Thermal energy storage and utilization has been widely concerned due to the intermittency, renewability, and economy of renewable energy. In this paper, the potential energy function of binary Na2CO3–K2CO3 salt was first constructed using the Deep Potential GENerator (DPGEN) enhanced sampling method. Deep potential molecular dynamics simulations were performed to calculate the thermal properties and structural evolution of binary carbonates. The results show that as the temperature increases from 1073 to 1273 K, the viscosity and thermal conductivity decrease from 5.011 mPa s and 0.502 W/(m K) to 2.526 mPa s and 0.481 W/(m K), respectively. The decrease in viscosity is related to the distance and interaction between the molten salt ions. In addition, the diffusion coefficients, energy barriers, ionic radius, angular distribution function, and coordination number of molten salt were calculated and analyzed. The CO32− exhibits a stable planar triangular structure. The ionic radius of Na+ is smaller than that of K+, which makes Na+ suffer less spatial hindrance during motion and has a higher diffusion coefficient. The energy barriers that Na+ needs to overcome to escape the Coulomb force is greater than that of K+ ions, so molten salt containing Na+ may possess greater heat storage potential. We believe that the potential function constructed with DPGEN enhanced sampling strategy can provide more convincing results for predicting the thermal properties of molten salts. This paper aims to provide a technical route to develop the novel complex molten salt phase change material for thermal energy storage.
Skip Nav Destination
A theoretical study of thermal properties and structural evolution in binary carbonates phase change material: Machine learning-enhanced sampling strategy
Article navigation
14 October 2024
Research Article|
October 08 2024
A theoretical study of thermal properties and structural evolution in binary carbonates phase change material: Machine learning-enhanced sampling strategy

Available to Purchase
Heqing Tian
;
Heqing Tian
a)
(Funding acquisition, Project administration, Writing – original draft, Writing – review & editing)
School of Mechanical and Power Engineering, Zhengzhou University
, Zhengzhou 450001, China
a)Author to whom correspondence should be addressed: [email protected]
Search for other works by this author on:
Wenguang Zhang;
Wenguang Zhang
(Investigation, Software, Validation, Writing – original draft, Writing – review & editing)
School of Mechanical and Power Engineering, Zhengzhou University
, Zhengzhou 450001, China
Search for other works by this author on:
Chaxiu Guo
Chaxiu Guo
(Resources, Supervision)
School of Mechanical and Power Engineering, Zhengzhou University
, Zhengzhou 450001, China
Search for other works by this author on:
Heqing Tian
Funding acquisition, Project administration, Writing – original draft, Writing – review & editing
a)
School of Mechanical and Power Engineering, Zhengzhou University
, Zhengzhou 450001, China
Wenguang Zhang
Investigation, Software, Validation, Writing – original draft, Writing – review & editing
School of Mechanical and Power Engineering, Zhengzhou University
, Zhengzhou 450001, China
Chaxiu Guo
Resources, Supervision
School of Mechanical and Power Engineering, Zhengzhou University
, Zhengzhou 450001, China
a)Author to whom correspondence should be addressed: [email protected]
J. Chem. Phys. 161, 144501 (2024)
Article history
Received:
May 17 2024
Accepted:
August 30 2024
Connected Content
A companion article has been published:
Machine learning reveals properties of molten salts
Citation
Heqing Tian, Wenguang Zhang, Chaxiu Guo; A theoretical study of thermal properties and structural evolution in binary carbonates phase change material: Machine learning-enhanced sampling strategy. J. Chem. Phys. 14 October 2024; 161 (14): 144501. https://doi.org/10.1063/5.0219401
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Citing articles via
CREST—A program for the exploration of low-energy molecular chemical space
Philipp Pracht, Stefan Grimme, et al.
DeePMD-kit v2: A software package for deep potential models
Jinzhe Zeng, Duo Zhang, et al.
Related Content
Machine learning reveals properties of molten salts
Scilight (October 2024)
Crystallization blockage in highway tunnel drainage system based on molecular dynamics
AIP Advances (March 2025)
Shear viscosity of molten alkali halides from equilibrium and nonequilibrium molecular-dynamics simulations
J. Chem. Phys. (June 2005)
On the intermolecular vibrational coupling, hydrogen bonding, and librational freedom of water in the hydration shell of mono- and bivalent anions
J. Chem. Phys. (October 2014)
Amorphous-dominated MgO hollow spheres enhanced fluoride adsorption: Mechanism analysis and machine learning prediction
J. Chem. Phys. (January 2025)