A neural network interatomic potential for molten NaCl

Molten salts have been widely exploited for clean energy applications such as molten salt reactor (MSR) and concentrated solar power (CSP) technologies. These applications impose stringent requirements on the choice of molten salts such as excellent thermophysical properties, stability under extreme conditions, tolerance to impurities as well as compatibility with major structural materials. Optimizing/searching appropriate molten salt systems thus calls for a deep understanding of the underlying molecular structures, chemistry and dynamics in a vast salts space. In this talk, we present the application of artificial neural-network (NN) in training accurate interatomic potentials that enable fast evaluations of salt properties on desired time-and length-scales. In particular, we highlight the feasibility of neural-network interatomic potential to accurately predict the short to medium range structures and thermophysical properties of ionic liquids. We also propose a generic strategy to address the short-range interactions that are generally difficult to be learned by artificial NN.