  Machine learning (ML) methods have gained increasing popularity in exploring
and developing new materials. More specifically, graph neural network (GNN) has
been applied in predicting material properties. In this work, we develop a
novel model, GATGNN, for predicting inorganic material properties based on
graph neural networks composed of multiple graph-attention layers (GAT) and a
global attention layer. Through the application of the GAT layers, our model
can efficiently learn the complex bonds shared among the atoms within each
atom's local neighborhood. Subsequently, the global attention layer provides
the weight coefficients of each atom in the inorganic crystal material which
are used to considerably improve our model's performance. Notably, with the
development of our GATGNN model, we show that our method is able to both
outperform the previous models' predictions and provide insight into the
crystallization of the material.
