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.