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Unsupervised Graph Neural Network Reveals the Structure–Dynamics Correlation in Disordered Systems
[article]
2022
arXiv
pre-print
Learning the structure--dynamics correlation in disordered systems is a long-standing problem. Here, we use unsupervised machine learning employing graph neural networks (GNN) to investigate the local structures in disordered systems. We test our approach on 2D binary A65B35 LJ glasses and extract structures corresponding to liquid, supercooled and glassy states at different cooling rates. The neighborhood representation of atoms learned by a GNN in an unsupervised fashion, when clustered,
arXiv:2206.12575v1
fatcat:wwatz5fnmzdrzpqrgleaddghpq