| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Environmental Degradation of Multiple Principal Component Materials
|
| Presentation Title |
Atomistic Exploration of Light-weight Refractory High Entropy Alloys by Promoting Short-range Chemical Order Using a Machine Learning Potential |
| Author(s) |
Yao Yi, Xiaoxiang Yu, Qiang Zhu, Lin Li |
| On-Site Speaker (Planned) |
Lin Li |
| Abstract Scope |
Refractory high-entropy alloys (RHEAs) emerge as promising high-temperature structural materials due to their remarkable strength. However, the heavy-weight elements, such as W, in the RHEAs could hinder their space application that requires reduced payload weight. Here, atomistic simulations using a machine learning potential are used to explore various non-equiatomic MoTaNbW quinary alloys, focusing on the influence of composition on the chemical short-range order (CSRO), dislocation, and mechanical strength. Annealing the chemically random cells using the Monte Carlo approach reveals that the Mo-Ta pair is favored in all the selected alloys, and an increase in Nb concentration promotes the Mo-Ta pairs and the MoTa B2 unit structure. The enhanced CSRO leads to an increase in diffuse antiphase boundary energies and the critical stress to move the dislocations. In combination with a solid solution strengthening model, the simulation results predict a promising direction in exploring the compositional space of light-weight RHEAs. |
| Proceedings Inclusion? |
Planned: |