Abstract
MgCl2–NaCl–KCl (MgNaK) eutectic salt has emerged as a highly promising candidate for concentrated solar power (CSP), with extensive research conducted in recent years. However, the thermal property data for the MgNaK eutectic salt are still very limited. Here, we present a novel and accurate many-body potential trained by machine learning (ML) for MgNaK eutectic salt (with mole fractions of 45.4 %, 33 %, and 21.6 % respectively), developed using energies and forces extracted from first-principles molecular dynamics calculations (FPMD). The performance of the potential is well tested. We conduct a detailed investigation on the temperature-dependent structural and corresponding thermal properties, analyzing them from both the atomic and electronic perspectives. The introduction of Na+ or K+ ions disrupts the net structure formed by corner-joint and edge-joint MgClx units, thereby affecting the transport properties. The calculated density (ρ) and constant pressure specific heat capacity (CP) exhibit agreement with experimental data within a margin of 2 %. We observe and discuss the typical λ of negative linear temperature correlation, which is similar to other molten alkali chloride salts. Finally, based on the simulated and experimental values, we provide reliable recommendations for the λ and viscosities (η) of MgNaK eutectic salt across its entire operating temperature range.
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