Big data of materials science: critical role of the descriptor.
Abstrak
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.
Penulis (5)
L. Ghiringhelli
J. Vybíral
S. Levchenko
C. Draxl
M. Scheffler
Akses Cepat
- Tahun Terbit
- 2014
- Bahasa
- en
- Total Sitasi
- 668×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1103/PhysRevLett.114.105503
- Akses
- Open Access ✓