DOI: 10.5061/dryad.2z34tmptt
title: Predicting the pathways of string-like motions in metallic glasses via path featurizing graph neural networks

description: 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”.
description: 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".
description: # Predicting the Pathways of String-like Motions in Metallic Glasses via
 Path-Featurizing Graph Neural Networks
 [https://doi.org/10.5061/dryad.2z34tmptt](https://doi.org/10.5061/dryad.2z34tmptt) This dataset contains the string-like motion (SLM) probability data of Al90Sm10 metallic glasses that are established by the iso-configurational ensemble simulations. ## Description of the data and file structure There are a total of 61 independent Al90Sm10 samples that are quenched to and relaxed at 400 K. **Files of each sample directory:** **[data.dump]** The glass configuration in the format of LAMMPS dump. Atom type 1 is for Al, 2 is for Sm. **[string_probability.csv]** The string-like motion (SLM) probability data of each glass configuration. The columns are ["source_id", "final_id", "atom_types", "string_probability"] "source_id": the source id of the string. "final_id": the final id of the string. For example, if source_id is 103 and final_id is 107, this indicates a string segment as 103 --&gt; 107. "atom_types": (atom type of source_id, atom type of final_id). For example, (1, 1) represents (Al, Al). "string_probability": the probability of each string segment emerging during the iso-configurational ensemble simulations (a total of 170 runs). It is calculated as a ratio of two counts (the number of times a string is observed to the total number of simulations), and is therefore a dimensionless quantity. **Train_val_test splits:** 56 samples are used for training, 3 samples for validation and 2 samples are set aside for testing. The train_val_test splits used in the paper: val_samples: [sample5, sample2, sample11] test_samples: [sample15, sample6] train_samples: all remaining samples **Citation:** Qi Wang*, Long-Fei Zhang, Zhen-Ya Zhou, Hai-Bin Yu*. Predicting the Pathways of String-like Motions in Metallic Glasses via Path-Featurizing Graph Neural Networks, 2024.