About this Abstract |
Meeting |
2025 TMS Annual Meeting & Exhibition
|
Symposium
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Computational Thermodynamics and Kinetics
|
Presentation Title |
Molecular Dynamics Investigation of Short-Range Chemical Ordering in the MoTaW Refractory Complex Concentrated Alloy Compositional Space Using Machine Learning Potential |
Author(s) |
Jonathan A. McGill, Yi Yao, Lin Li |
On-Site Speaker (Planned) |
Jonathan A. McGill |
Abstract Scope |
Refractory complex concentrated alloys (RCCAs) are an extensively researched group of alloys that promote strength in extreme environments. Short-range chemical ordering (SRCO) is believed to enhance mechanical properties of RCCAs, making it critical to understand the formation mechanism of SRCO structures. This study employs atomistic simulations with a hybrid molecular dynamics/Monte-Carlo method and machine learning potential to identify chemical order-disorder transition temperatures and melting points of 30+ compositions for the ternary MoTaW system. The machine learning SNAP potential, originally based on MoNbTaW quaternary alloys, is adapted to the ternary MoTaW system. Predictions reveal that the system forms Mo-Ta B2 SRCO structures at sufficiently low annealing temperatures and an A2 phase at higher temperatures. These transition temperatures will provide insights into potential annealing temperatures and cooling rates to promote ordering both thermodynamically and kinetically. The study offers valuable guidance for designing and preparation of high-performance RCCAs in an experimental setting. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Modeling and Simulation, Phase Transformations |