Actinide Molten Salts: A Machine-Learning Potential Molecular Dynamics Study.

Actinide molten salts represent a class of important materials in nuclear energy. Understanding them at a molecular level is critical for the proper and optimal design of relevant technological applications. Yet, owing to the complexity of electronic structure due to the 5f orbitals, computational studies of heavy elements in condensed phases using ab initio potentials to study the structure and dynamics of these elements embedded in molten salts are difficult. This lack of efficient computational protocols makes it difficult to obtain information on properties that require extensive statistical sampling like transport properties. To tackle this problem, we adopted a machine-learning approach to study ThCl-NaCl and UCl-NaCl binary systems. The machine-learning potential with the density functional theory accuracy allows us to obtain long molecular dynamics trajectories (ns) for large systems (10 atoms) at a considerably low computing cost, thereby efficiently gaining information about their bonding structures, thermodynamics, and dynamics at a range of temperatures. We observed a considerable change in the coordination environments of actinide elements and their characteristic coordination sphere lifetime. Our study also suggests that actinides in molten salts may not follow well-known entropy-scaling laws.