Big data of materials science: Critical role of the descriptor

Luca M. Ghiringhelli, Jan Vybiral, Sergey V. Levchenko, Claudia Draxl, Matthias Scheffler

Research output: Contribution to journalArticlepeer-review

505 Citations (Scopus)

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.

Original languageEnglish
Article number105503
JournalPhysical Review Letters
Volume114
Issue number10
DOIs
Publication statusPublished - 10 Mar 2015
Externally publishedYes

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