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arXiv · 1411.7437

Big Data of Materials Science - Critical Role of the Descriptor

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

Statistical learning of materials properties or functions so far starts with a largely silent, non-challenged 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, causality of the learned descriptor-property relation is uncertain. Thus, trustful prediction of new promising materials, identification of anomalies, and scientific advancement are doubtful. We analyse this issue and define requirements for a suited descriptor. For a classical example, the energy difference of zincblende/wurtzite and rocksalt semiconductors, we demonstrate how a meaningful descriptor can be found systematically.

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Luca M. Ghiringhelli, Jan Vybiral, Sergey V. Levchenko, Claudia Draxl, Matthias Scheffler. 2015-02-05. Big Data of Materials Science - Critical Role of the Descriptor. https://doi.org/10.1103/physrevlett.114.105503

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