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Unraveling the structural factors influencing the dynamics of amorphoussolids is crucial. While deep learning aids in navigating these complexities,transparency issues persist. Inspired by the successful application ofprototype neural networks in the field of image analysis, this study introducesa new machine-learning approach to tackle the interpretability challenges facedin glassy research. Distinguishing from traditional machine learning modelsthat only predict dynamics from the structural input, the adapted neuralnetwork additionally tries to learn structural prototypes under various dynamicpatterns in the training phase. Such learned structural constraints can serveas a breakthrough in explaining how structural differences impact dynamics. Wefurther use the proposed model to explore the correlation between the localstructure and activation energy in the CuZr metallic glass. Building upon thisinterpretable model, we demonstrated significant structural differences amongparticles with distinct activation energies. The insights gained from thisanalysis serve as a data-driven solution for unraveling the origins of thestructural heterogeneity in amorphous alloys, offering a valuable contributionto the understanding the amorphous materials.

Towards interpreting the thermally activated $\beta$ dynamics in metallic glass with the structural constraint neural network