Finally, we perform LOCO-CV to evaluate the utility of the DiSCoVeR method in identifying clusters with high average cluster bulk modulus. A LOCO-CV parity plot is given in Figure . We accurately sort the list of validation clusters by their average performance with a weighted scaled sorting error (Section 2.3) of ∼1.4 %. In other words, the out-of-cluster regression is very accurate. This suggests that CrabNet can successfully extrapolate performance predictions for new chemical spaces in accordance with the goal of DiSCoVeR. In future work, we plan to also test the out-of-cluster extrapolation performance for chemical uniqueness proxies (Section 2.3).