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Machine-Learning-Driven Simulations on Microstructure and Thermophysical Properties of MgCl₂-KCl Eutectic.

Wenshuo Liang^1,2, Guimin Lu^1,2, Jianguo Yu^1,2

¹School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China.

ACS Applied Materials & Interfaces

|January 12, 2021

Summary

A new machine learning model combining deep potential and molecular dynamics accurately predicts MgCl2-KCl eutectic properties. This method overcomes limitations of ab initio calculations for large-scale simulations, revealing insights into microstructure and thermophysical behavior.

Keywords:

MgCl2−KCl eutectic deep potential machine learning microstructure thermophysical properties

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Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

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Area of Science:

Computational Materials Science
Physical Chemistry
Machine Learning in Materials

Background:

Ab initio calculations (Density Functional Theory - DFT) are crucial for MgCl2-KCl eutectic studies but limited by computational cost for large-scale and long-time simulations.
Accurate prediction of thermophysical properties and microstructure evolution requires methods that overcome these computational barriers.

Purpose of the Study:

To develop a more efficient and accurate computational scheme for studying the MgCl2-KCl eutectic.
To investigate the microstructure and thermophysical properties of the MgCl2-KCl eutectic using the developed method.
To provide a deeper understanding of the Mg2+ ion coordination, medium-range order, and transport properties.

Main Methods:

Constructed a deep potential (DP) using ab initio calculations to describe interatomic interactions.
Employed molecular dynamics (MD) simulations powered by the DP for higher efficiency and comparable accuracy to DFT.
Analyzed structural evolution using partial radial distribution functions, coordination numbers, angular distribution functions, and structural factors.

Main Results:

The DP-based MD simulations successfully predicted thermophysical properties (density, thermal expansion, viscosity, diffusion, specific heat) in agreement with experimental data.
Revealed that Mg2+ ions exhibit a distorted tetrahedral coordination, not octahedral, and possess a strong local structure.
Observed medium-range order in the microstructure, which is enhanced at higher temperatures.

Conclusions:

The combined ab initio-DP-MD approach offers a computationally efficient alternative to pure DFT for complex material systems.
The study provides valuable insights into the structural characteristics and thermophysical behavior of the MgCl2-KCl eutectic.
Enhanced understanding of ion coordination and diffusion mechanisms, crucial for molten salt applications.