About this Abstract |
| Meeting |
2027 TMS Annual Meeting & Exhibition
|
| Symposium
|
Modeling, AI Applications, and Method Development in Nuclear Materials
|
| Presentation Title |
Machine Learning and Calphad Guided Exploration of Low-Neutron-Cross-Section Refractory Multi-Principal Element Alloys |
| Author(s) |
Daozheng Li, Marcia Myung Hye Ahn, Xinchang Zhang, Jingfan Yang, Rongjie Song, Wei Xiong |
| On-Site Speaker (Planned) |
Daozheng Li |
| Abstract Scope |
The development of Generation IV nuclear reactors demands cladding materials that combine high-temperature strength with low neutron cross-section. This study presents an integrated Machine Learning and CALPHAD framework to navigate the refractory multi-principal element alloy (MPEA) design space under constraints of Valence Electron Concentration (VEC<4.8) and thermal neutron cross-section (<2.4 barns). Random Forest analysis identified Mo and Al as the dominant solid-solution strengthening elements in BCC MPEAs and guided subsequent high-throughput CALPHAD screening of four candidate compositions. Composition C3 (Al9.48-Mo4.68-Nb20.22-Ti4.61-Zr61.01, at. %) achieved a chemically homogeneous, single-phase BCC structure in the as-cast condition, consistent with Scheil-Gulliver solidification predictions, and exhibited a Vickers hardness of 441HV and a localized estimated yield strength of approximately 1970 MPa from nanoindentation reverse analysis. The resulting neutron cross-section of #C3 (0.77 barns) was approximately one-third that of FeCrAl alloys, with the strength-to-neutron cross-section ratio equivalent to Zircaloy-4. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Nuclear Materials, Machine Learning, ICME |