Crystal graph convolutional neural networks for per-site property prediction

Graph convolutional neural networks (GCNNs) have been shown to accurately predict materials properties by featurizing local atomic environments. However, such models have not yet been utilized for predicting per-site features such as Bader charge, magnetic moment, or site-projected band centers. In this work, we develop a per-site crystal graph convolutional neural network that predicts a wide array of per-site properties. This model outperforms a per-element average baseline, and is thus capturing the effect of the neighborhood around each atom. Using magnetic moments as a case study, we explore an example of underlying physics the per-site model is able to learn.