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

2025-12-07

AI Summary

Existing ODMN frameworks require separate training for each crystal texture, exhibiting poor generalizability. To address this, we propose TACS-GNN-ODMN—a novel framework enabling generalized prediction of polycrystalline mechanical response and texture evolution across diverse microstructures without retraining. Our method introduces Texture-Adaptive Clustering Sampling (TACS) to initialize model parameters, integrates a Graph Neural Network (GNN) to capture orientation-dependent intergranular interactions, and preserves the physics-driven architecture of ODMN. The resulting framework ensures both physical interpretability and cross-texture transferability. Quantitative evaluation demonstrates prediction accuracy comparable to direct numerical simulation across multiple textures, while achieving substantial computational speedup. This enables efficient multiscale simulation and accelerated design of novel metallic materials.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• Generalizes orientation-aware deep material network across textures
• Predicts nonlinear mechanical responses without texture-specific retraining
• Enhances surrogate model for multiscale polycrystal simulations

Innovation

• Texture-Adaptive Clustering initializes texture parameters
• Graph Neural Network predicts stress-equilibrium parameters
• Framework eliminates retraining for diverse crystallographic textures