Opportunities and perspectives of artificial intelligence in electrocatalysts design for water electrolysis

• A comprehensive AI-driven multiscale design framework was synthesized for electrocatalyst design. • A cross-scale, data-driven algorithm strategy integrates AI models based on domain-specific features. • Generative AI and automation experimental techniques enable a shift toward autonomous electrocatalyst discovery. As a key pathway for green hydrogen production, water electrolysis is expected to play a central role in the future energy landscape. However, its large-scale deployment is hindered by challenges related to cost, performance, and durability. The emergence of artificial intelligence (AI) has transformed this field by offering powerful and efficient tools for the design and optimization of electrocatalysts. This review outlines an AI-driven multiscale design framework, highlighting its role at the microscopic scale for identifying atomic-level active sites and key descriptors, at the mesoscopic scale for structural and morphological characterization, and at the macroscopic scale for multi-objective optimization and intelligent control. This multiscale framework demonstrates the potential of AI to accelerate the development of next-generation electrocatalysts. In addition, the integration of generative AI and automated experimental techniques is highlighted as promising strategies to further enhance electrocatalyst discovery and promote the practical implementation of water electrolysis technologies.