In this work, we present an autonomous workflow (Figure ) for unsupervised kinetic measurements of molecular electrocatalysis. The workflow optimizes solution conditions and electrochemical parameters to obtain kinetically insightful CVs. To achieve this, we combine our previously developed platform for automated electrocatalysis studies, the eLab , with a Bayesian optimization (BO) algorithm to enable fully autonomous and close-loop investigations of molecular electrocatalysis. To facilitate this, we took inspiration from traditional conceptual frameworks in molecular electrocatalysis theory to develop effective CV descriptors. We use a simulated replica of our autonomous platform-a "digital twin"-to assess the performance of our CV descriptors in the BOdriven autonomous workflow. We experimentally demonstrated our autonomous workflow for TEMPO catalyzed alcohol electro-oxidation, comparing the BO guided experiments to a random sampling strategy and evaluating the effects that reaction kinetics have on BO performance.