About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
Deformation Mechanisms, Microstructure Evolution, and Mechanical Properties of Nanoscale Materials
|
Presentation Title |
Theoretical and Machine Learning Studies of Grain Boundary Solute Drag in Nanocrystalline Alloys |
Author(s) |
Fadi Abdeljawad, Malek Alkayyali |
On-Site Speaker (Planned) |
Fadi Abdeljawad |
Abstract Scope |
Even minute amounts of solutes at grain boundaries (GBs) lead to drastic changes to GB dynamics. Of particular interest is how GB solute segregation influences boundary migration in nanocrystalline alloys. While GB segregation has been shown to mitigate grain growth in such alloys, most studies are focused on the thermodynamic aspect of segregation and the role of dynamic solute drag remains poorly understood. Herein, we develop a solute drag model in nanocrystalline alloys, which explicitly accounts for solute-solute interactions in both the bulk and GBs. Further, the model captures at a phenomenological level the impact of GB structure and its influence on segregation behavior. Computational and machine learning studies employing neural networks are used to explore the solute drag hypersurface and identify regimes with maximum drag effects. A universal solute drag-velocity relation is proposed that provides a robust fit for alloys with various chemical interactions and compositions. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Machine Learning, Thin Films and Interfaces |