Thermophysical properties of FLiBe using moment tensor potentials

TL;DR

This study develops a moment tensor potential (MTP) model for FLiBe, enabling accurate and efficient prediction of its thermophysical properties, which are difficult to measure experimentally due to high-temperature challenges.

Contribution

The paper introduces a new MTP-based approach for modeling FLiBe's properties, achieving high accuracy with fewer DFT configurations and faster computation compared to existing machine learning potentials.

Findings

MTP accurately reproduces potential energy surface of FLiBe.

Predicted thermophysical properties agree well with experimental data.

Fitting requires less than 600 configurations and 1200 core hours.

Abstract

Fluoride salts are prospective materials for applications in some next generation nuclear reactors and their thermophysical properties at various conditions are of interest. Experimental measurement of the properties of these salts is often difficult and, in some cases, unfeasible due to challenges from high temperatures, impurity control, and corrosivity. Therefore, accurate theoretical methods are needed for fluoride salt property prediction. In this work, we used moment tensor potentials (MTP) to approximate the potential energy surface of eutectic FLiBe (0.66 LiF 0.33 BeF2) predicted by the ab initio (DFT D3) method. We then used the developed potential and molecular dynamics to obtain several thermophysical properties of FLiBe, including radial distribution functions, density, self-diffusion coefficients, thermal expansion, specific heat capacity, bulk modulus, viscosity, and thermal conductivity. Our results show that the MTP potential approximates the potential energy surface accurately and the overall approach yields very good agreement with experimental values. The converged fitting can be obtained with less than 600 configurations generated from DFT calculations, which data can be generated in just 1200 core hours on today's typical processors. The MTP potential is faster than many machine learning potentials and about one order of magnitude slower than widely used empirical molten salt potentials such as Tosi Fumi.