Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks

@article{Xie2018HierarchicalVO,
  title={Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks},
  author={Tian Xie and Jeffrey C. Grossman},
  journal={The Journal of chemical physics},
  year={2018},
  volume={149 17},
  pages={
          174111
        }
}
  • T. XieJ. Grossman
  • Published 9 July 2018
  • Materials Science
  • The Journal of chemical physics
The combination of high throughput computation and machine learning has led to a new paradigm in materials design by allowing for the direct screening of vast portions of structural, chemical, and property spaces. The use of these powerful techniques leads to the generation of enormous amounts of data, which in turn calls for new techniques to efficiently explore and visualize the materials space to help identify underlying patterns. In this work, we develop a unified framework to… 
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