Artificial-intelligence (AI) approaches in materials science usually attempt a description of all possible scenarios with a single, global model. However, the materials that are useful for a given application, which requires a special and high performance, are often statistically exceptional. For instance, one might be interested in identifying exceedingly hard materials, or materials with band gap within a narrow range of values. Global models of materials' properties and functions are designed to perform well in average for the majority of (uninteresting) compounds. Thus, AI might well overlook the useful materials. In contrast, subgroup discovery (SGD) identifies local descriptions of the materials space, accepting that a global model might be inaccurate or inappropriate to capture the useful materials subspace. Indeed, different mechanisms may govern the materials' performance across the immense materials space and SGD can focus on the mechanism(s) that result in exceptional performance.