| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Bridging Scale Gaps in Multiscale Materials Modeling in the Age of Artificial Intelligence
|
| Presentation Title |
Understanding Microstructural Evolution Using Graph Attention Networks |
| Author(s) |
Elizabeth A. Holm, Ryan Cohn |
| On-Site Speaker (Planned) |
Elizabeth A. Holm |
| Abstract Scope |
Polycrystalline microstructures can be represented as graphs, which capture both grain geometry and connectivity. A number of machine learning (ML) algorithms operate on graph data, raising the question of whether they can be used to predict microstructural evolution in polycrystals. Operating on a data set of Monte Carlo grain growth simulations, we find that a simple graph convolution network outperforms a computer vision approach for predicting the occurrence of abnormal grain growth (AGG) in a model polycrystalline system. In turn, a graph attention network significantly outperforms simple graph convolution, achieving a further 20% reduction in error rate to a level commensurate with the predicted achievable accuracy. Feature importance analysis identifies the grain characteristics associated with AGG. Taken together, these results show the promise of ML for both predicting microstructural outcomes and supporting microstructural science. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation |