Learning physical descriptors for materials science by compressed sensing

Learning physical descriptors for materials science by compressed sensing

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...

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Bibliographic Details
Main Authors: Luca M Ghiringhelli, Jan Vybiral, Emre Ahmetcik, Runhai Ouyang, Sergey V Levchenko, Claudia Draxl, Matthias Scheffler
Format: Article
Language:English
Published: IOP Publishing 2017-01-01
Series:New Journal of Physics
Subjects:
crystal-structure prediction
big-data driven materials science
compressed sensing
feature selection
Online Access:https://doi.org/10.1088/1367-2630/aa57bf

Internet

https://doi.org/10.1088/1367-2630/aa57bf

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