Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal–Organic Framework Space

Author:

Xin Ruiqi12,Wang Chaohai2,Zhang Yingchao3,Peng Rongfu2,Li Rui24,Wang Junning2,Mao Yanli2ORCID,Zhu Xinfeng2,Zhu Wenkai1,Kim Minjun5ORCID,Nam Ho Ngoc6ORCID,Yamauchi Yusuke567ORCID

Affiliation:

1. College of Chemistry and Materials Engineering, Zhejiang A&F University, Hangzhou 311300, China

2. Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, Henan International Joint Laboratory for Green Low Carbon−Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan 467036, China

3. School of Civil Engineering and Transportation, North China University of Water Resources and Electric Power, Zhengzhou 450000, China

4. College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China

5. School of Chemical Engineering and Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, Queensland 4072, Australia

6. Department of Materials Process Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan

7. Department of Plant and Environmental New Resources, College of Life Sciences, Kyung Hee University, Gyeonggi-do, 17104, South Korea

Funder

Nagoya University

National Key Research and Development Program of China

Scientific Research Foundation of Zhejiang A and F University

Exploratory Research for Advanced Technology

China Postdoctoral Science Foundation

Natural Science Foundation of Jiangsu Province

International Centre for Bamboo and Rattan

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

Central Plain's Leading Scientific and Technological

Publisher

American Chemical Society (ACS)

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