Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties.

@article{Xie2018CrystalGC,
  title={Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties.},
  author={Tian Xie and Jeffrey C. Grossman},
  journal={Physical review letters},
  year={2018},
  volume={120 14},
  pages={
          145301
        }
}
  • T. Xie, J. Grossman
  • Published 27 October 2017
  • Computer Science, Materials Science
  • Physical review letters
The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. [] Key Method Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure…

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