Robust prediction of force chains in jammed solids using graph neural networks

Abstract

The emergence of correlated and self-organized linear structures, known as force chains, is relevant for granular materials, foams, emulsions, and extreme active matter. The authors develop a machine learning-based approach to predict force chain formation in jammed disordered solids under deformation.