This paper reviews artificial intelligence (AI) applications in the design and optimization of proton exchange membrane (PEM) materials for hydrogen fuel cells. Clean energy conversion is a substantial benefit of PEM fuel cells, which conventional membrane development struggles with due to time-consuming trial-and-error methods, which are not adequate in capturing the different interdependencies of the membrane structure, and environmental variables. The review establishes foundational design principles of PEMs and outlines their challenges and computational methodologies are constructed to address them. Various advanced AI methods have been highlighted which include graph neural networks, multitask frameworks, and physics-informed models that facilitate rapid prediction of polymer properties. Optimization methods have been reported with 10–30% performance improvements, for instance, NSGA-II frameworks achieving 13–27% gains in power density. Experimental requirements are reduced by 40–60%, as seen with Bayesian optimization, identifying optimal designs within as few as 40 iterations. Current challenges include data availability, generalizability, and scalability, which are closely assessed in this review.
Nasser et al. (Tue,) studied this question.