Crystallographic Texture-Generalizable Orientation-Aware Interaction-Based Deep Material Network for Polycrystal Modeling and Texture Evolution

Machine learning has significantly advanced materials modeling by enabling surrogate models that achieve high computational efficiency without compromising predictive accuracy. The Orientation-aware Interaction-based Deep Material Network (ODMN) is one such framework, in which a set of material nodes represents crystallographic textures, and a hierarchical interaction network enforces stress equilibrium among these nodes based on the Hill-Mandel condition. Using only linear elastic stiffness data, ODMN learns the intrinsic geometry-mechanics relationships within polycrystalline microstructures, allowing it to predict nonlinear mechanical responses and texture evolution with high fidelity. However, its applicability remains limited by the need to retrain for each distinct crystallographic texture. To address this limitation, we introduce the TACS-GNN-ODMN framework, which integrates (i) a Texture-Adaptive Clustering and Sampling (TACS) scheme for initializing texture-related parameters and (ii) a Graph Neural Network (GNN) for predicting stress-equilibrium-related parameters. The proposed framework accurately predicts nonlinear responses and texture evolution across diverse textures, showing close agreement with direct numerical simulations (DNS). By eliminating the requirement for texture-specific retraining while preserving physical interpretability, TACS-GNN-ODMN substantially enhances the generalization capability of ODMN, offering a robust and efficient surrogate model for multiscale simulations and next-generation materials design.