Predicting the pathways of string-like motions in metallic glasses via path featurizing graph neural networks
Data files
Apr 01, 2024 version files 19.18 MB
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data-AlSm.zip
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README.md
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”.
README: Predicting the Pathways of String-like Motions in Metallic Glasses via Path-Featurizing Graph Neural Networks
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 --> 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.
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".