Understanding reaction mechanisms can help to optimize efficiency, which allows industrially important molecules to be synthesized less expensively and more sustainably. 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 “fingerprints” are created from the yields of a set of substrates subjected to several different reaction conditions. The resultant fingerprints 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, phenol cross coupling, and amination catalysts. We determined that our method successfully categorizes elimination reaction mechanisms, as well as C–H activation reaction mechanisms between eCMD, CMD, and SEAr. Additionally, we found that due to the swift progress and discovery of new mechanisms in the C–H activation field, much of the C–H activation literature contains outdated mechanistic labels. Our method was able to correct these labels, and can be used to quickly hypothesize a mechanism for new C–H activation or amination catalysts with only 4-6 experiments. We also predicted potential similarities in phenol oxidative cross coupling catalysts that are mechanistically not well understood. Overall, this method has the potential to simplify the study of reaction mechanism for many types of reactions.
