title: Big data of materials science: critical role of the descriptor.
abstract: Statistical learning of materials properties or functions so far starts with a largely silent, nonchallenged 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, the causality of the learned descriptor-property relation is uncertain. Thus, a trustful prediction of new promising materials, identification of anomalies, and scientific advancement are doubtful. We analyze this issue and define requirements for a suitable descriptor. For a classic example, the energy difference of zinc blende or wurtzite and rocksalt semiconductors, we demonstrate how a meaningful descriptor can be found systematically.
authors: Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195 Berlin-Dahlem, Germany.
authors: Luca M
authors: LM
authors: Ghiringhelli
authors: Department of Mathematical Analysis, Charles University, 18675 Prague, Czech Republic.
authors: Jan
authors: J
authors: Vybiral
authors: Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195 Berlin-Dahlem, Germany.
authors: Sergey V
authors: SV
authors: Levchenko
authors: Humboldt-Universität zu Berlin, Institut für Physik and IRIS Adlershof, 12489 Berlin, Germany.
authors: Claudia
authors: C
authors: Draxl
authors: Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195 Berlin-Dahlem, Germany.
authors: Matthias
authors: M
authors: Scheffler