To understand the relationship between prediction accuracy and training set composition, we visualized the representations of bonds learned by the final model (Fig. ). Since the bond states have a dimension of 128, we performed dimensionality reduction with the t-SNE method to project 8924 different C-X bonds in two dimensions. The newly added C-X bonds used to build BDE-db2a and BDE-db2b are spread in chemical space and cover regions not present in the original dataset. Further, we studied specific examples of outlier predictions that were improved by successive generations of our model (Fig. ). For these bonds, we found the ten nearest neighbors (in the model's 128-dimensional latent space) from the training dataset and computed the mean distance of these nearest neighbors. In each case, we found that poor predictions result where the closest training bonds are highly chemically dissimilar to the query bond. Previous work has also shown that determining the distance in latent space enables the identification of high and low confidence points. Overall, the systematic improvement in performance can be attributed to the incorporation of new regions of chemical space in the training set containing more diverse structural features.