DataVXChemistry of MaterialsPaper
journal article·2024-06-11

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

Jinglong Lin, Huibao Zhang, Mojgan Asadi, Kai Zhao, Luming Yang, Yunlong Fan, Jintao Zhu, Qianyi Liu, Lei Sun, Wen Jun Xie, Chenru Duan, Fanyang Mo, Jin-Hu Dou

Chemistry of Materials· Volume 36· Issue 11
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Source abstract

Abstract

Electrically conductive metal–organic frameworks (MOFs) are a class of materials with emergent applications in fields such as electrocatalysis, electrochemical energy storage, and chemiresistive sensors due to their unique combination of porosity and conductivity. However, due to the structural complexity and versatility, rational design of conductive MOFs is still challenging, which limits their further development and applications. To overcome this limitation, we established a database of 224 conductive MOFs, covering all of the reported conductive MOFs as far as we know, and utilized a combination of machine learning (ML) models and density functional theory (DFT) calculations to develop structure–conductivity relationship models. The interpretability of the models provided guidelines for the design of these materials and allowed us to identify new conductive MOFs through rapid screening. Subsequent experiments confirmed the model’s reliability and viability by synthesizing and validating a conductive MOF, CuTTPD, selected via the ML screening. Our results demonstrate that ML models are powerful tools for prescreening new conductive MOFs, thereby accelerating the development of this field.
Source: openalex_inverted_index · Quality: baseline

Research context

Catalysis & surfaces

OpenAlex topics: Metal-Organic Frameworks: Synthesis and Applications · Machine Learning in Materials Science · Electrochemical Analysis and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

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

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

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

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

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

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

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

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

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

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

Funding

National Key Research and Development Program of China · Fundamental Research Funds for the Central Universities · National Natural Science Foundation of China

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