Machine Learning-Aided Design of Highly Conductive Anion Exchange Membranes for Fuel Cells and Water Electrolyzers
Qiuhuan Zhang1, Yongjiang Yuan1, Jiale Zhang1, Pengda Fang1, Ji Pan1, Hao Zhang1, Tao Zhou1, Qikun Yu1, Xiuyang Zou2（邹修洋）*, Zhe Sun1（孙哲）*, Feng Yan1,3（严锋）*
1Jiangsu Engineering Laboratory of Novel Functional Polymeric Materials, Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Suzhou Key Laboratory of Soft Material and New Energy, College of Chemistry Chemical Engineering and Materials Science, Soochow University, Suzhou 215123, China
2Jiangsu Engineering Research Center for Environmental Functional Materials School of Chemistry and Chemical Engineering, Huaiyin Normal University, Huaian 223300, China
3State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai 201600, China
Adv. Mater. 2024, 36, 2404981
Abstract: Alkaline anion exchange membrane (AEM)-based fuel cells (AEMFCs) and water electrolyzers (AEMWEs) are vital for enabling the efficient and large-scale utilization of hydrogen energy. However, the performance of such energy devices is impeded by the relatively low conductivity of AEMs. The conventional trial-and-error approach to designing membrane structures has proven to be both inefficient and costly. To address this challenge, a fully connected neural network (FCNN) model is developed based on acid-catalyzed AEMs to analyze the relationship between structure and conductivity among 180,000 AEM variations. Under machine learning guidance, anilinium cation-type membranes are designed and synthesized. Molecular dynamics simulations and Mulliken charge population analysis validated that the presence of a large anilinium cation domain is a result of the inductive effect of N+ and benzene rings. The interconnected anilinium cation domains facilitated the formation of a continuous ion transport channel within the AEMs. Additionally, the incorporation of the benzyl electron-withdrawing group heightened the inductive effect, leading to high conductivity AEM variant as screened by the machine learning model. Furthermore, based on the highly active and low-cost monomers given by machine learning, the large-scale synthesis of anilinium-based AEMs confirms the potential for commercial applications.
链接：//onlinelibrary.wiley.com/doi/10.1002/adma.202404981
