From Experimental Optimization to AI Empowerment: Advanced Strategies of Oxygen Evolution Reaction for Large‐Scale PEMWE

Proton exchange membrane water electrolysis (PEMWE) has emerged as a core technology for green hydrogen production owing to its fast dynamic response and efficient compatibility with intermittent renewable energy sources. It is now transitioning from megawatt- to gigawatt-scale applications, yet still faces many practical challenges. The oxygen evolution reaction at the anode is a key breakthrough point for enhancing the overall performance of PEMWE. In recent years, significant progress has been made in this field via traditional experimental optimization, while artificial intelligence has also become a major driver of technological paradigm shifts. This paper first systematically reviews the core challenges of PEMWE's large-scale application. Then, it delves into the optimization strategies for the anode reaction of PEMWE from three dimensions, establishing a strategy system that covers traditional experimental optimization to artificial intelligence (AI) assisted driving. Finally, future development directions toward scaled-up application are prospected. This paper aims to bridge traditional experimental optimization methods and AI-enabled approaches, accelerating the large-scale application of PEMWE and promoting the high-quality development of the green hydrogen industry.