Noteworthy, the cumulative Jensen-Shannon divergence (𝐷 𝐽𝑆 ) between the distribution of bulk moduli in the SG and in the entire dataset is used as quality function in the example of Fig. . 𝐷 𝐽𝑆 assumes small values for similar distributions and increases as the distribution of target values in the SG is, e.g., shifted or narrower with respect to the distribution of the entire dataset. Crucially, 𝐷 𝐽𝑆 does not assume that one single summary-statistics value represents the distributions. Divergence-based utility functions addressing, e.g., high or low target values, will thus be an important advance. We note that the utility function might also incorporate information on multiple targets or physical constraints that are specific to the scientific question being addressed. However, in order to ensure that the training data is representative of the relevant materials space one would like to cover, the iterative incorporation of new data points and training of SGD rules in an active-learning fashion might be required.