Used alone, composition information results in more accurate predictions of bulk modulus than structural information (GRID), but in both cases there are a significant number of materials where considering only one aspect of S-C space results in zero EMD but significant difference in bulk modulus (Fig. ). Both structure and composition are therefore critical to predict bulk modulus. Figure shows a two-dimensional representation of the highdimensional feature space for each of the similarity measures described, using t-distributed stochastic network embedding (t-SNE). While these plots are a dramatic simplification of the high dimensional data, proximity within the 2D plane indicates proximity within S-C space (although the reverse is not necessarily true). Examining the bulk modulus data it is clear that clusters with similar bulk moduli appear for both GRID and composition features, and the combined descriptor shows particularly strong clustering of the low-K materials. The same t-SNE distributions can also be visualised with different material properties; in this case we have extracted shear moduli, formation energies and band gaps (Fig. ). In all cases there is clustering of points with similar properties, although the clusters occur in different points of the t-SNE plane and with different amounts of spread. This ability of GRID and compositional similarity to place materials with similar properties in close proximity shows that they are an accurate representation of the underlying S-C space. Using a similar kNN (k=1) model on each of these properties with no further optimisation yields predictions with a MAE (median AE in brackets) of: 15 (7) GPa for shear modulus prediction, 0.26 (0.12) eV for formation energy and 0.45 (0.0) eV for band gap. In all cases the median AE is lower than the mean, indicating that outlying data points are enlarging the MAE (note that the median AE of 0 eV for band gap prediction arises due to the significant number of metallic materials present in the data).