  Subgroup discovery (SGD) is presented here as a data-mining approach to help
find interpretable local patterns, correlations, and descriptors of a target
property in materials-science data. Specifically, we will be concerned with
data generated by density-functional theory calculations. At first, we
demonstrate that SGD can identify physically meaningful models that classify
the crystal structures of 82 octet binary semiconductors as either rocksalt or
zincblende. SGD identifies an interpretable two-dimensional model derived from
only the atomic radii of valence s and p orbitals that properly classifies the
crystal structures for 79 of the 82 octet binary semiconductors. The SGD
framework is subsequently applied to 24 400 configurations of neutral gas-phase
gold clusters with 5 to 14 atoms to discern general patterns between
geometrical and physicochemical properties. For example, SGD helps find that
van der Waals interactions within gold clusters are linearly correlated with
their radius of gyration and are weaker for planar clusters than for nonplanar
clusters. Also, a descriptor that predicts a local linear correlation between
the chemical hardness and the cluster isomer stability is found for the
even-sized gold clusters.
