Predicting the pathways of string-like motions in metallic glasses via path featurizing graph neural networks

String-like motions (SLMs) cooperative, “snake”-like movements of particles—are crucial for dynamics in diverse glass formers.  Despite their ubiquity, questions persist: do SLMs prefer specific paths? If so, can we predict these paths? Here, in Al-Sm glasses, our iso-configurational ensemble simulations reveal that SLMs indeed follow certain paths. By designing a graph neural network (GNN) to featurize the environment around directional paths, we achieve a high-fidelity prediction of likely SLM pathways solely based on the static structure. GNN gauges a structural measure to assess each path’s propensity to engage in SLMs, akin to a “softness” metric, but for paths rather than for atoms. Our GNN interpretation reveals the critical role of the bottleneck zone along paths in steering SLMs. By monitoring “path-softness”, we elucidate SLM-favored paths transit from fragmented to interconnected upon glass transition. Our findings reveal that, beyond atoms or clusters, glasses have another dimension of structural heterogeneity: “paths”.

Methods
This dataset contains the string-like motion (SLM) probability data of Al90Sm10 metallic glasses that are established by the iso-configurational ensemble simulations.

There are a total of 61 independent Al90Sm10 samples that are quenched to and relaxed at 400 K.

Additional details, including the train/val/test splits used in the paper, can be found in "Readme.txt".