Machine Learning‐Aided Design of Highly Conductive Anion Exchange Membranes for Fuel Cells and Water Electrolyzers

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.