  It is difficult to quantify structure-property relationships and to identify
structural features of complex materials. The characterization of amorphous
materials is especially challenging because their lack of long-range order
makes it difficult to define structural metrics. In this work, we apply deep
learning algorithms to accurately classify amorphous materials and characterize
their structural features. Specifically, we show that convolutional neural
networks and message passing neural networks can classify two-dimensional
liquids and liquid-cooled glasses from molecular dynamics simulations with
greater than 0.98 AUC, with no a priori assumptions about local particle
relationships, even when the liquids and glasses are prepared at the same
inherent structure energy. Furthermore, we demonstrate that message passing
neural networks surpass convolutional neural networks in this context in both
accuracy and interpretability. We extract a clear interpretation of how message
passing neural networks evaluate liquid and glass structures by using a
self-attention mechanism. Using this interpretation, we derive three novel
structural metrics that accurately characterize glass formation. The methods
presented here provide us with a procedure to identify important structural
features in materials that could be missed by standard techniques and give us a
unique insight into how these neural networks process data.
