About this Abstract |
| Meeting |
2027 TMS Annual Meeting & Exhibition
|
| Symposium
|
Modeling, AI Applications, and Method Development in Nuclear Materials
|
| Presentation Title |
Understanding Dislocation Interactions in BCC Fe using Machine-Learned Potentials |
| Author(s) |
Yazhuo Liu, Joshua Kempfert, Shigenobu Ogata, Jessica Yeager, Jonathan Wormald, Ting Zhu |
| On-Site Speaker (Planned) |
Yazhuo Liu |
| Abstract Scope |
Low-alloy steels are widely used as structural materials in nuclear reactors, where irradiation-generated self-interstitial atoms can aggregate into prismatic dislocation loops in the Fe matrix. These defects interact with mobile dislocations, contributing to irradiation hardening and embrittlement. Accurate modeling of dislocation line-loop reactions requires resolving localized atomic rearrangements within multi-million-atom systems. Prior atomistic studies have typically relied on empirical potentials to access the required length scales. To retain this system size while improving the fidelity of dislocation-interaction modeling, we employ a machine-learned potential (MLP) that balances computational efficiency and accuracy. We investigate dislocation line-loop interactions in irradiated Fe and correlate atomic-scale interaction mechanisms with characteristic features in the stress-strain response. This correlation reveals reaction pathways that govern dislocation pinning, loop evolution, and residual-defect formation. These results provide mechanistic insight into irradiation-induced hardening in reactor structural steels and demonstrate the value of MLP-enabled simulations for studying complex irradiation defects. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Nuclear Materials, Iron and Steel, Machine Learning |