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

In this study, we systematically investigated the phase transition characteristics of alkali metal chloride salts through a deep potential molecular dynamics (DPMD) strategy. The melting points are determined using the superheating-supercooling hysteresis method, while the phase transition behavior is comprehensively analyzed through the radial distribution function (RDF), mean-square displacement (MSD), self-diffusion coefficient (<i>D</i>), coordination number (CN), and molecular dynamics trajectory. The simulation captures the abrupt changes in the local environment of anions and cations during the melting of a molten salt. Molten salts do not complete phase transitions near the actual melting point. Instead, they undergo long-range ordered and long-range disordered transitions near the superheating and supercooling temperature, respectively. MSD, <i>D</i>, and CN analysis quantitatively demonstrate the hysteresis phenomenon during thermal cycling, accompanied by the recombination of ion coordination structures during the phase transition. This work reveals the solid-liquid phase transformation of molten salt and establishes a reliable computational framework for the high-precision prediction of thermal properties.