where kcalc is the rate constant calculated using equation 5, and kreal is the true rate constant. The values of kreal are only known precisely through simulations however, necessitating a predictive model to attain the error-based descriptor for experimental data. We trained a neural-network regressor on simulated CVs to predict the error values described in equation 6 for cases where the catalytic rate constant is not known (i.e., experimental measurements). The neural network employed a multi-layer perceptron architecture with a single hidden layer containing 192 units. This lightweight architecture was chosen for its balance between model complexity and training efficiency. A dropout layer (10% probability) additionally mitigated overfitting. Further details on the simulated training data, model architecture, training, and performance are provided in the Methods section. Mapping the response of the error-based descriptor across the kinetic zone diagram (Figure ) revealed unexpected behavior. KS and KD zones are traditionally thought to be the only kinetic regimes where equation 5 is applicable, however there is a thin region passing through the KG and D zones where the rate constant can be calculated within ± 10% error. This region is not practically useful for kinetic analysis however, as it is an artifact of measuring catalytic current at the switching potential for CVs that do not possess plateau-like behavior (Figure ). This unanticipated region of low error presents complications for autonomous experiments, as optimization becomes more difficult if more local minima exist.