Big data of materials science: Critical role of the descriptor

Ghiringhelli LM, Vybiral J, Levchenko SV, Draxl C, Scheffler M (2015)

Publication Type: Journal article

Publication year: 2015

Journal

Physical Review Letters American Physical Society

Book Volume: 114

Article Number: 105503

Journal Issue: 10

DOI: 10.1103/PhysRevLett.114.105503

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 with CRIS profile

Luca Ghiringhelli

Involved external institutions

Fritz-Haber-Institut der Max-Planck-Gesellschaft (FHI) DE Germany (DE) Univerzita Karlova v Praze / Charles University in Prague CZ Czech Republic (CZ) Humboldt-Universität zu Berlin DE Germany (DE)

How to cite

APA:

Ghiringhelli, L.M., Vybiral, J., Levchenko, S.V., Draxl, C., & Scheffler, M. (2015). Big data of materials science: Critical role of the descriptor. Physical Review Letters, 114(10). https://doi.org/10.1103/PhysRevLett.114.105503

MLA:

Ghiringhelli, Luca M., et al. "Big data of materials science: Critical role of the descriptor." Physical Review Letters 114.10 (2015).

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