AI for Electrocatalytic Energy Conversion: From Atoms to Industry
Achieving carbon neutralization relies heavily on green hydrogen and electrochemical carbon-nitrogen cycles. However, the complexity of these systems and the cost of traditional Edisonian trial-and-error methods hinder rapid progress. Artificial intelligence (AI) has emerged as a transformative tool, enabling high-throughput data processing and dynamic adaptation. This review surveys the landscape of AI-driven electrochemistry, bridging the gap from atomic-scale design to industrial-scale implementation. Specifically, we focus on three areas: atomic structure-function decoding, fully automated “self-driving” laboratories, and macro-scale simulations for device durability. Furthermore, we elucidate the critical challenges in integrating AI with materials science. By mapping current trends and future directions, this work aims to unlock the full transformative potential of AI in next-generation energy storage and conversion.