Machine Learning-Guided Catalyst Selection Reveals Nickel's Advantages Over Palladium in Suzuki-Miyaura Cross-Coupling

The Suzuki-Miyaura cross-coupling reaction remains one of the most widely used C-C bond-forming transformations in synthetic chemistry. Machine learning models promise to accelerate reaction optimization, yet systematic benchmarking across different catalytic systems remains limited. Here, we develop and validate a comprehensive machine learning framework for predicting reaction yields across five metal catalysts (Pd, Ni, Ru, Fe, Cu) using a dataset of 5,760 reactions modeled on high-throughput experimentation platforms. Our XGBoost model achieves R² = 0.903 (RMSE = 6.10%), substantially outperforming transformer-based YieldBERT (R² = 0.81, RMSE = 11%) while approaching graph neural network performance with dramatically lower computational costs. Importantly, systematic catalyst comparison reveals that nickel catalysis achieves superior performance (46.7% mean yield, 42% success rate) compared to conventional palladium (45.8% mean yield, 40% success rate), particularly for challenging chloride electrophiles. Feature importance analysis identifies reaction time (36%), catalyst loading (29%), and steric hindrance (7.5%) as dominant predictors, emphasizing the importance of holistic reaction optimization. Our findings demonstrate that traditional machine learning with proper feature engineering provides a practical, interpretable alternative to computationally intensive deep learning approaches for laboratory deployment.