About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Advances in Multi-Principal Element Alloys V: Mechanical Behavior
|
| Presentation Title |
Unraveling Temperature-Dependent Dislocation Mechanisms in MoNbTaWV via Machine Learning Interatomic Potentials |
| Author(s) |
Yi Yao, Lin Li |
| On-Site Speaker (Planned) |
Lin Li |
| Abstract Scope |
Refractory complex concentrated alloys (RCCAs) exhibit exceptionally high-temperature strength, yet their underlying dislocation mechanisms remain poorly understood. In this study, we develop a machine-learned Spectral Neighbor Analysis Potential (SNAP) and conduct large-scale atomistic simulations to investigate dislocation and diffusion behaviors in MoNbTaW and MoNbTaWV RCCAs. We find that increasing lattice distortion from the quaternary to the quinary alloy enhances resistance to edge dislocation motion and facilitates screw dislocation cross-slip. This shift promotes a transition in the dominant deformation mechanism from kink-pair nucleation in pure W to cross-kink interactions in RCCAs, thereby reducing thermal softening. However, increased atomic diffusion in the quinary system weakens this strengthening effect at elevated temperatures. These findings suggest that carefully balancing lattice distortion and diffusion is critical for optimizing the high-temperature performance of RCCAs. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
High-Entropy Alloys, Machine Learning, Mechanical Properties |