Berlin 2015 – wissenschaftliches Programm
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MM: Fachverband Metall- und Materialphysik
MM 19: Methods in Computational Materials Modelling I: Materials Design
MM 19.2: Vortrag
Dienstag, 17. März 2015, 10:30–10:45, H 0106
Big Data of Materials Science - Critical Role of the Descriptor — •Luca M. Ghiringhelli1, Jan Vybiral2, Sergey V. Levchenko1, Claudia Draxl3, and Matthias Scheffler1 — 1Fritz-Haber-Institut der MPG, Berlin, DE — 2Charles University, Prague, CZ — 3Humboldt-Universität zu Berlin, Berlin, DE
Statistical learning of materials properties or functions so far starts with a largely silent, non-challenged step: the introduction of a multidimensional descriptor. However, when the scientific relationship of the descriptor to the actuating mechanisms is unclear, causality of the trained (learned) descriptor-property relation is uncertain. Thus, scientific advancement, trustful prediction of new promising materials and identification of anomalies is doubtful. We discuss and analyze this issue and define requirements for a descriptor that is suited for statistical learning of materials properties and functions. We show how a meaningful descriptor can be found systematically, by means of compressed sensing techniques. These concepts are demonstrated for examples in materials science: prediction of the relative stability of zincblende/wurtzite vs rocksalt octet binary semiconductors, and prediction of their band gaps, by using simple atomic input for building the descriptor.