A supervised machine learning model can serve as a cheaper, albeit lower fidelity, surrogate for the high-fidelity experiment [ , -], thereby reducing the cost of an exhaustive search. A machine learning approach is predicated upon cheaply computed (relative to the experiment) (i) vector representations of the NPMs-hand-engineered or learned [ ]that encode structural features and are correlated to the property or (ii) kernels that capture the tendency for any given pair of NPMs to exhibit similar properties [ ]. Training examples are gathered by selecting a small (random or diverse ) subset of the library of NPMs and labeling them with the property values using the high-fidelity experiment. Using the training examples, the supervised machine learning model learns to predict the property of any given NPM from e.g., its vector representation. The trained model is then used, as a surrogate for the high-fidelity experiment, to cheaply predict, from their vector representations, the properties of the remaining NPMs in the library. Further high-fidelity experiments may be directed on the NPMs predicted to be optimal by the machine learning model. See Refs. [ , -] as examples.