Revealing Hydroxide Ion Transport Mechanisms in Commercial Anion-Exchange Membranes at Nano-Scale from Machine-learned Interatomic Potential Simulations

TL;DR

This study uses machine-learned interatomic potentials in large-scale molecular dynamics simulations to uncover how hydroxide ions move within commercial anion-exchange membranes, linking nano-scale structure to macroscopic transport properties.

Contribution

It introduces a simulation framework that combines machine learning and molecular dynamics to reveal hydroxide transport mechanisms at the atomic level in membranes.

Findings

Water content enhances hydroxide mobility by forming hydrogen-bond networks.

Dry conditions trap hydroxide ions, hindering transport.

Simulated diffusion coefficients match experimental data.

Abstract

Hydroxide ion transport in anion-exchange membranes fundamentally limits the efficiency of alkaline water electrolysis for green hydrogen production, yet the atomic-scale transport mechanisms remain poorly understood due to the computational challenges associated with modeling ion dynamics. Given that anion-exchange membranes enable alkaline electrolysis with abundant catalysts while avoiding perfluoroalkyl and polyfluoroalkyl materials, a deeper mechanistic understanding of hydroxide transport in these systems is essential for advancing sustainable hydrogen production. Here, we show that large-scale molecular dynamics simulations with fine-tuned machine-learned interatomic potentials provide atomistic insight into hydroxide mobility in a commercial membrane over tens of nanoseconds and over ten nanometer. We find that increasing water content transforms isolated water clusters into a connected hydrogen-bond network that enables long-range proton transfer. Under dry conditions hydroxide ions are trapped near positively charged groups and transport is strongly hindered, whereas well-hydrated membranes exhibit extended proton migration and diffusion coefficients approaching those of dilute aqueous solutions. The simulations reproduce experimental trends in diffusion and activation energies. Our results establish a direct link between nano-scale structure and macroscopic transport. Beyond mechanistic insight, the presented simulation framework enables predictive, simulation-guided optimization of membrane chemistry and architecture, opening a pathway toward the rational design of more efficient anion-exchange membranes for green hydrogen technologies.