About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Thermodynamics and Kinetics
|
Presentation Title |
Exploring Short-range Order and Phase Stability of CrCoNi Using Machine Learning Potentials |
Author(s) |
Sheuly Ghosh, Vadim Sotskov, Alexander Shapeev, Joerg Neugebauer, Fritz Koermann |
On-Site Speaker (Planned) |
Joerg Neugebauer |
Abstract Scope |
Solid solutions of multicomponent alloys are often assumed to be ideally random. However, short-range-order, which is challenging to quantify by experiments, is known to affect the phase stability and mechanical properties of these alloys. It is therefore important to quantify the degree of local chemical ordering as function of temperature and its chemical nature.
In the present work, we have investigated short-range-order and its impact on phase stability in CrCoNi medium-entropy alloy. This alloy is known for its cryogenic damage tolerance and general mechanical superiority. For this purpose, we have employed a recently proposed computationally efficient on-lattice machine-learning interatomic potential called low-rank potentials. These potentials, which fully account for atomic relaxations, are capable of accurately representing interactions in a system with many chemical components and are used in subsequent Monte Carlo simulations. The computed short-range-order parameters and observed ordering are discussed in view of recent simulation and experimental works. |
Proceedings Inclusion? |
Planned: |