Self-optimizing flow reactors have received significant attention in recent years, with Bayesian optimization (BO) being identified as the most effective method for reaction optimization. However, there are many different approaches using BO algorithms, which is overwhelming for experimentalists. Here, using pharmaceutically relevant amide coupling reactions, we explore “best practices” in three areas, to promote the efficient design of sustainable processes: (1) A high extent of exploration in an optimization algorithm was deemed necessary to ensure a good design space overview. (2) Yield was optimized within a small experimental budget, while minimizing environmental impact, by setting up an objective function with penalties (e.g., for excess reagent usage). (3) An optimization algorithm using an auxiliary data set appeared to behave well for the same substrates using a different coupling reagent, but provided no advantage when using substrates with substantially lower reactivity. We envisage that these general recommendations will aid flow chemists utilizing BO for automated development of sustainable reactions.

中文翻译：

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可持续发展的自优化流动反应：实验贝叶斯优化研究


近年来，自优化流动反应器受到了极大的关注，贝叶斯优化（BO）被认为是最有效的反应优化方法。然而，使用 BO 算法的方法有很多种，这让实验者不知所措。在这里，我们利用药学相关的酰胺偶联反应，在三个领域探索“最佳实践”，以促进可持续工艺的高效设计：（1）对优化算法进行高度探索被认为有必要确保良好的设计空间概览。 (2) 通过建立带有惩罚的目标函数（例如，过量试剂使用），在较小的实验预算内优化产量，同时最大限度地减少对环境的影响。 (3) 使用辅助数据集的优化算法对于使用不同偶联试剂的相同底物似乎表现良好，但在使用反应性明显较低的底物时没有提供任何优势。我们预计这些一般性建议将帮助流动化学家利用 BO 来自动开发可持续反应。