Structure and properties of the FLiNaK – LaF3 melt obtained with neural network potential

Abstract

Accurate and efficient prediction of the thermochemical properties of the melts applicable in molten salt reactors could be made if the proper machine learning model is used. In this paper, the neural network potential for simulation of 85 % (LiF – NaF – KF)eut. – 15 % LaF3 molten mixture was developed based on ab initio data. In spite of multiple atomic types, the model of a moderate size showed small root mean squared errors in energy and forces of 0.5 meV/atom and 39 meV/Å, respectively. Then the neural network potential was employed to calculate local structure, density, self-diffusion coefficients, heat capacity, thermal conductivity, and thermal diffusivity for a range of temperatures. We found that the addition of LaF3 to the eutectic mixture of alkali fluorides results in a reduction the melt ability to store and transfer heat. The strong effect observed here is the reduction in heat capacity by 20–30 %. The analysis of the local structure details reveals the existence of [LaF6], [LaF7] and [LaF8] groupings, with the most probable being [LaF7] and the La – F separation averaged over the ensemble of 2.35 Å.