| Abstract Scope |
Molten salts have attracted interest as a potential heat carrier and/or fuel dissolver in developments of new Gen IV reactor designs. Those containing lithium and fluoride-based compounds are of particular interest due to their affinity to lower melting points of mixtures and their compatibility with alloys. A molecular dynamics study is performed on two popular molten salts, namely LiF (50% Li) and FliBe (66% LiF - 33% BeF2), to predict properties, namely density, specific heat, thermal conductivity, and shear viscosity. Due to the large possibilities of atomic environments, we employ training using the Deep Potential (DeepPot) neural networks to learn from large DFT datasets of 118,115 structures with 70 atoms each for LiF and 222,903 structures with 91 atoms each for FLiBe molten salts. These networks are then deployed in fast molecular dynamics to predict dynamic properties that become apparent at longer time-scales, and are otherwise difficult to achieve with man-made potentials, ab-initio, or with experiments. |