About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Chemistry and Physics of Interfaces
|
| Presentation Title |
Atomistic Modeling of Ti-V Interfaces: Structure, Stability, and Migration via Machine Learning Potentials |
| Author(s) |
Tongqi Wen, Beilin Ye, Che Fan, David J. Srolovitz |
| On-Site Speaker (Planned) |
Tongqi Wen |
| Abstract Scope |
We present an atomistic study of interface properties in Ti-V alloys using a deep learning interatomic potential developed to accurately capture the energetics and structural features of hexagonal close-packed (HCP), body-centered cubic (BCC), and ω phases. The potential is trained on a comprehensive dataset that spans a wide compositional and structural range, enabling precise prediction of phase stability across temperature and composition. Using this potential, we investigate the atomic structures and energetics of various phase interfaces, revealing their thermodynamic stability and structural transitions under different thermal and mechanical environments. We further explore the kinetics of interface migration, identifying key pathways and mechanisms that govern phase transformation behavior in Ti-V alloys. Our findings provide atomistic insights into the role of interface structure in phase evolution and offer a predictive framework for understanding microstructural stability in multiphase alloys, with implications for the design of advanced titanium-based materials. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Mechanical Properties, Titanium |