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Learning physical descriptors for materials science by compressed sensing

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons47933

Ghiringhelli,  Luca M.
Theory, Fritz Haber Institute, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons203189

Ahmetcik,  Emre
Theory, Fritz Haber Institute, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons188977

Ouyang,  Runhai
Theory, Fritz Haber Institute, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons21797

Levchenko,  Sergey V.
Theory, Fritz Haber Institute, Max Planck Society;

Draxl,  Claudia
Theory, Fritz Haber Institute, Max Planck Society;
Humboldt-Universität zu Berlin, Institut für Physik and IRIS Adlershof;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons22064

Scheffler,  Matthias
Theory, Fritz Haber Institute, Max Planck Society;
Department of Chemistry and Biochemistry and Materials Department, University of California-Santa Barbara;

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Zitation

Ghiringhelli, L. M., Vybiral, J., Ahmetcik, E., Ouyang, R., Levchenko, S. V., Draxl, C., et al. (2017). Learning physical descriptors for materials science by compressed sensing. New Journal of Physics, 19(2): 023017. doi:10.1088/1367-2630/aa57bf.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-002C-9388-3
Zusammenfassung
The availability of big data in materials science offers new routes for analyzing materials properties and functions and achieving scientific understanding. Finding structure in these data that is not directly visible by standard tools and exploitation of the scientific information requires new and dedicated methodology based on approaches from statistical learning, compressed sensing, and other recent methods from applied mathematics, computer science, statistics, signal processing, and information science. In this paper, we explain and demonstrate a compressed-sensing based methodology for feature selection, specifically for discovering physical descriptors, i.e., physical parameters that describe the material and its properties of interest, and associated equations that explicitly and quantitatively describe those relevant properties. As showcase application and proof of concept, we describe how to build a physical model for the quantitative prediction of the crystal structure of binary compound semiconductors.