Global Attention based Graph Convolutional Neural Networks for Improved Materials Property Prediction
This work addresses the need for more accurate materials property prediction in materials science, representing an incremental improvement over existing graph neural network methods.
The authors tackled the problem of predicting inorganic material properties by developing GATGNN, a graph neural network model that combines local graph-attention layers with a global attention layer, achieving improved performance over previous models.
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.