Big data of materials science: critical role of the descriptor.

By Luca M. Ghiringhelli, Jan Vyb'iral, Sergey V. Levchenko, C. Draxl, Matthias Scheffler. 2014

684 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Materials Chemistry

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Citations in the corpus

Machine learning bandgaps of double perovskites (2016, methodology): “While χ , I , h and l naturally form the energy scales relevant towards prediction of the bandgap, the valence orbital radii were included based on their excellent performance exhibited in classification of AB binary solids 50,51 .”
Machine learning for molecular and materials science (2018, background): “89 In the context of materials, useful descriptors 90 and new approaches for adapting simple existing heuristics for machine learning have been outlined; 91 however, much work remains to develop powerful new descriptions.”