To minimize the objective function, which in this case is the mean square error (MSE) of the fit to the CO and H2 specific polarization curves, the covariance matrix adaptation evolutionary strategy (CMA-ES) was employed (shown schematically in Figure ). CMA-ES is a global, derivative-free optimization method that operates by forming a parametric distribution over the solution space. It samples a population of solution candidates iteratively, typically from a multivariate gaussian distribution. This objective function is then evaluated for each candidate set of parameters. After evaluating the objective function for each point in a given generation of solution candidates, the solutions are sorted by the magnitude of the objective function and the distribution parameters (the mean vector and covariance matrix) are updated based on the ranking of objective function values. The goal is to search for the optimum set of parameters, such that the objective function is a minimum. The method stops once the change in error across generations has dropped below a certain tolerance (in this study, a tolerance of 1 × 10 &'' was used, which is the default in CMA-ES algorithm in MATLAB.) It is critical to note that CMA-ES is gradient-free since the objective function used for fitting the Tafel kinetic is non-convex, so there exist many local minima in which a gradient-descent method can get trapped. Notably, previous work has shown that small changes in the fitted Tafel parameters can lead to local minima that approximately fit experimental data. We also note (Section S4.1 of the SI) that using a gradient-descent method (e.g., MATLAB's fmincon) in lieu of the CMA-ES method ultimately fails to fit experimental polarization data (Section S10 of the SI).