Institute for Theoretical Physics, Georg-August-Universität Göttingen, 37077, Göttingen, Germany.
Department of Physics and Astronomy, Ghent University, 9000, Ghent, Belgium.
Nat Commun. 2022 Jul 30;13(1):4424. doi: 10.1038/s41467-022-31732-3.
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.
力链是携带大应力的准线性自组织结构，在类似颗粒材料、泡沫甚至细胞组装等被阻塞的无定形材料中无处不在。预测它们在变形时将在哪里形成对于描述这些材料的性质至关重要，但仍然是一个悬而未决的问题。在这里，我们证明了图神经网络 (GNN) 可以从无变形结构中准确预测无摩擦和有摩擦材料中力链的位置，而无需任何其他信息。GNN 的预测精度也被证明对堆积分数、混合物组成、变形量、摩擦系数、系统大小和相互作用势的形式的变化具有鲁棒性。通过分析力链的结构，我们确定了影响预测精度的关键特征。我们的结果和方法将对颗粒物质和无序系统感兴趣，例如在直接力链可视化或力测量不可能的情况下。
