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Meeting	2025 TMS Annual Meeting & Exhibition
Symposium	Artificial Intelligence Applications in Integrated Computational Materials Engineering
Presentation Title	Accelerating Crystal Plasticity Simulations with Graph Neural Networks
Author(s)	Kyle Farmer, Elizabeth A. Holm
On-Site Speaker (Planned)	Kyle Farmer
Abstract Scope	Crystal plasticity provides a framework for modeling the mechanical behavior of crystalline materials by predicting deformation at the microstructural level. This approach typically employs finite element analysis (FEA), which is computationally intensive and suffers from poor scalability. Accelerating these simulations is crucial for enabling more efficient and extensive analyses of material behavior, particularly for applications requiring large-scale modeling. To address this, we introduce a flexible Graph Neural Network (GNN) architecture as a surrogate model capable of learning the evolution of material behavior in a stressed environment. As a demonstration, we reproduce the results of Eshelby's elastic inclusion problem and subsequently introduce plasticity into the constitutive relationship. We show the GNN can be trained using data from either a finite element or an analytical solution. This advancement underscores the critical role of GNNs, and deep learning at large, in enhancing the scalability and speed of crystal plasticity simulations.
Proceedings Inclusion?	Planned:
Keywords	Computational Materials Science & Engineering, ICME,

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