Machine Learning for Prediction and Synthesis of Anion Exchange Membranes

Abstract

Anion exchange membrane fuel cells (AEMFCs) and water electrolyzers (AEMWEs) play a crucial role in the utilization and production of hydrogen energy, offering significant potential for widespread application due to their high energy conversion efficiency and cost-effectiveness. Anion exchange membranes (AEMs) serve the dual purpose of gas isolation and the conduction of OH^– ions. However, the poor chemical stability, low ionic conductivity, and inadequate dimensional stability of AEMs hinder the development of AEM-based energy devices. AEMs exhibit a more intricate chemical structure than general polymers, primarily due to their complex composition and unique attributes. This complexity is attributed to varying chain lengths, degrees of branching, and copolymerization compositions. Furthermore, diverse ion types, ion distribution, ion exchange capacity, hydrophilic clusters, electrostatic interactions, and microphase morphology further complicate these characteristics. In the past decade, more than 5,000 references have been dedicated to obtaining high-performance AEMs. Despite the large amount of work conducted during this period, the performance of AEMs still falls short of meeting the actual needs. The trial-and-error method used in designing membrane structures has proven inefficient and costly. Machine learning, a data-driven computational method, leverages existing data and algorithms to predict yet-to-be-discovered properties of materials. Recently, our group and some researchers have utilized machine learning to expedite the process of material discovery and achieve accurate synthesis of high-performance AEMs.