DiSCoVeR depends on clusters exhibiting homogeneity with respect to chemical classes, which we enforce via a recently introduced distance metric: Element Mover's Distance (ElMD) . Dimensionality reduction algorithms such as Uniform Manifold Approximation and Projection (UMAP) or t-distributed stochastic neighbor embeddings can then be used to create lowdimensional embeddings suitable for clustering algorithms such as Hierarchical Density-based Spatial Clustering of Applications with Noise (HDB-SCAN*) or k-means clustering .