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- 01GPXAC64SACCTE8AD05Z301P2 classification A1.
- 01GPXAC64SACCTE8AD05Z301P2 date "2022".
- 01GPXAC64SACCTE8AD05Z301P2 language "eng".
- 01GPXAC64SACCTE8AD05Z301P2 type journalArticle.
- 01GPXAC64SACCTE8AD05Z301P2 hasPart 01H2WY3835VF6T8AM0KB2S4MPD.pdf.
- 01GPXAC64SACCTE8AD05Z301P2 subject "Mathematics and Statistics".
- 01GPXAC64SACCTE8AD05Z301P2 subject "Physics and Astronomy".
- 01GPXAC64SACCTE8AD05Z301P2 doi "10.1038/s41467-022-31732-3".
- 01GPXAC64SACCTE8AD05Z301P2 issn "2041-1723".
- 01GPXAC64SACCTE8AD05Z301P2 issue "1".
- 01GPXAC64SACCTE8AD05Z301P2 volume "13".
- 01GPXAC64SACCTE8AD05Z301P2 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. 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.".
- 01GPXAC64SACCTE8AD05Z301P2 author B90ED270-F0EE-11E1-A197-91C894A0A6B4.
- 01GPXAC64SACCTE8AD05Z301P2 author urn:uuid:f1decfb2-6e4d-46e8-97b5-5a257051d75f.
- 01GPXAC64SACCTE8AD05Z301P2 author urn:uuid:fa09c94e-89fa-4ed0-bd79-b33727c3811a.
- 01GPXAC64SACCTE8AD05Z301P2 dateCreated "2023-01-16T12:55:25Z".
- 01GPXAC64SACCTE8AD05Z301P2 dateModified "2024-12-12T19:15:44Z".
- 01GPXAC64SACCTE8AD05Z301P2 name "Robust prediction of force chains in jammed solids using graph neural networks".
- 01GPXAC64SACCTE8AD05Z301P2 pagination urn:uuid:4d252fb5-5763-4f9b-aafb-78739de77067.
- 01GPXAC64SACCTE8AD05Z301P2 sameAs LU-01GPXAC64SACCTE8AD05Z301P2.
- 01GPXAC64SACCTE8AD05Z301P2 sourceOrganization urn:uuid:82d64012-023d-462c-b42c-43e6344c8d8c.
- 01GPXAC64SACCTE8AD05Z301P2 type A1.