Bayesian Optimization for Chemical Synthesis in the Era of Artificial Intelligence: Advances and Applications

This review highlights recent advances in the application of Bayesian optimization to chemical synthesis. In the era of artificial intelligence, Bayesian optimization has emerged as a powerful machine learning approach that transforms reaction engineering by enabling efficient and cost-effective optimization of complex reaction systems. We begin with a concise overview of the theoretical foundations of Bayesian optimization, emphasizing key components such as Gaussian process-based surrogate models and acquisition functions that balance exploration and exploitation. Subsequently, we examine its practical applications across various chemical synthesis contexts, including reaction parameter tuning, catalyst screening, molecular design, synthetic route planning, self-optimizing systems, and autonomous laboratories. In addition, we discuss the integration of emerging techniques, such as noise-robust methods, multi-task learning, transfer learning, and multi-fidelity modeling, which enhance the versatility of Bayesian optimization in addressing the challenges and limitations inherent in chemical synthesis.