| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Local Chemical Ordering and Its Impact on Mechanical Behaviors, Radiation Damage, and Corrosion
|
| Presentation Title |
Predicting diffusion kinetics and its resulting local chemical ordering in compositionally complex materials |
| Author(s) |
Bin Xing, Timothy J. Rupert, Xiaoqing Pan, Penghui Cao |
| On-Site Speaker (Planned) |
Bin Xing |
| Abstract Scope |
Diffusion governs many critical kinetic processes, such as local chemical order formation and precipitate nucleation. Simulating diffusion in compositionally complex materials (CCMs) is challenging due to their inherent high chemical complexity. In this presentation, we will introduce a neural network kinetics (NNK) scheme for efficiently and accurately predicting diffusion kinetics in CCMs. The NNK applies on-lattice representation to fully capture material structure and chemistry, from which artificial neural networks predict path-dependent migration barriers and atomic jumps. Trained on dozens of Nb-Mo-Ta compositions, the framework can predict migration barriers and diffusion kinetics over the entire composition space. Using the method, we reveal a critical temperature where the strongest B2 order forms supported by high diffusion heterogeneity after studying local chemical ordering at various temperatures in equimolar Nb-Mo-Ta alloys. Finally, we shall demonstrate barrier predictions in equimolar quaternary and quinary alloys, indicating the scheme naturally generalizes to more chemically complex materials. |
| Proceedings Inclusion? |
Planned: |