Python 3.10.2 was used to write and execute all underlying code. A variety of Python libraries were employed to implement the autonomous workflow and support data analysis, visualization, and machine learning tasks. Key libraries include NumPy (1.26.4), pandas (1.4.3), Pytables (3.9.2), and matplotlib (3.5.1) for visualizing results. MPh (1.1.5) was employed to interface with COMSOL Multiphysics for simulating electrochemical reactions and generating training data. Machine learning components, including neural network training, were implemented using Keras/TensorFlow (2.15.0) and scikit-learn (1.1.1). For Bayesian optimization, the GPyOpt (1.2.6) library was used alongside its dependency GPy (1.13.1) for Gaussian process modeling. We note that at the time of writing, support for GPyOpt has been discontinued, and alternative libraries may be considered in future work. Custom libraries developed for this study, hardpotato (1.3.13) and elabAPI (1.1.0), were used to control the potentiostat and manage solution handling , respectively. These custom libraries are publicly available on our GitHub repository: .