In summary, we have successfully demonstrated an autonomous, closed-loop workflow for kinetic investigations of molecular electrocatalysis using TEMPO-mediated alcohol electro-oxidation as a benchmark system. We leveraged the conceptual framework of the EC' kinetic zone diagram to derive effective CV descriptors, enabling the identification of kinetically insightful current-potential profiles. We evaluated the performance of the Bayesian optimization algorithm using first a digital twin of our autonomous platform built upon finite element method simulations incorporating likely experimental artifacts and constraints. We then combined Bayesian optimization and automated electrochemistry to perform fully autonomous, closed-loop kinetic studies of the TEMPO-alcohol system for ethanol and isopropanol. Our autonomous workflow identified conditions yielding kinetically insightful current-potential profiles in 10 iterations or less, outperforming random sampling, and reliably performing extensive solution handling and electrochemical operations, thus minimizing human involvement and error. This work showcases integration of machine learning and automation with experimental electrochemistry, paving the way for future high-throughput kinetic studies of molecular electrocatalysts across large scopes of catalysts and substrates. In this work we demonstrated BO of a hysteresis-based descriptor which dramatically outperformed random sampling, as well as BO with an error-based descriptor. We acknowledge there could be improvements to the error-based descriptor by improving the underlying neural network model. To this end, we provide the community with a ~25,000 simulated CV training data set to do their own explorations. We note that the methodology developed in this work can be applied to systems beyond molecular electrocatalysis, providing a general workflow to optimize CV experimental parameters for systems in energy conversion, electrosynthesis, bio-electrocatalysis, and more. The flexibility of our autonomous workflow is complemented by the modularity of our eLab and Electrolab 37 platforms, which allows experimental control of temperature, gas sparging, pH, in addition to electrochemical functions beyond CV, thus enabling extremely detailed mechanistic studies without overbearing the user. Additionally, the small footprint of the solutionhandling hardware (5.6"×5"×1.8") enables easy incorporation into more complex setups, such as those controlling inert environments or coupling with spectroscopic modes of analysis. We believe that the integration of machine learning and automation with electrochemistry will accelerate the discovery of new molecules and materials for a greener and more sustainable future.