Learning physical descriptors for materials science by compressed sensing

Ghiringhelli LM, Vybiral J, Ahmetcik E, Ouyang R, Levchenko SV, Draxl C, Scheffler M (2017)

Publication Type: Journal article

Publication year: 2017

Journal

New Journal of Physics Institute of Physics: Open Access Journals / Institute of Physics (IoP) and Deutsche Physikalische Gesellschaft

Book Volume: 19

Article Number: 023017

Journal Issue: 2

DOI: 10.1088/1367-2630/aa57bf

Abstract

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.

Authors with CRIS profile

Luca Ghiringhelli

Involved external institutions

Fritz-Haber-Institut der Max-Planck-Gesellschaft (FHI) DE Germany (DE) Univerzita Karlova v Praze / Charles University in Prague CZ Czech Republic (CZ)

How to cite

APA:

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

MLA:

Ghiringhelli, Luca M., et al. "Learning physical descriptors for materials science by compressed sensing." New Journal of Physics 19.2 (2017).

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