Figure : Illustration of a single iteration of active search in Bayesian optimization (BO) of nanoporous materials. First, we conduct an experiment that measures the property f (x n ) of the material represented by x n . Second, using the new observation, we update our belief about the underlying objective function f (x), reflected by the surrogate model. Third, we use the acquisition function to pick the next material for an experiment. BO is an active search method to find the material x that optimizes the black-box function f (x).