  Metallic Glasses (MGs) are widely used materials that are stronger than steel
while being shapeable as plastic. While understanding the structure-property
relationship of MGs remains a challenge in materials science, studying their
energy barriers (EBs) as an intermediary step shows promise. In this work, we
utilize Graph Neural Networks (GNNs) to model MGs and study EBs. We contribute
a new dataset for EB prediction and a novel Symmetrized GNN (SymGNN) model that
is E(3)-invariant in expectation. SymGNN handles invariance by aggregating over
orthogonal transformations of the graph structure. When applied to EB
prediction, SymGNN are more accurate than molecular dynamics (MD)
local-sampling methods and other machine-learning models. Compared to precise
MD simulations, SymGNN reduces the inference time on new MGs from roughly 41
days to less than one second. We apply explanation algorithms to reveal the
relationship between structures and EBs. The structures that we identify
through explanations match the medium-range order (MRO) hypothesis and possess
unique topological properties. Our work enables effective prediction and
interpretation of MG EBs, bolstering material science research.
