AI is redefining the paradigm of electrochemical research by extending the field into domains beyond the limits of conventional methodologies, while accelerating the discovery–validation cycle to substantially improve research efficiency.

   This AI-driven electrochemical research is structured as a closed-loop autonomous workflow that integrates experiment and computation through the following four stages:

1. Experimental design — Literature- and data-driven hypothesis generation, Bayesian optimization of experimental conditions, and AI-assisted catalyst screening enable the efficient identification of promising catalyst candidates and optimized reaction parameters.

2. Automated experimentation — Robotic-hardware-based automation of electrochemical experiments enables the rapid and reproducible generation of high-quality, large-scale datasets.

3. Automated data analysis — An AI-agent-based automated data processing pipeline enables rapid refinement and analysis of large experimental datasets.

4. Computational simulation — AI-assisted DFT-based mechanism elucidation and key-descriptor exploration identify the factors governing activity and selectivity.