Physics-Informed Graph Neural Networks for Predicting Deformation in Disordered Fibrous Materials.

Journal: Nano letters
Published Date: Dec 26, 2025

Abstract

Disordered fibrous networks play vital mechanical roles but are difficult to model due to their sparse connectivity and complex nonaffine deformation. We introduce a physics-informed, graph-learning-based Network Mechanics Prediction (GNMP) that predicts deformation and stress-strain responses of 2D semiflexible networks with reliable accuracy and >10× efficiency gains over molecular dynamics. GNMP integrates graph-attention message passing, multiscale physical embeddings, and a bond-length-guided scheduler to capture key nonaffine rearrangements. Experiments on Poisson-ratio-aligned 3D-printed networks show consistent deformation motifs, descriptor trends, and comparable log-area changes (∼0.46 vs ∼0.24) while reducing the geometry-inference time to ∼0.13 s per step. GNMP offers a generalizable route for rapid, topology-aware mechanical prediction in fibrous network materials and supports the accelerated design of biomimetic soft tissues, flexible conductors, and related network-based systems.

Authors

  • Shuo Yang
    Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Yunhao Yang
    School of Physical Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
  • Chen Huang
    Department of Pharmacy, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
  • Runnan Bai
    School of Physical Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
  • Leitao Cao
    School of Physical Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China.
  • Jing Ren
    College of Life Science and Engineering, Lanzhou University of TechnologyLanzhou 730050, P. R. China; The Key Lab of Screening, Evaluation and Advanced Processing of TCM and Tibetan Medicine, Education Department of Gansu Provincial GovernmentLanzhou 730050, P. R. China.
  • Shengjie Ling
    School of Physical Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China. [email protected].

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