As a proof of concept, we use 10 583 unique chemical formulae and associated bulk moduli from Materials Project to test whether DiSCoVeR can find new classes of materials with high performance. In accordance with materials informatics best practices , we also sanitize the data. Materials are filtered to exclude noble gases, Tccontaining compounds, and compounds with an energy above hull value greater than 500 meV. The highest bulk modulus is chosen when considering identical formulae. We use CrabNet as the regression model for bulk modulus which depends only on composition to generate machine learning features; however, one of the other models mentioned in Section 1 could have been used instead.