

}

TY - JOUR

T1 - MOF-GRU: A MOFid-Aided Deep Learning Model for Predicting the Gas Separation Performance of Metal–Organic Frameworks

AU - Li, Wenxuan

AU - Situ, Yizhen

AU - Ding, Lifeng

AU - Chen, Yanling

AU - Yang, Qingyuan

PY - 2023/12

Y1 - 2023/12

U2 - 10.1021/acsami.3c11790

DO - 10.1021/acsami.3c11790

M3 - Article

C2 - 38087435

SN - 1944-8244

VL - 15

SP - 59887

EP - 59894

JO - ACS Applied Materials and Interfaces

JF - ACS Applied Materials and Interfaces

IS - 51

ER -