We also present a Pareto front for cluster-wise properties. For the ordinate, we use predicted cluster average bulk modulus Figure . For the abscissa, we use cluster validation fraction as a proxy for chemical distinctiveness of a cluster. In this example, the data is clustered tightly in the abscissa due to a the train/val split being applied randomly without regard to cluster. In a more realistic scenario with much more validation data than training data, where the validation encompasses previously unexplored chemical spaces, there is likely to be a larger spread. Indeed, such a use-case is the intention for this visualization tool. There is a much wider spread in the ordinate, indicating an interesting feature of the clustering results: compositions which are chemically similar to each other also tend to have, on average, similar bulk moduli. This is reasonable, especially since the regression model used is based purely on composition.