The potential of SGD to uncover local patterns in materials science has been demonstrated by the identification of structure-property relationships, and by the discovery of materials for heterogeneous catalysis. Additionally, using (prediction) errors as target in SGD, we identified descriptions of the regions of the materials space in which (machine-learning) models have low or high errors. Thus, the domain of applicability (DoA) of the models could be established. Despite these encouraging results, the advancement of the SGD approach in materials science requires addressing key challenges:  The quality function introduces one generality-exceptionality tradeoff, among a multitude of possible tradeoffs that can be relevant for a given application and that can be obtained with different 𝛾. For instance, the required hardness of a material depends on the type of device in which it will be used and the DoA of a model depends on the accuracy that is acceptable to describe a certain property or phenomenon. However, choosing the appropriate 𝛾 and assessing the similarity -or redundancyamong the multiple rules obtained with different tradeoffs are challenging tasks.