AI in Membrane Design and Optimization for Hydrogen Fuel Cells

TL;DR

This paper reviews how AI can improve the design of proton exchange membranes for hydrogen fuel cells, making the process faster and more efficient.

Contribution

The paper introduces AI methods like graph neural networks and Bayesian optimization to accelerate membrane design and optimization.

Findings

AI methods like NSGA-II improve membrane performance by 13–27% in power density.

Bayesian optimization reduces experimental requirements by 40–60%.

Current challenges include data scarcity and model generalizability.

Abstract

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.

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