From Optimization to Mechanism: Bayesian Optimization-Guided Exploration of p-Xylene Electro-oxidation

Electrochemical synthesis, with its mild conditions and high atom economy, offers a sustainable alternative for creating high-value products. However, for electrochemical reactions, commonly involving a larger parameter space than traditional chemical reactions, it is challenging to efficiently identify optimal reaction conditions or investigate the underlying principles. In this article, a two-step framework of AI-guided exploration is proposed for investigating the Co(II)-mediated electro-oxidation of p-xylene. A multiobjective diversity-driven Bayesian optimization approach is applied for the first step to search the space of operation, identifying Pareto-optimal solutions that balance maximizing reaction yield and minimizing Faradaic contribution. These solutions are then analyzed by employing SHAP (SHapley Additive Explanations) and additional experiments in the second step to investigate the reaction mechanisms. The optimized conditions achieved higher yields and lower energy consumption compared to reported studies in the literature while avoiding the use of toxic solvents. The framework demonstrates the advantage of AI-guided optimization to accelerate the research of electrochemical reactions by significantly reducing the cost of experiments and reducing reagent consumption as well as waste generation, presenting a new path for exploring sustainable chemical processes.