Self-Reconstruction of Fe-Doped Co-Metal-Organic Frameworks Boosted Electrocatalytic Performance for Oxygen Evolution Reaction
Zou Xiuyang1, Xu Guodong1, Fang Pengda1, Li Weizheng1, Jin Zhiyu1, Guo Siyu1, Hu Yin1, Li Meisheng2, Pan Ji1, Sun Zhe1*(孙哲), Yan Feng1*(严锋)
1Jiangsu Engineering Laboratory of Novel Functional Polymeric Materials, Jiangsu Key Laboratory of Advanced Negative Carbon Technologies College of Chemistry, Suzhou Key Laboratory of Soft Material and New Energy, College of Chemistry, Chemical Engineering and Materials Science, Soochow University, Suzhou 215123 (China)
2School of Chemistry and Chemical Engineering, Jiangsu Key Laboratory for Chemistry of Low-Dimensional Materials, Huaiyin Normal University Huaian 223300 (China)
Angew. Chem. Int. Ed. 2023, 62, e202300388
Abstract：Without insight into the correlation between the structure and properties, anion exchange membranes (AEMs) for fuel cells are developed usually using the empirical trial and error method or simulation methods. Here, a virtual module compound enumeration screening (V-MCES) approach, which does not require the establishment of expensive training databases and can search the chemical space containing more than 4.2x10(5) candidates was proposed. The accuracy of the V-MCES model was considerably improved when the model was combined with supervised learning for the feature selection of molecular descriptors. Techniques from V-MCES, correlating the molecular structures of the AEMs with the predicted chemical stability, generated a ranking list of potential high stability AEMs. Under the guidance of V-MCES, highly stable AEMs were synthesized. With understanding of AEM structure and performance by machine learning, AEM science may enter a new era of unprecedented levels of architectural design.
链接：//onlinelibrary.wiley.com/doi/10.1002/anie.202300388
