About this Abstract |
Meeting |
2025 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Thermodynamics and Kinetics
|
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 |
Computational investigations of chemistry-microstructure relationships require atomistic models that act at the appropriate length scales while capturing chemical-bond intricacies, such as short-range order (SRO). Here we consider various approaches for the construction of training data sets for machine learning potentials (MLPs) for metallic alloys and evaluate their performance in capturing SRO and its effects on thermodynamic quantities of relevance for microstructural evolution. Based on this analysis we systematically derive design principles for the rational construction of MLPs that capture SRO. The resulting approach is demonstrated to have high physical fidelity by comparing the predictions directly to experimental measurements, such as enthalpy of SRO formation and SRO domain size. This enables realistic evaluations of solute partitioning and microstructure evolution during the solidification processes of metallic alloys. |
Proceedings Inclusion? |
Planned: |
Keywords |
High-Entropy Alloys, Solidification, Machine Learning |