Modeling fission product nucleation in molten NaCl using universal machine-learning potentials

Abstract

The design of molten salt nuclear reactors (MSRs) requires a detailed understanding of the formation, precipitation, and transport of solid fission products in the fuel salt. However, the experimental characterization of these processes remains challenging and expensive. Molecular simulations can be leveraged to obtain insight into these systems. In particular, machine-learning potentials (MLPs) offer a promising path to obtain relevant insight at an acceptable computational cost. Here, we use a pre-trained foundation MLP to study Ru and Mo atoms in NaCl as a model system for solid fission products in MSR fuel salts. We find the MLP shows good agreement with ab initio calculations for isolated atoms in the salt and allows for sufficiently long simulations to observe their nucleation into nanoparticles and study the local structure around them. This approach provides a practical framework for studying fission-product behavior in molten salts relevant to MSR design.