About this Abstract |
Meeting |
2024 TMS Annual Meeting & Exhibition
|
Symposium
|
Chemistry and Physics of Interfaces
|
Presentation Title |
Machine Learning Meets Interface Physics: A Case Study of Grain Boundary Solute Segregation |
Author(s) |
Fadi Abdeljawad, Malek Alkayyali |
On-Site Speaker (Planned) |
Fadi Abdeljawad |
Abstract Scope |
Even minute amounts of dopants at grain boundaries (GBs) result in profound changes to GB dynamics, specifically grain growth during processing treatments or under operating conditions. While GB solute segregation has been the subject of active research, GB solute drag remains unexplored. The challenge here is that solute drag depends on several parameters describing alloy thermodynamics and various mass transport processes; solute drag is a hypersurface. Herein, we present recent work that integrates physics-based mesoscale modeling of GB segregation with machine learning to unravel GB drag effects in metallic alloys. Representative results are presented to reveal new insights about the roles of asymmetric GB segregation and solute-solute interactions in drag effects. In broad terms, our modeling approach provides avenues to detangle the thermodynamic and kinetic roles of GB segregation in grain coarsening of a wide range of systems such as nanostructured materials and sintered microstructures. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Computational Materials Science & Engineering, Thin Films and Interfaces |