Here we provide a short overview of Bayesian optimization and refer readers to the SI and extensive literature for more detailed accounts . BO involves two main components, namely a surrogate model that approximates the target function and an acquisition function that provides a data collection policy. During a BO iteration, the surrogate model is fit to the current data set using Gaussian process regression. The posterior mean of the Gaussian process represents the most probable approximation of the target, and the posterior variance provides a measure of the model uncertainty.