In addition to efficiently searching for NPMs with optimal properties, BO is applicable to a wide variety of optimization problems in the chemical and materials sciences . BO has been used to efficiently search for optimal reaction conditions [ , ], compositions of and processing conditions for materials The effectiveness of BO is predicted upon an accurate surrogate model of the structure-property relationship. In turn, the accuracy of the surrogate model is predicated on (i) an information-rich representation x of the NPM that encodes the salient features of its structure and chemical composition and (ii) a statistical model that (a) is sufficiently flexible/expressive to approximate the underlying objective function and (b) learns in a data-efficient manner. This gives important and currently active directions for future research. Particularly, engineering useful vector representations x of NPMs, using domain knowledge, is a very active research area . The representation should be invariant to rotations, translations, replications (if a crystal), and permutations of the list of atoms comprising the structure. Moreover, the mapping from NPM structures to feature vectors should be injective. Graph neural networks can, instead, learn vector representations of NPMs from their crystal structures represented as graphs with node labels.