30 years of AI for electrocatalysis: Where we are and what’s next?

Artificial Intelligence (AI) has evolved over the past three decades from the initial pioneering stage to become a transformative force in electrocatalytic research yet is far from realizing its full potential. This review traces foundational applications of AI to electrocatalysis in the 1990s to highlight the integration of AI into the full catalyst development workflow in the last five years, from material design and synthesis to characterization and performance evaluation, and ultimately to knowledge extraction. Emphasis is placed on critical but often partially recognized or neglected bottlenecks: the scale gap between atomistic simulations and macroscopic performance, inverse electrocatalyst design, physical consistency and interpretability of machine learning models, automated experiments, and the scarcity of high-quality, well validated experimental data. Cutting edge solutions such as exascale computing, machine learning interatomic potentials (MLIPs), physics-informed machine learning (PIML), generative models (variational autoencoders, diffusion models, and large language models), and FAIR-compliant data are discussed. This review highlights that the progress of AI for electrocatalysis is inherently data-centric, driven by advances in data-quality, FAIR-compliant infrastructure, and data-driven workflows that connect experiment, simulations, and machine learning. Beyond technical perspectives, this review also emphasizes the importance of interdisciplinary collaboration, industrial relevance, and cautions in respect of hyping. By identifying challenges and highlighting emerging breakthroughs, this work offers a roadmap for advancing AI-driven electrocatalysis towards more predictive, interpretable, and scalable discovery. • Traces 30 years of AI development and integration in electrocatalysis. • Identify five bottlenecks limiting AI-enabled catalyst discovery. • Discusses MLIPs, PIML, and generative AI for design and mechanistic insights. • Highlights data-centric progress driven by accessible and consistent data. • Calls for interdisciplinary, industrial, and responsible AI integration.