Assessing inadequacies of training data is more complicated. If 3-body structures from (MeCN) 16 are substantially different from the data sets, then the model may break down. To investigate this, we used dimensionality reduction to visualize high-dimensional similarity in 2D space. Similar structures in feature space should be clustered together and vice versa. Fig. shows the GDML feature space, a 2D embedding of trained and 3-body structures from (MeCN) 16 using Uniform Manifold Approximation and Projection (UMAP). There is a significant overlap between the GDML training set feature space and the structures from (MeCN) 16 . High overlap suggests that GDML should have low prediction errors, which is the case. SchNet, on the other hand, has several test structures isolated from training data, resulting in higher errors (shown in the ESI). Not all structures with a high error are dissimilar in feature space. Models should have learned similar structures and thus should have performed well. A simple, ad hoc geometry descriptor (discussed in the ESI) applied in Fig. shows that all higherror structures are dissimilar to anything in the data set. SchNet has some difficulty with these structures, which results in a substantial 16.1 kcal/mol error. Many-body GAP's 17.6 kcal/mol error in (MeOH) 16 is likely for the same reason. However, GAP uses a local descriptor, making feature space more complicated to analyze. Models under these circumstances were excluded from further analyses (namely, mbSchNet for acetonitrile and mbGAP for methanol).