Key points are not available for this paper at this time.
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.
Building similarity graph...
Analyzing shared references across papers
Loading...
Luca M. Ghiringhelli
Jan Vybíral
Sergey V. Levchenko
Physical Review Letters
Humboldt-Universität zu Berlin
Charles University
Fritz Haber Institute of the Max Planck Society
Building similarity graph...
Analyzing shared references across papers
Loading...
Ghiringhelli et al. (Tue,) studied this question.
www.synapsesocial.com/papers/6993430e8a0caae9a931b4f9 — DOI: https://doi.org/10.1103/physrevlett.114.105503