Unsupervised Graph Neural Network Reveals the Structure–Dynamics Correlation in Disordered Systems [article]

Vaibhav Bihani, Sahil Manchanda, Sayan Ranu, N. M. Anoop Krishnan
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,
more » ... l local structures with varying potential energies. These clusters exhibit dynamical heterogeneity in the structure in congruence with their local energy landscape. Altogether, the present study shows that unsupervised graph embedding can reveal the structure--dynamics correlation in disordered structures.
arXiv:2206.12575v1 fatcat:wwatz5fnmzdrzpqrgleaddghpq