One of the advantages of our model is its relatively low training data requirement. We achieve this by not using the training structures as a whole, but instead we extract every atom-centered local environment as "real environments" for our critic model as illustrated on the top part of Fig. . Since the atomic composition of the training structures do not necessarily match the composition of the predicted structure, during preprocessing we extract the atom centered local descriptors for every atom in the structure and order them in lists based on the central atom type. For the critic to evaluate a "real" structure, descriptors are randomly drawn from these lists according to the goal composition. For example, if the training database consists of structures with the composition C 4 H 10 O and C 6 H 14 O, but the aim is to generate isomers with the C 5 H 12 O composition, then the atomic descriptors of the generated structure, as well as 5 C, 12 H and 1 O atomic descriptors sampled randomly from the training structures are input to the critic, Fig. . This is necessary to prevent the critic from distinguishing between compositions (flagging all C 5 H 12 O as fake, and all C 4 H 10 O and C 6 H 14 O as real), rather than discerning between physically valid and invalid environments. It furthermore enables the model to predict on entirely unseen stoichiometries without a single data entry, which is in contrast to some other current models requiring retraining on new stoichiometries. Also, the resampling allows our model to operate in low-data regimes, as there is a much larger set of possible resampled descriptors than actual training structures.