We obtain a total of 27 clusters, plus a noncluster of unclassified points comprising a small percentage of the data (∼5 %). The number of clusters gives an estimation of the number of distinct chemical classes present in the dataset and is also affected by DensMAP and HDBSCAN* model parameters such as local density regularization strength (dens_lambda) and minimum cluster size (min_cluster_size). The unclassified points are typically isolated points in DensMAP space. In other words, unclassified points will likely exhibit high chemical contrast relative to other composi-Table : Validation methods, splits, notion of best fit, and property used to calculate notion of best fit. * This density is the sum of all training densities evaluated at the validation location in the embedded DensMAP space. For the k-neighbors data, the average of the 10 nearest neighbor properties were used as a proxy. † cluster validation fraction refers to the ratio of number of validation points within a cluster (as opposed to training points) to the total number of points in the cluster. DensMAP densities and cluster fractions are determined simultaneously for both validation and training sets during the DensMAP embedding resulting in computational throughput restrictions. In other words, "predicted" and "true" are identical due to implementation of DiSCoVeR at the time of writing. We plan to address this in future work. tions via a low density proxy. We discuss this further in Section 3.2. A summary of the computational runtimes of the various methods is given in Table . Computation of the full pairwise distance matrix takes ∼18 s, which is quite fast due to use of a CUDA/Numba version of the Wasserstein distance that we developed for this work. An NVIDIA GeForce RTX 2060 is used for GPU computations, and an In-tel® Core™ i7-10750H CPU @ 2.60GHz is used for CPU computations. All non-GPU calculations are single-threaded.