Actinides in complex reactive media: A combined ab initio molecular dynamics and machine learning analytics study of transuranic ions in molten salts

Abstract

The predominant ionic chemistry and the similarity in ionic radius of actinides make it very difficult to structurally distinguish them in liquids. To tackle this problem, we investigate actinides in molten salts using ab initio molecular dynamics and machine learning analytics. Ab initio simulations show that the f-states clearly affects the electronic properties while their impact on structural properties is not obvious. For the series of trivalent actinides U3+, Pu3+, Cm3+, Cf3+, and Fm3+ in molten NaCl and FLiBe, actinide-ligand bonds have a higher degree of covalency in NaCl (than in FLiBe), and a higher degree of ionicity in FLiBe. Furthermore, a machine learned classification model can distinguish atomic environments of chemically similar actinides with more than 80% confidence, as long as atoms beyond the first solvation shells are considered. Our work shows that only two types of descriptors are necessary to account for all the fluctuations in heavy metal/molten salt mixtures: The first descriptor represents the electronic state of the heavy metal, while the second encompasses the local coordination environment.