Theoretical studies on the MgCl2–KCl eutectic
heavily rely on ab initio calculations based on density functional
theory (DFT). However, neither large-scale nor long-time calculations
are feasible in the framework of the ab initio method, which makes
it challenging to accurately predict some properties. To address this
issue, a scheme based on ab initio calculation, deep neural networks,
and machine learning is introduced. By training on high-quality data
sets generated by ab initio calculations, a deep potential (DP) is
constructed to describe the interaction between atoms. This work shows
that the DP enables higher efficiency and similar accuracy relative
to DFT. By performing molecular dynamics simulations with DP, the
microstructure and thermophysical properties of the MgCl2–KCl eutectic (32:68 mol %) are investigated. The structural
evolution with temperature is analyzed through partial radial distribution
functions, coordination numbers, angular distribution functions, and
structural factors. Meanwhile, the estimated thermophysical properties
are discussed, including density, thermal expansion coefficient, shear
viscosity, self-diffusion coefficient, and specific heat capacity.
It reveals that the Mg2+ ions in this system have a distorted
tetrahedral geometry rather than an octahedral one (with vacancies).
The microstructure of the MgCl2–KCl eutectic shows
the feature of medium-range order, and this feature will be enhanced
at a higher temperature. All predicted thermophysical properties are
in good agreement with the experimental results. The hydrodynamic
radius determined from the shear viscosity and self-diffusion coefficient
shows that the Mg2+ ions have a strong local structure
and diffuse as if with an intact coordination shell. Overall, this
work provides a thorough understanding of the microstructure and enriches
the data of the thermophysical properties of the MgCl2–KCl
eutectic.