About this Abstract |
| Meeting |
TMS Specialty Congress 2025
|
| Symposium
|
8th World Congress on Integrated Computational Materials Engineering (ICME 2025)
|
| Presentation Title |
Rapid Structure Prediction of Single-Phase High Entropy Alloys Using Graph Neural Network Based Surrogate Modelling |
| Author(s) |
Nicolas Beaver, Aniruddha Dive, Marina Wong, Keita Shimanuki, Ananya Patil, Anthony Ferrell, Mohsen Kivy |
| On-Site Speaker (Planned) |
Nicolas Beaver |
| Abstract Scope |
In this study, we developed a rapid, reliable and cost-effective methodology via employing a Graph Neural Network based machine learning approach to effectively predict the crystal structures of single-phase high entropy alloys. Our novel approach searches through the enormous potential energy surface (PES) landscape with an aim to identify stable lowest energy crystal structure. Our approach was tested on 132 high entropy alloys, and the predictions were verified with the experimental data in literature. Overall, we achieved ~ 83 % accuracy in correctly predicting the stable crystal structures. Our prediction accuracy easily betters the prediction accuracy based on valence electron concentration (VEC) approach that is widely utilized for crystal structure prediction. Our proposed method was ultimately applied to predict the structure of a novel cobalt-free high-entropy alloy. Our predicted crystal structure of the alloy matched the one characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), and X-ray fluorescence (XRF). |
| Proceedings Inclusion? |
Undecided |