From DFT to machine learning: recent approaches to materials science–a review

@article{Schleder2019FromDT,
  title={From DFT to machine learning: recent approaches to materials science–a review},
  author={Gabriel Ravanhani Schleder and Antonio Claudio M. Padilha and Carlos Mera Acosta and Marcio Costa and Adalberto Fazzio},
  journal={Journal of Physics: Materials},
  year={2019},
  volume={2}
}
Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the materials science field. Identifying correlations and patterns from large amounts of complex data is being performed by machine learning algorithms for decades. Recently, the materials science community started to invest in these methodologies to extract knowledge and insights from the… 
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