Deep-Learning-Driven Prediction Strategy for the Phase Transition Behavior of Alkali Chloride XCl (X = Li, Na, or K)

Area of Science:

• Materials Science
• Physical Chemistry
• Computational Chemistry

Background:

• Alkali metal chlorides are crucial in various industrial applications, necessitating accurate understanding of their thermal properties.
• Phase transition behavior in molten salts is complex and challenging to predict accurately using traditional methods.

Purpose of the Study:

• To systematically investigate the phase transition characteristics of alkali metal chloride salts.
• To establish a reliable computational framework for high-precision prediction of molten salt thermal properties.

Main Methods:

• Deep Potential Molecular Dynamics (DPMD) strategy was employed for simulations.
• Superheating-supercooling hysteresis method was used to determine melting points.
• Analysis included radial distribution function (RDF), mean-square displacement (MSD), self-diffusion coefficient (D), coordination number (CN), and molecular dynamics trajectory.

Main Results:

• Simulations captured abrupt changes in local anion and cation environments during salt melting.
• Molten salts exhibited long-range ordered transitions near superheating and long-range disordered transitions near supercooling.
• MSD, D, and CN analyses quantitatively demonstrated hysteresis during thermal cycling and ion coordination structure recombination.

Conclusions:

• Molten salts undergo phase transitions near superheating and supercooling temperatures, not precisely at the melting point.
• The study reveals the solid-liquid phase transformation dynamics in molten salts.
• A reliable computational framework for accurate prediction of molten salt thermal properties was established.