About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
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| Symposium
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Chemistry and Physics of Interfaces
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| Presentation Title |
Atomistic and Machine Learning Investigations of Grain Boundary Segregation and Migration in Mg Alloys at Finite Temperatures
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| Author(s) |
Vaidehi Menon, Liang Qi |
| On-Site Speaker (Planned) |
Liang Qi |
| Abstract Scope |
This talk presents recent advances in modeling solute segregation and grain boundary (GB) migration in Mg alloys. Thermodynamic integration was used to compute finite-temperature segregation free energies for Y at representative GB sites, and a machine learning framework with physics-informed descriptors extended these results across large GB datasets. A spectral segregation model revealed that experimentally observed Y segregation reflects high-temperature equilibrium states due to slow solute diffusion. Additionally, systematic atomistic simulations of GB migration under shear-coupled stress showed strong anisotropy in GB mobility. Surrogate models identified key structural descriptors governing mobility trends. Remarkably, non-equilibrium vacancies were observed near migrating GBs, suggesting a vacancy-mediated migration mechanism. Coupled solute-vacancy diffusion revealed a Kirkendall effect that alters solute distribution during migration, offering insights into solute drag and texture evolution. These results collectively establish a new framework for understanding solute-GB interactions in polycrystalline Mg alloys under processing conditions. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Modeling and Simulation, Magnesium, Machine Learning |