Geometry-enhanced graph neural network for learning the smoothness of glassy dynamics from static structure

Understand

Understanding the dynamic processes of the glassy system continues to be challenging.

• Recent advances have shown the power of graph neural networks (GNNs) for determining the correlation between structure and dynamics in the glassy system.
• These methods treat the glassy system as a topological graph.
• However, the inherent "smoothness" property of the dynamics on the graph (variation of dynamics over the graph) is ignored, resulting in a deteriorated performance of GNN for dynamic predictions over various time scales.