About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Hume-Rothery Symposium: Interface Structure and Properties: Impact on Microstructure Evolution
|
| Presentation Title |
Microstructure of Ultraelastic Chemically Complex Alloys from Machine Learning – assisted Atomistic Simulations |
| Author(s) |
Po-Yu Yang, Chun-Wei Pao |
| On-Site Speaker (Planned) |
Chun-Wei Pao |
| Abstract Scope |
Understanding the microstructures and their impact on the mechanical behavior of chemically complex alloys has been a longstanding challenge. In this study, we develop a machine learning (ML)-based potential energy model that accurately reproduces the energetics and atomic forces of the Co25Ni25(HfTiZr)50 ultra-elastic complex alloy with DFT-level fidelity. This enables large-scale atomistic simulations involving hundreds of thousands of atoms while retaining quantum accuracy. Our results agree well with experiments and, through a combination of molecular dynamics (MD) and nudged elastic band (NEB) calculations, reveal the dislocation core structures and plastic deformation mechanisms that are difficult to access experimentally. These findings provide atomistic insights into the origins of ultra-elasticity in complex alloys. Overall, this work demonstrates that ML-enabled energy models are powerful tools for uncovering the intricate relationships between microstructure and mechanical responses in chemically complex materials. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, High-Entropy Alloys, Machine Learning |