Learning physical descriptors for materials science by compressed sensing

@article{Ghiringhelli2016LearningPD,
  title={Learning physical descriptors for materials science by compressed sensing},
  author={Luca M. Ghiringhelli and Jan Vyb{\'i}ral and Emre Ahmetcik and Runhai Ouyang and Sergey V. Levchenko and Claudia Draxl and Matthias Scheffler},
  journal={New Journal of Physics},
  year={2016},
  volume={19}
}
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… 

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