ArticleNature communications2022
Robust prediction of force chains in jammed solids using graph neural networks.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- The interplay between stack pressure, mechanical expansion and degradation pathways in lithium-ion batteries.Nature energy · 2026Article
- Structural analysis of physical gel networks using graph neural networks.The European physical journal. E, Soft matter · 2025Article
- Mechanical Behavior of Flexible Fiber Assemblies: Review and Future Perspectives.Materials (Basel, Switzerland) · 2024Review
- Homophily modulates double descent generalization in graph convolution networks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Estimation of Particle Location in Granular Materials Based on Graph Neural Networks.Micromachines · 2023Article
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Authors and funding
3 authors.
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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 identify the key features that affect prediction accuracy. Our results and methodology will be of interest for granular matter and disordered systems, e.g. in cases where direct force chain visualisation or force measurements are impossible.
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