Predicting and Interpreting Energy Barriers of Metallic Glasses with
  Graph Neural Networks
Haoyu Li Shichang Zhang Longwen Tang Mathieu Bauchy Yizhou Sun
Metallic Glasses (MGs) are widely used disordered materials. Understanding
the relationship between the local structure and physical properties of MGs is
one of the greatest challenges for both material science and condensed matter
physics. In this work, we utilize Graph Neural Networks (GNNs) to model the
atomic graph structure and study the connection between the structure and the
corresponding local energy barrier, which is believed to govern many critical
physical properties in MGs. One of our key contributions is to propose a novel
Symmetrized GNN (SymGNN) model for predicting the energy barriers, which is
invariant under orthogonal transformations of the structure, e.g., rotations
and reflections. Such invariance is a desired property that standard GNNs like
Graph Convolutional Networks cannot capture. SymGNNs handle the invariance by
aggregating over orthogonal transformations of the graph structure for
representation learning, and an optimal distribution over all 3D orthogonal
transformations $\mathcal{O}_3$ is learned to maximize the benefit of
invariance. We demonstrate in our experiments that SymGNN can significantly
improve the energy barrier prediction over other GNNs and non-graph machine
learning models. With such an accurate model, we also apply graph explanation
algorithms to better reveal the structure-property relationship of MGs. Our GNN
framework allows effective prediction of material physical properties and
bolsters material science research through the use of AI models.