  Statistical learning of materials properties or functions so far starts with
a largely silent, non-challenged step: the choice of the set of descriptive
parameters (termed descriptor). However, when the scientific connection between
the descriptor and the actuating mechanisms is unclear, causality of the
learned descriptor-property relation is uncertain. Thus, trustful prediction of
new promising materials, identification of anomalies, and scientific
advancement are doubtful. We analyse this issue and define requirements for a
suited descriptor. For a classical example, the energy difference of
zincblende/wurtzite and rocksalt semiconductors, we demonstrate how a
meaningful descriptor can be found systematically.
