| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Microstructural, Mechanical, and Chemical Behavior of Solid Nuclear Fuel and Fuel-Cladding Interface II
|
| Presentation Title |
Deep Learning Potential for Exploring High‑Temperature Properties of Yttrium Hydride |
| Author(s) |
Yuqing Huang, Jacob Eapen |
| On-Site Speaker (Planned) |
Yuqing Huang |
| Abstract Scope |
Yttrium hydride (YH2) has emerged as a promising material for the next-generation microreactors. The mechanism of hydrogen transport, however, remains poorly understood. Our previous study using ab initio molecular dynamics (AIMD) simulations has revealed a correlated diffusive motion of hydrogen atoms in YH2. Due to the high computational cost of AIMD simulations, investigations have been limited to small systems. In this study, we employ the DeePMD toolkit to develop a deep learning potential for YH2 trained on AIMD data from small systems. Classical molecular dynamics simulations are then conducted using this potential on larger systems with several thousands of atoms for sub and super stoichiometric systems. Our simulations capture and conform the quasi-one-dimensional string-like displacements above 750K for YH2. Interestingly, for sub-stoichiometric systems, string-like formation diminishes although with a small activation energy. We rationalize our results from a thermal jamming perspective in close-packed structures that allow superionic-like diffusion in materials. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Modeling and Simulation, Nuclear Materials |