Physics-Informed Graph Diffusion Model for Cross-Scale Performance Prediction of Silver Nanowire Percolation Networks.

Silver nanowire (AgNW) networks are promising transparent conductive electrodes (TCEs) for flexible optoelectronics, yet their industrial quality control is hindered by the gap between invisible microstructures and unpredictable macroscopic performance. Herein, we propose a Physics-informed Graph Diffusion Model that injects the physical laws governing TCE conduction into deep learning as an inductive bias. From a single scanning electron micrograph, we construct a topology graph and predict the sheet resistance, Rs. A physics-informed GIN encoder, pretrained on a resistor-network-derived conductance label, provides a robust cold-start representation; a target-posterior diffusion model then supplies a calibrated per-prediction uncertainty rather than performing latent manifold augmentation. The encoder is trained on a multisource graph data set that fuses synthetic percolation models with real SEM micrographs of AgNW films at three sheet-resistance levels, augmented by an external continuous-Rs data set. Under a leakage-free, group-aware evaluation, the two-stage model predicts Rs with a relative error of 7.27% (R