AI-driven design of fluorine-free polymers for sustainable and high-performance anion exchange membranes

Abstract

As global demand for clean energy increases, fuel cells have emerged as a key technology for sustainable power generation. Anion exchange membrane (AEM) fuel cells offer a more economical and environmentally friendly alternative to the popular proton exchange membrane (PEM) fuel cells, which rely on fluorinated polymers and also use expensive platinum group catalysts. However, designing high-performance AEMs is challenging because of the need to balance conflicting material properties. In this study, we employ machine learning to accelerate the design of fluorine-free copolymers for AEMs, focusing on known monomer chemistries. By training models on AEM data from the literature, we predicted key properties, namely, hydroxide ion conductivity, water uptake (WU), and swelling ratio (SR). Screening 11 million novel copolymer candidates using predictive models and heuristic filters, we identified more than 400 promising fluorine-free copolymer candidates with predicted OH^- conductivity greater than 100 mS/cm, WU below 35 wt%, and SR below 50%. This computational approach to AEM design could contribute to developing more efficient and sustainable AEM fuel cells for various energy applications.

Keywords

Anion exchange membrane / fuel cells / anion conductivity / water uptake / swelling / materials informatics / high-throughput polymer design

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