Machine learning-driven discovery of optimal designs for water electrolysis devices.

Abstract

Green hydrogen production via water electrolysis is key to decarbonizing energy systems, but current electrolyzer designs are energy-intensive due to gas bubble entrapment and manual optimization. This study introduces a machine learning (ML) strategy that autonomously designs high-efficiency electrolyzer flow channels. We identify an array-type channel geometry that enhances bubble removal using a mixture-of-experts framework, with a parametric analysis establishing structure-performance relationships. A prototype electrolyzer incorporating the artificial intelligence-optimized channel demonstrated ~23% improvement in current density at 2 V compared to a conventional serpentine design. This enhancement was consistently in scaled-up devices, underscoring the effectiveness of the design across scales. In contrast to computational fluid dynamics-based approaches simulating every geometry, our ML surrogate performs data-driven screening to efficiently identify and validate high-performance channel structures. By decoding the relationships between topological features and multiphase transport, this work outlines a scalable pathway toward autonomous design of next-generation electrochemical systems.