Local reaction condition optimization via machine learning

Reaction condition optimization addresses shared requirements across academia and industry, particularly in chemistry, pharmaceutical development, and fine chemical engineering. This review examines recent progress and persistent challenges in machine learning-guided optimization of localized reaction conditions, with an emphasis on three core aspects: dataset, condition representation, and optimization methods, as well as the main issues in each related stage. The review explores challenges such as dataset scarcity, data quality, and the "completeness trap" in dataset preparation stage, summarizes the limitations of current molecular representation techniques in condition representation stage, and discusses the search efficiency challenges of optimization methods in optimization stage.

The review analyzes the molecular representation techniques and identifies them as the primary bottleneck in advancing localized reaction condition optimization. It further examines existing optimization methodologies. Among them, Bayesian optimization and active learning emerges as the most commonly applied approaches in this field, utilizing incremental learning mechanisms and human-in-the-loop strategies to minimize experimental data requirements while mitigating molecular representation limitations. The review concludes that advancements in molecular representation techniques are essential for developing more efficient optimization methods in the future.