About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Materials Corrosion Behavior in Advanced Nuclear Reactor Environments III
|
| Presentation Title |
Predicting Co-segregation Tendency of Cesium and Tellurium via Ab Initio and Machine Learning Methods |
| Author(s) |
Ho Lee, Kwanghee Lee, Sangtae Kim |
| On-Site Speaker (Planned) |
Ho Lee |
| Abstract Scope |
Grain boundary segregation of fission products influences the integrity of austenitic stainless steels in molten salt reactor environments. Especially, the co-presence of cesium and tellurium, two major fission products of MSRs, embrittles AISI 316, whereas Cs alone does not. Here, we employ density functional theory calculations to compute the co-segregation energies of Cs-Te in four γ-Fe symmetric tilt GBs, along with individual segregation energies of Cs and Te. From each Cs and Te segregated structures, we extracted physical descriptors, including local atomic-environment metrics, elastic contributions and electronic features. These descriptors were employed to train a gradient-boosting machine-learning model to predict co-segregation energies in unseen γ-Fe GBs. The trained model reveals that changes in local atomic-environment induced by Cs segregation influences co-segregation behavior of Cs-Te mixtures. These findings provide a computationally efficient framework for designing Fe-based alloys that mitigate loss of ductility due to interactions between fission products and structural alloys. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Machine Learning, Iron and Steel |