| About this Abstract |
| Meeting |
2024 TMS Annual Meeting & Exhibition
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| Symposium
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Local Ordering in Materials and Its Impacts on Mechanical Behaviors, Radiation Damage, and Corrosion
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| Presentation Title |
Neural Network Potential-based Molecular Dynamics Nanoindentation and Machine Learning-based Kinetic Monte-Carlo Simulations of the Impact of Chemical Order in CrCoNi Medium-entropy Alloy |
| Author(s) |
Jun-Ping Du, Shigenobu Ogata |
| On-Site Speaker (Planned) |
Jun-Ping Du |
| Abstract Scope |
The presence of chemical short-range order (CSRO) in annealed equiatomic CrCoNi medium-entropy alloy (MEA) has been suggested by both experimental and theoretical research. However, it is still an open question about what the atomic structure of CSRO is and its influence on mechanical properties. Here, using the neural network potential for CrCoNi MEA, we find that a chemical ordering transition at 800 K with decreasing annealing temperatures, from the CSRO to the superlattice ordering consisting of Cr(110)/CoNi and Cr(100)/CoNi nano-sized superlattice domains. In addition, the kinetics of chemical ordering evolution by vacancy diffusion is given using kinetic Monte-Carlo simulation with a machine learning-based activation energy prediction technique. The molecular dynamics nanoindentation simulations demonstrate that the CrCoNi MEA with the nano-sized superlattice domains exhibits a higher average first pop-in load than the MEA with CSRO, suggesting that the incipient plasticity of CrCoNi MEA can be tailored by controlling the chemical ordering. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Modeling and Simulation, Machine Learning, Mechanical Properties |