AI-Assisted Physics-Informed Predictions of Degradation Behavior of Polymeric Anion Exchange Membranes
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 h) from minimal early time experimental data. This capability significantly reduces experimental burdens and may expedite the design of high-performance, durable AEM materials.
Fields of Study & Subjects
Fuel Cells and Related Materials
Advanced battery technologies research
Electrocatalysts for Energy Conversion
Publication details
- Journal / source
- The Journal of Physical Chemistry B
- Publisher
- American Chemical Society (ACS)
- Volume
- 130
- Issue
- 5
- Pages
- 1684–1693
- Publication date
- 27 January 2026
- Language
- EN