Revealing the complex structure of molten FLiBe (2LiF−BeF2) by experimental x-ray scattering, neutron scattering, and deep neural network-based molecular dynamics

Abstract

The use of molten salts as coolants, fuels, and tritium breeding blankets in the next generation of fission and fusion nuclear reactors benefits from furthering the characterization of the molecular structure of molten halide salts, paving the way to predictive capability of chemical and thermo-physical properties of molten salts. Due to its neutronic, chemical, and thermo-chemical properties, 2LiF−BeF2 is a candidate molten salt for several fusion and fission reactor designs. We perform neutron and X-ray total scattering measurements to determine the atomic structure of 2LiF−BeF2. We also perform ab-initio and neural network molecular dynamics simulations to predict the structure obtained by neutron and X-ray diffraction experiments. The use of machine learning provides improvements to the efficiency in predicting the structure at a longer length scales than is achievable with ab-initio simulations at significantly lower computational expense while retaining near ab-initio accuracy. The comparison among experimental and modeling results at a higher resolution and efficiency than previous measurements provides the opportunity to explore the structural determination of 2LiF−BeF2 beyond the first-nearest neighbor analysis that had been previously achieved with X-ray diffraction measurements of a FLiBe melt. This work may serve as a reference for future studies of salt structure and macroscopic properties with and without the addition of solutes.