Autonomous process optimization involves the human intervention-free exploration of a range process parameters to improve responses such as product yield and selectivity. Utilizing off-the-shelf components, we develop a closed-loop system for carrying out parallel autonomous process optimization experiments in batch. Upon implementation of our system in the optimization of a stereoselective Suzuki-Miyaura coupling, we find that the definition of a set of meaningful, broad, and unbiased process parameters is the most critical aspect of successful optimization. Importantly, we discern that phosphine ligand, a categorical parameter, is vital to determination of the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing bias into the experimental design. In seeking a systematic method for selecting a diverse set of phosphine ligands, we develop a strategy that leverages computed molecular feature clustering. The resulting optimization uncovers conditions to selectively access the desired product isomer in high yield.

自主过程优化涉及对一系列过程参数进行无人工干预的探索，以提高产品产量和选择性等响应。利用现成的组件，我们开发了一个闭环系统，用于批量进行并行自主过程优化实验。在优化立体选择性 Suzuki-Miyaura 耦合中实施我们的系统后，我们发现一组有意义、广泛且无偏差的工艺参数的定义是成功优化的最关键方面。重要的是，我们发现膦配体是一个分类参数，对于确定反应结果至关重要。迄今为止，分类参数选择依赖于化学直觉，可能会在实验设计中引入偏差。在寻求一种系统的方法来选择一组不同的膦配体时，我们开发了一种利用计算分子特征聚类的策略。由此产生的优化揭示了以高产率选择性地获得所需产物异构体的条件。