BO is a direct application of Bayes Theorem, and consists of two main components: the construction of a surrogate model to approximate the black-box function f to be optimized, and an acquisition function for deciding the next samples to evaluate. Pseudo-code for the BO algorithm used can be found in the ESI; for more details about the foundations and/or applications, we refer to references. Surrogate model