Towards a Multi-Objective Optimization of Subgroups for the Discovery of Materials with Exceptional Performance

Artificial intelligence (AI) has the potential to revolutionize the design of materials by uncovering correlations and complex patterns in data. However, current AI methods attempt to describe the entire, immense materials space with a single model, while different mechanisms govern the materials behaviors across the materials space. The subgroup-discovery (SGD) approach identifies local rules describing exceptional subsets of data with respect to a given target. Thus, SGD can focus on mechanisms leading to exceptional performance. However, the identification of appropriate SG rules requires a careful consideration of the exceptionality-generality tradeoff. Here, we discuss the notion of SG exceptionality and analyse the tradeoff between exceptionality and generality based on a Pareto front of SGD solutions, thus providing a roadmap for advancing the SGD approach in materials science.