DiSCoVeR: DiSCoVeR algorithm is a recently proposed ensemble of machine learning methods aimed at facilitating the identification of chemistries lying at the intersection between novelty and performance. In practice, DiSCoVeR can be used to provide novelty scores of a given pool of data with respect to another and it was recently employed to identify new chemically novel high-temperature superconductors The framework employed by DiSCoVeR is structured as follows: first, a distance matrix between all compositions in the dataset is computed by using the Element Movers Distance , a proposed metric which takes into account chemical similarities; subsequently, the obtained distance matrix is used to obtain 2D UMAP embeddings of all data points (A ∪ B); the likelihood of each point in B is computed with respect to the density of A, returning a quantitative measure of novelty (density score). Compositions in regions of low density are assigned with a higher novelty score. In the original DiSCoVeR implementation, a complementary score target score is calculated based on a specific property of interest. Subsequently, these two scores are combined using predetermined weighting factors to highlight materials that lie at the intersection of novelty and performance boundaries. We rely only on the density score to propose the 10% top candidates of B to be merged into the training set of A. To avoid merging a novel data block with all points similar to each other, we iteratively alternate the merging of a small number of candidates and an update of the novelty scores, until 10% of B is integrated into A.