Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
October 3, 2025Langmuir

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

View Full Paper
Ask AI
Bookmark
Share

Authors

HTHeqing TianTLTianyu LiuXLXiaozheng Lan

Discussion

Loading...

Member takes

Overview

Analysis reveals phase transition behaviors and properties of alkali chloride melts, indicating computational frameworks are reliable.

Key Points

  • Phase transition characteristics of alkali metal chlorides were investigated using deep potential molecular dynamics methods.
  • Melting points were determined through superheating and supercooling hysteresis, revealing insights into the solid–liquid phase transformation.
  • The study quantitatively analyzed behaviors like self-diffusion coefficient and coordination number, supporting findings on ion coordination during phase transitions.
  • The results establish a computational framework that could be crucial for the high-precision prediction of thermal properties in molten salts.

Cite This Study

Tian et al. (2025) studied this question.

synapsesocial.com/papers/68e02f3cf0e39f13e7fa275ehttps://doi.org/10.1021/acs.langmuir.5c03547
View Full Paper
Ask AI
Bookmark
Share