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

Author

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  • Rituparno Mandal

    (Georg-August-Universität Göttingen)

  • Corneel Casert

    (Ghent University)

  • Peter Sollich

    (Georg-August-Universität Göttingen
    King’s College London)

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.

Suggested Citation

  • Rituparno Mandal & Corneel Casert & Peter Sollich, 2022. "Robust prediction of force chains in jammed solids using graph neural networks," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31732-3
    DOI: 10.1038/s41467-022-31732-3
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    References listed on IDEAS

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    1. Nicolas Brodu & Joshua A. Dijksman & Robert P. Behringer, 2015. "Spanning the scales of granular materials through microscopic force imaging," Nature Communications, Nature, vol. 6(1), pages 1-6, May.
    2. Emanuele Boattini & Susana Marín-Aguilar & Saheli Mitra & Giuseppe Foffi & Frank Smallenburg & Laura Filion, 2020. "Autonomously revealing hidden local structures in supercooled liquids," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    3. T. S. Majmudar & R. P. Behringer, 2005. "Contact force measurements and stress-induced anisotropy in granular materials," Nature, Nature, vol. 435(7045), pages 1079-1082, June.
    4. Brujić, Jasna & F. Edwards, Sam & Hopkinson, Ian & Makse, Hernán A., 2003. "Measuring the distribution of interdroplet forces in a compressed emulsion system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 327(3), pages 201-212.
    5. Srdjan Ostojic & Ellák Somfai & Bernard Nienhuis, 2006. "Scale invariance and universality of force networks in static granular matter," Nature, Nature, vol. 439(7078), pages 828-830, February.
    6. Rituparno Mandal & Pranab Jyoti Bhuyan & Pinaki Chaudhuri & Chandan Dasgupta & Madan Rao, 2020. "Extreme active matter at high densities," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
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