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arXiv · 2510.12655

AI-Assisted Physics-Informed Predictions of Degradation Behavior of Polymeric Anion Exchange Membranes

William Schertzer·Mohamed Al Otmi·Janani Sampath·Ryan P. Lively·Rampi Ramprasad
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Abstract

The global transition to hydrogen-based energy infrastructures faces significant hurdles. Chief among these are the high costs and sustainability issues associated with acid-based proton exchange membrane fuel cells. Anion exchange membrane (AEM) fuel cells offer promising cost-effective alternatives, yet their widespread adoption is limited by rapid degradation in alkaline environments. Here, we develop a framework that integrates mechanistic insights with machine learning, enabling the identification of generalized degradation behavior across diverse polymeric AEM chemistries and operating conditions. Our model successfully predicts long-term hydroxide conductivity degradation (up to 10,000 hours) from minimal early-time experimental data. This capability significantly reduces experimental burdens and may expedite the design of high-performance, durable AEM materials.

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William Schertzer, Mohamed Al Otmi, Janani Sampath, Ryan P. Lively, Rampi Ramprasad. 2025-10-14. AI-Assisted Physics-Informed Predictions of Degradation Behavior of Polymeric Anion Exchange Membranes. https://arxiv.org/abs/2510.12655

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