Figure (a) shows density parity plots of all outer test set predictions for C K = {Random, Structure, Composition, Chemsys, SG#, Elements} and D = 1.0, T = None, and S = K-fold, while Figure (b) shows the same but for T = Binary and S = (K, L)-fold. The color code is on a logarithmic scale with respect to the number of predictions at that grid point. Note that for this dataset, we are able to compute all ∆H V for at least one of each binary oxide in the chemical space of interest, motivating the investigation of automatically assigning binaries to the training data. Immediately noticeable in Figure ) is the mitigation of over-fitting and substantial error reduction for some outliers observed in Figure . Additional analysis in the Supplementary Information, applicable only to this exemplar, investigates the C K = Elements parity plots at a more granular level and further reveals insights into the generalization capabilities of dGNN.