  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, reveal 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.
