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Dryad

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

Wang, Qi1; Zhang, Longfei2; Zhou, Zhen-Ya3; Yu, Hai-Bin4

Published Apr 01, 2024 on Dryad. https://doi.org/10.5061/dryad.2z34tmptt

Data files

Apr 01, 2024 version files 19.18 MB

Abstract

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”.