Gilmer, et al.(Neural Message Passing) investigated various graph neural networks for predicting molecular properties, and proposed the neural message passing framework that unifies them. Xie, et al.(Crystal Graph Convolutional Neural Networks) studied graph neural networks to predict bulk properties of crystalline materials, and used a multi-graph named a crystal graph. Schütt, et al.(SchNet ) proposed a deep learning architecture with an implicit graph neural network not only to predict material properties, but also to perform molecular dynamics simulations. These studies use bond distances as features for machine learning. In contrast, the CGNN architectures use no bond distances to predict bulk properties at equilibrium states of crystalline materials at 0 K and 0 Pa, such as the formation energy, the unit cell volume, the band gap, and the total magnetization.
Yamamoto, Takenori. "Crystal Graph Neural Networks for Data Mining in Materials Science." (2019).
Graph Neural Network, Predict bulk properties, equilibrium states,
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