• Corpus ID: 119280903

NOMAD 2018 Kaggle Competition: Solving Materials Science Challenges Through Crowd Sourcing

@article{Sutton2018NOMAD2K,
  title={NOMAD 2018 Kaggle Competition: Solving Materials Science Challenges Through Crowd Sourcing},
  author={Christopher Sutton and Luca M. Ghiringhelli and Takenori Yamamoto and Yury Lysogorskiy and Lars Blumenthal and Thomas Hammerschmidt and Jacek R. Golebiowski and Xiangyue Liu and Angelo Ziletti and Matthias Scheffler},
  journal={arXiv: Materials Science},
  year={2018}
}
Machine learning (ML) is increasingly used in the field of materials science, where statistical estimates of computed properties are employed to rapidly examine the chemical space for new compounds. However, a systematic comparison of several ML models for this domain has been hindered by the scarcity of appropriate datasets of materials properties, as well as the lack of thorough benchmarking studies. To address this, a public data-analytics competition was organized by the Novel Materials… 
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