About this Abstract |
Meeting |
2024 TMS Annual Meeting & Exhibition
|
Symposium
|
Local Ordering in Materials and Its Impacts on Mechanical Behaviors, Radiation Damage, and Corrosion
|
Presentation Title |
Capturing Short-range Order in High-entropy Alloys with Machine-learning Potentials |
Author(s) |
Yifan Cao, Killian Sheriff, Rodrigo Freitas |
On-Site Speaker (Planned) |
Yifan Cao |
Abstract Scope |
Capturing chemical short-range order (cSRO) with machine learning potentials (MLPs) is challenging because of the sheer number and complexity of all possible chemical configurations in high-entropy alloys (HEAs). In this work, we propose a generalized strategy to construct first-principles training databases and train MLPs capable of capturing cSRO in HEAs. We demonstrate this strategy by quantifying the MLP performances in reproducing cSRO effects in various properties of CrCoNi, including defect properties and phase stability. |
Proceedings Inclusion? |
Planned: |
Keywords |
High-Entropy Alloys, Machine Learning, Computational Materials Science & Engineering |