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.

翻译：机器学习通过构建代理模型，在不牺牲预测精度的前提下显著提升了计算效率，从而极大地推动了材料建模的发展。方向感知交互式深度材料网络（ODMN）便是此类框架之一，其中一组材料节点表征晶体学织构，而一个基于Hill-Mandel条件的层级交互网络则在这些节点间强制执行应力平衡。ODMN仅利用线弹性刚度数据，即可学习多晶微观结构内禀的几何-力学关系，从而能够高保真地预测非线性力学响应与织构演化。然而，其应用仍受限于需针对每种不同的晶体学织构进行重新训练。为克服此局限，我们提出了TACS-GNN-ODMN框架，该框架整合了（i）用于初始化织构相关参数的织构自适应聚类与采样方案，以及（ii）用于预测应力平衡相关参数的图神经网络。所提出的框架能够准确预测多种织构下的非线性响应与织构演化，结果与直接数值模拟高度吻合。通过消除针对特定织构的重新训练需求，同时保持物理可解释性，TACS-GNN-ODMN显著增强了ODMN的泛化能力，为多尺度模拟与下一代材料设计提供了一个稳健且高效的代理模型。