About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
The interp5DOF Matlab Toolbox: Grain Boundary Energy Models and Uncertainty Quantification |
Author(s) |
Oliver Johnson, Sterling G Baird, Eric R Homer, David T Fullwood, Gus Hart |
On-Site Speaker (Planned) |
Oliver Johnson |
Abstract Scope |
Leveraging our recently developed Voronoi fundamental zone (VFZ) framework, and Matlab Toolbox for Bayesian inference of grain boundary (GB) structure-property models (interp5DOF), we develop fully-anisotropic (5D) models for GB energy in Ni, Al, and Fe, with quantified uncertainty (UQ). We demonstrate computationally efficient methods to enforce physical constraints (crystallographic symmetry, no-boundary singularity, non-negativity, etc.) in the resulting models, and compute the crystallographic distance between GBs. We evaluate GB energy correlation lengths, and find them to be incredibly consistent across materials and crystal systems. Using these models, we identify pairs of GBs that are connected by low-energy pathways through the GB energy landscape, and which may influence microstructural evolution in ways that have not previously been investigated. Finally, we discuss the potential use of these models in mesoscale simulations, similar to the way that interatomic potentials are employed in atomistic simulations. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Computational Materials Science & Engineering, Other |