Uranium – molybdenum alloys UMo present excellent thermal properties and a good uranium density. For those reasons, they are considered as nuclear fuel candidates for research reactors. It is therefore crucial for the CEA to deploy new computational methodologies in order to investigate the evolution of their thermo-physical properties under irradiation conditions.

This project is centered on the application of atomistic methods in order to investigate the stability and diffusion of intra-granular xenon clusters within the metallic nuclear fuel UMo.
The first step of your work will involve continuing the development of atomic-scale computational models for UMo, as initiated within the host laboratory. These models use machine learning methods to develop interatomic potentials and will be validated by comparison with existing experimental data for this material. They will then be used to assess the temperature-dependent evolution and the impact of defect accumulation (both point and extended defects) on several thermophysical properties critical to fuel modeling, such as elastic properties, density, thermal expansion, as well as thermal properties like specific heat and thermal conductivity. In collaboration with other researchers in the department, you will format these results for integration into the Scientific Computing Tools used to simulate the behavior of nuclear fuels.

In a second phase, you will be responsible for extending the validity of your models to account for the formation of fission gases such as xenon within UMo single crystals. This will enable you to simulate the stability of xenon clusters in UMo crystals. These calculations, performed using classical molecular dynamics methods, will be systematically compared with experimental observations obtained via transmission electron microscopy.

The results obtained during the various stages of this project will be completely innovative and will be the subject of scientific publications as well as presentations at international scientific conferences. Besides, this work will enable you to complement your training by acquiring skills applicable to many areas of materials science, including ab initio calculations, machine learning-based interatomic potential fitting, classical molecular dynamics, use of CEA supercomputers, and key concepts in statistical physics and condensed matter physics—fields in which the supervising team members are recognized experts.

You will join the Fuel Behavior Modeling Laboratory at the Research Institute for Nuclear Systems for Low-Carbon Energy Production (IRESNE, CEA Cadarache), a dynamic research team where you will have regular opportunities to interact with fellow PhD students and researchers. This environment also provides extensive opportunities for national and international collaboration, including with:
• Developers and users of the MAIA fuel performance code (dedicated to research reactor fuel studies),
• Experimental researchers from the Nuclear Fuel Studies Department,
• Teams from other CEA centers (Saclay, CEA/DAM),
• International partners.
This rich and multidisciplinary context will enable you to fully engage with the scientific community focused on nuclear materials science.

[1] Dubois, E. T., Tranchida, J., Bouchet, J., & Maillet, J. B. (2024). Atomistic simulations of nuclear fuel UO2 with machine learning interatomic potentials. Physical Review Materials, 8(2), 025402.
[2] Chaney, D., Castellano, A., Bosak, A., Bouchet, J., Bottin, F., Dorado, B., ... & Lander, G. H. (2021). Tuneable correlated disorder in alloys. Physical Review Materials, 5(3), 035004.