  Unraveling the structural factors influencing the dynamics of amorphous
solids is crucial. While deep learning aids in navigating these complexities,
transparency issues persist. Inspired by the successful application of
prototype neural networks in the field of image analysis, this study introduces
a new machine-learning approach to tackle the interpretability challenges faced
in glassy research. Distinguishing from traditional machine learning models
that only predict dynamics from the structural input, the adapted neural
network additionally tries to learn structural prototypes under various dynamic
patterns in the training phase. Such learned structural constraints can serve
as a breakthrough in explaining how structural differences impact dynamics. We
further use the proposed model to explore the correlation between the local
structure and activation energy in the CuZr metallic glass. Building upon this
interpretable model, we demonstrated significant structural differences among
particles with distinct activation energies. The insights gained from this
analysis serve as a data-driven solution for unraveling the origins of the
structural heterogeneity in amorphous alloys, offering a valuable contribution
to the understanding the amorphous materials.
