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A molecular dynamics study on local structural features and thermophysical properties of the NaCl-UCl3 molten salt with machine learning potential
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DOI：10.1016/j.molliq.2026.129638.png)
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Abstract
En 中文
• A machine learning interatomic potential (MLIP) was developed to determine the thermophysical properties of NaCl-UCl3 molten salts with near ab initio accuracy across the full compositional range. • The calculated thermophysical properties of NaCl-UCl3 melts are directly relevant to the molten salt reactor design and other nuclear engineering applications. • Oligomerization of UCl species dictates the thermophysical properties of NaCl-UCl3 at different compositions. • NaCl acts as a spacer salt that disrupts the U-Cl-U network and modifies the thermophysical properties of NaCl-UCl3 melt.
Keywords:
machine learning interatomic potential
thermophysical properties
NaCl-UCl3 molten salt
molecular dynamics
oligomerization
Journal
IF:
5.2
Papers:
2.5W
Citations:
9.0W
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