About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
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Grain Boundaries, Interfaces, and Surfaces: Fundamental Structure-Property-Performance Relationships
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Presentation Title |
E-8: Theoretical and Machine Learning Studies of Grain Boundary Segregation and Solute Drag Effects |
Author(s) |
Malek Alkayyali |
On-Site Speaker (Planned) |
Malek Alkayyali |
Abstract Scope |
The preferential segregation of elemental species to grain boundaries (GBs) affects many GB related phenomena. Of particular interest is the impact of GB segregation on GB migration. Experimental observations revealed that GB segregation mitigates grain coarsening and enhances the thermal stability of metallic alloys; however, most studies are focused on the thermodynamic aspect of GB segregation, and the role of the dynamic solute drag remains poorly understood. Herein, we develop a solute drag model in regular solution alloys, which captures the effect of solute-solute interactions and GB structure on boundary segregation. Machine learning tools employing artificial neural networks are utilized to explore the solute drag hyperspace. Furthermore, a universal solute drag-velocity relation is proposed that provides a robust fit for various metallic alloys. Overall, our solute drag treatment provides a predictive tool to rapidly explore the alloy design space for thermally stable metallic alloys. |