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Crystal Graph Neural Networks

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Crystal graph neural network (CGNN) architectures were developed for materials property predictions on the basis of a multi-graph representing each crystalline material in a materials database. A study on these architectures is presented in the paper "Crystal Graph Neural Networks for Data Mining in Materials Science". This study used the PyTorch implementation available from the repository Tony-Y/cgnn. This site provides the user guide for the CGNN program.

When you mention this work, please cite the CGNN paper: 

@techreport{yamamoto2019cgnn,
  Author = {Takenori Yamamoto},
  Title = {Crystal Graph Neural Networks for Data Mining in Materials Science},
  Address = {Yokohama, Japan},
  Institution = {Research Institute for Mathematical and Computational Sciences, LLC},
  Year = {2019},
  Note = {https://github.com/Tony-Y/cgnn}
}