  Force chains, which are quasi-linear self-organised structures carrying large
stresses, are ubiquitous in jammed amorphous materials, such as granular
materials, foams, emulsions or even assemblies of cells. Predicting where they
will form upon mechanical deformation is crucial in order to describe the
physical properties of such materials, but remains an open question. Here we
demonstrate that graph neural networks (GNN) can accurately infer the location
of these force chains in frictionless materials from the local structure prior
to deformation, without receiving any information about the inter-particle
forces. Once trained on a prototypical system, the GNN prediction accuracy
proves to be robust to changes in packing fraction, mixture composition, amount
of deformation, and the form of the interaction potential. The GNN is also
scalable, as it can make predictions for systems much larger than those it was
trained on. Our results and methodology will be of interest for experimental
realizations of granular matter and jammed disordered systems, e.g. in cases
where direct visualisation of force chains is not possible or contact forces
cannot be measured.
