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

## TL;DR

This paper introduces a machine learning framework that predicts how polymeric membranes degrade over time in fuel cells, helping to design more durable materials.

## Contribution

The novel integration of mechanistic insights with machine learning to predict degradation behavior of anion exchange membranes.

## Key findings

- The model predicts long-term degradation of hydroxide conductivity in AEMs using minimal early data.
- The framework reduces the need for extensive experimental testing of membrane materials.
- It enables generalized predictions across diverse polymeric chemistries and conditions.

## 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.