Exploring NaCl-PuCl₃ molten salts with machine learning interatomic potentials and graph theory

Actinide molten salts are the basis of the liquid fuels used in molten salt reactors. Due to the inherent difficulties associated with high temperature and hazardous conditions, experimental investigations of fundamental properties of these materials are usually challenging. In this work, we describe the structure and transport of NaCl-PuCl₃ mixtures using computational techniques. Three compositions were considered (16, 25, and 36 mol% PuCl₃) over a temperature range (730 – 1257 K) using ab initio molecular dynamics, which provided the necessary data sets for training machine learned interatomic potentials. Molecular dynamics simulations based on these potentials were then used to determine structure and transport properties. A substantial change was noted in the structure factor when increasing the PuCl₃ content from 25 to 36 mol%. This change is linked to the aggregation of larger Pu³⁺ clusters. In addition, the similarity of the atomic environments of metal cations in molten salt systems to their solid states counterparts was investigated using an unsupervised learning technique. Finally, graph theory was employed to explore the structure and size of actinide networks. Consistent with the structure factor, a dense Pu³⁺ intermolecular structure is observed within the 36 mol% PuCl₃ mixture. The structure of cation-cation inter-junctions is also discussed. In all cases, the diffusion of Pu³⁺ is significantly lower than that of Na⁺ and Cl⁻.