Reaction optimization is fundamental to synthetic chemistry, from optimizing the yield of industrial processes to selecting conditions for the preparation of medicinal candidates¹. Likewise, parameter optimization is omnipresent in artificial intelligence, from tuning virtual personal assistants to training social media and product recommendation systems². Owing to the high cost associated with carrying out experiments, scientists in both areas set numerous (hyper)parameter values by evaluating only a small subset of the possible configurations. Bayesian optimization, an iterative response surface-based global optimization algorithm, has demonstrated exceptional performance in the tuning of machine learning models³. Bayesian optimization has also been recently applied in chemistry^4,5,6,7,8,9; however, its application and assessment for reaction optimization in synthetic chemistry has not been investigated. Here we report the development of a framework for Bayesian reaction optimization and an open-source software tool that allows chemists to easily integrate state-of-the-art optimization algorithms into their everyday laboratory practices. We collect a large benchmark dataset for a palladium-catalysed direct arylation reaction, perform a systematic study of Bayesian optimization compared to human decision-making in reaction optimization, and apply Bayesian optimization to two real-world optimization efforts (Mitsunobu and deoxyfluorination reactions). Benchmarking is accomplished via an online game that links the decisions made by expert chemists and engineers to real experiments run in the laboratory. Our findings demonstrate that Bayesian optimization outperforms human decisionmaking in both average optimization efficiency (number of experiments) and consistency (variance of outcome against initially available data). Overall, our studies suggest that adopting Bayesian optimization methods into everyday laboratory practices could facilitate more efficient synthesis of functional chemicals by enabling better-informed, data-driven decisions about which experiments to run.

反应优化是合成化学的基础，从优化工业过程的产量到选择制备候选药物¹的条件。同样，参数优化在人工智能中无处不在，从调整虚拟个人助理到训练社交媒体和产品推荐系统²。由于与进行实验相关的高成本，这两个领域的科学家通过仅评估可能配置的一小部分来设置许多（超）参数值。贝叶斯优化是一种基于迭代响应面的全局优化算法，在机器学习模型的调优中表现出卓越的性能³. 贝叶斯优化最近也被应用于化学^4,5,6,7,8,9; 然而，尚未研究其在合成化学中反应优化的应用和评估。在这里，我们报告了贝叶斯反应优化框架和开源软件工具的开发，该工具使化学家可以轻松地将最先进的优化算法集成到他们的日常实验室实践中。我们为钯催化的直接芳基化反应收集了一个大型基准数据集，对贝叶斯优化与反应优化中的人类决策进行了系统研究，并将贝叶斯优化应用于两个现实世界的优化工作（Mitsunobu 和脱氧氟化反应）。基准测试是通过一个在线游戏完成的，该游戏将专业化学家和工程师做出的决定与实验室中运行的真实实验联系起来。我们的研究结果表明，贝叶斯优化在平均优化效率（实验次数）和一致性（结果与初始可用数据的差异）方面都优于人类决策。总体而言，我们的研究表明，在日常实验室实践中采用贝叶斯优化方法可以促进更有效地合成功能性化学物质，从而实现关于运行哪些实验的更明智、数据驱动的决策。