Staged Diversity‐Constrained Machine Learning for High‐Dimensional Reaction Condition Optimization

Optimizing reaction conditions in high‐dimensional chemical spaces remains a central challenge in modern synthesis. In this context, we developed and evaluated a staged diversity‐constrained machine learning framework that efficiently balances exploration and exploitation during condition optimization. At each stage, a within‐batch diversity constraint promotes broad chemical coverage, while the constraint is progressively relaxed to focus on promising subspaces. Systematic evaluation across large‐scale palladium‐catalyzed C─C and C─N coupling datasets revealed that the number of stages, rather than the exploration portion, was the dominant factor governing optimization efficiency. A comparison with Bayesian optimization (BO) methods shows a dimension‐dependent performance trend. Here, the staged diversity‐constrained strategy was shown to be more advantageous in higher‐dimensional reaction spaces, whereas BO performed better in lower‐dimensional settings. Moreover, we developed a user‐friendly software tool making the herein developed framework readily accessible for experimental chemists. Our strategy was further applied to challenging ruthenium‐catalyzed meta ‐C─H functionalization involving 11,880 possible conditions, only 44 experiments were required to identify the optimal setup (91% yield). This work provides a validated and practical framework for accelerating high‐dimensional reaction condition optimization, bridging data‐driven modeling with experimental synthesis.