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

ABSTRACT: Force chains are quasi-linear self-organised structures carrying large stresses and are ubiquitous in jammed amorphous materials like granular materials, foams or even cell assemblies. Predicting where they will form upon deformation is crucial to describe the properties of such materials, but remains an open question. Here we demonstrate that graph neural networks (GNN) can accurately predict the location of force chains in both frictionless and frictional materials from the undeformed structure, without any additional information. The GNN prediction accuracy also proves to be robust to changes in packing fraction, mixture composition, amount of deformation, friction coefficient, system size, and the form of the interaction potential. By analysing the structure of the force chains, we ide

SUBMITTER: Mandal R 

PROVIDER: S-EPMC9338954 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature