Using Yield Profiles to Study Reaction Mechanism

Understanding reaction mechanisms can help to optimize efficiency, which allows industrially important molecules to be synthesized at a lower cost. Determining mechanisms, however, can be time-consuming and resource-intensive. In this work, we outline an expeditious method to differentiate reaction mechanisms by means of unsupervised learning. With this technique, reaction “profiles” are created from the yields of a set of substrates subjected to several different reaction conditions. The resultant profiles are clustered by similarity to reveal mechanistic relationships. We have benchmarked this method with elimination reaction mechanisms and used it to explore mechanistic relationships among C–H activation and phenol cross-coupling catalysts. We determined that our method successfully categorizes elimination reaction mechanisms. Additionally, we found that our method was able to correct mechanistic assignments in C–H activation chemistry, previously misassigned due to the swift progress and discovery of previously unknown mechanisms in the field. Our method can be used to quickly hypothesize a mechanism for C–H activation catalysts with only four experiments. We also predicted potential similarities in phenol oxidative cross-coupling catalysts that are mechanistically not well understood. Overall, this method provides a tool that can complement existing methods to study reaction mechanisms.