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.