Machine-Learning-Driven Simulations on Microstructure and Thermophysical Properties of MgCl₂–KCl Eutectic

Abstract

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.

Short- to Intermediate-Range Structure, Transport, and Thermophysical Properties of LiF–NaF–ZrF4 Molten Salts

“…Weighted least square regression was used to obtain a linear trend between specific enthalpies and temperature for each composition. A similar linear trend in specific enthalpies and temperature for short temperature ranges (200 °C-300 °C) at higher temperatures (>T melt ) was noticed in previous literature [13,[39][40][41][42][43], while a nonlinear relationship was obtained when fitting to a wider temperature range [37,38,44,45]. For composition C, a second-order polynomial fit better described the specific enthalpies and temperature trend in temperature range of 700 °C-900 °C (Figure 11C).…”

Deep potentials for materials science

“…Other DPs were developed to calculate transport properties of silicate in the mantle [144][145][146]. DPs were also employed in large-scale calculations of thermodynamic, transport, and structural properties in different molten salts [149][150][151][152][153][154][155][156][157].…”

Coordination and Thermophysical Properties of Transition Metal Chlorocomplexes in LiCl–KCl Eutectic

“…In addition, the Behler–Parrinello NN (BP-NN) model uses hand-crafted local symmetry functions as descriptors, requiring human intervention. The recent studies using the ML force field (ML-FF) on molten salt systems showed that the predicted local structure, thermophysical properties, and transport properties of some alkaline agree well with the AIMD and experimental results, − suggesting that these properties can be accurately predicted using the ML-FF-based MD simulations.…”