Comparing and assessing the thermophysical and structural predictions of an Empirical and a Machine-learning interatomic potentials on liquid (U,Zr)

Liquid uranium-zirconium (U,Zr) mixtures play a crucial role in the context of nuclear accident scenarios, particularly in the early stages of Pressurized-Water Reactor accidents. In this study, we compare the thermophysical and structural predictions of two interatomic potentials for this system, namely a Modified-Embedded Atom Model (MEAM) semi-empirical interatomic potential and a Spectral Neighbor Analysis Potential (SNAP). These models are employed to investigate the relationship between the viscosity, density, and structural properties of liquid (U,Zr) mixtures, and compare their respective predictions. More specifically, the MEAM potential predicts a significant viscosity anomaly at a molar fraction of approximately 70% of Zr. A thorough structural analysis leveraging radial distribution functions and structure factors, Voronoi tessellation, average degree of five-fold local symmetry, and common neighbor analysis supports these findings and attributes them to the formation of an icosahedral short-range order. In contrast, this structural ordering is not observed with the ab initio and SNAP-based computations. We finally analyze those differences and suggest that the semi-empirical MEAM potential may overestimate ordering effects in the liquid phase. These new results can play a role in the refinement of nuclear fuel models, improving the available recommendations for in-vessel corium retention simulations aimed at mitigating severe accident scenarios.