The SGD analysis is based on a dataset 𝑃 ̃, which contains a known set of materials. 𝑃 ̃ is part of a larger space of possible materials, the full, typically infinite population 𝑃. For the materials in 𝑃 ̃, we know a target of interest 𝑌 (metric or categorical), such as a materials' property, as well as many candidate descriptive parameters 𝜑 possibly correlated with the underlying phenomena governing 𝑌 (Fig. ). From this dataset, SGD generates propositions 𝜋 about the descriptive parameters, e.g., inequalities constraining their values, and then identifies selectors 𝜎, conjunctions of 𝜋, that result in SGs that maximize a quality function 𝑄: In Eq. 1, the ratio 𝑠 𝑆𝐺 𝑠 𝑃 ⁄ is called the coverage, where 𝑠 𝑆𝐺 and 𝑠 𝑃 ̃ are the number of data points in the SG and in 𝑃 ̃, respectively. The utility function 𝑢(𝑆𝐺, 𝑃 ̃) measures how exceptional the SGs are compared to 𝑃 ̃ based on the distributions of 𝑌 values in the SG and in 𝑃 ̃. 𝑄 establishes a tradeoff between the coverage (generality) and the utility (exceptionality), which can be tuned by a tradeoff parameter 𝛾. Typically, the identified selectors only depend on few of the initially offered candidate descriptive parameters. The identified SG selectors (or rules) describe the local behaviour in the SG and they can be exploited for the identification of new materials in 𝑃.