Machine Learning-Driven Discovery and Structure–Activity Relationship Analysis of Conductive Metal–Organic Frameworks

Author:

Lin Jinglong1,Zhang Huibao2,Asadi Mojgan3,Zhao Kai1,Yang Luming4,Fan Yunlong1,Zhu Jintao5,Liu Qianyi6,Sun Lei78ORCID,Xie Wen Jun9ORCID,Duan Chenru10,Mo Fanyang1ORCID,Dou Jin-Hu1ORCID

Affiliation:

1. School of Materials Science and Engineering, Peking University, No.5 Yiheyuan Road, Beijing 100871, China

2. School of Electronics Engineering and Computer Science, Peking University, No.5 Yiheyuan Road, Beijing 100871, China

3. Department of Chemistry, University of Southern California, 3551 Trousdale Parkway, Los Angeles, California 90089, United States

4. Research Group EPR Spectroscopy, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, Göttingen 37077, Germany

5. Center for Quantitative Biology, Peking University, No. 5 Yiheyuan Road, Beijing 100871, China

6. Department of Chemistry, University of Chicago, 5735 South Ellis Avenue, Chicago, Illinois 60637, United States

7. Department of Chemistry and Department of Physics, School of Science, Westlake University, 600 Dunyu Road, Hangzhou 310030, China

8. Institute of Natural Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, China

9. Department of Medicinal Chemistry, University of Florida, 1345 Center Drive, Gainesville, Florida 32610, United States

10. Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States

Funder

National Key Research and Development Program of China

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

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