Development of AI-eChemist Laboratory

Abstract

The development of a self-driving laboratory (SDL) is driving electrocatalysis research from traditional trial-and-error approaches toward automation, high throughput, and intelligence. As an autonomous experimental system tailored to electrochemical scenarios, the AI-eChemist Laboratory integrates front-end intelligent decision-making, automated high-throughput experimentation, multimodal characterization, and data-driven analysis, providing a new paradigm for the discovery, mechanistic understanding, and application validation of complex electrocatalytic materials. This review first summarizes recent advances in SDL from two perspectives: front-end intelligence and autonomous experimental platforms. On this basis, we further focus on three key technical routes established in AI-eChemist: high-throughput synthesis and screening of model catalysts, high-throughput synthesis and screening of practical powder catalysts, and emerging screening strategies targeting intrinsic catalytic activity. These routes promote the construction of a closed-loop research system in AI-eChemist, spanning materials screening and mechanistic investigation to device validation, through standardized data acquisition, practical materials discovery, and intrinsic activity evaluation. Finally, in view of the demands of AI-eChemist for practical applications and autonomous development, we discuss future directions including multimodal characterization, automated function islands, scalable fabrication, and multi-agent collaboration, aiming to provide systematic insights for the intelligent discovery and application-oriented translation of advanced energy materials.