Microstructure and Thermophysical Property Prediction for Chloride Composite Phase Change Materials: A Deep Potential Molecular Dynamics Study-Reference-Cited by-同舟云学术

Microstructure and Thermophysical Property Prediction for Chloride Composite Phase Change Materials: A Deep Potential Molecular Dynamics Study

Published:2023-03-31 Issue:14 Volume:127 Page:6852-6860
ISSN:1932-7447
Container-title:The Journal of Physical Chemistry C
language:en
Short-container-title:J. Phys. Chem. C

Author:

Zhao C. Y.¹^ORCID,Tao Y. B.¹,He Y.¹

Affiliation:

1. Key Laboratory of Thermo-fluid Science and Engineering, Ministry of Education, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China

Funder

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Surfaces, Coatings and Films,Physical and Theoretical Chemistry,General Energy,Electronic, Optical and Magnetic Materials

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcc.2c08589

Reference38 articles.

1. Heat transfer characteristics and compatibility of molten salt/ceramic porous composite phase change material

2. Selection principles and thermophysical properties of high temperature phase change materials for thermal energy storage: A review

3. A reliable framework to predict the temperature dependent thermal conductivity of multicomponent salt based PCMs in both solid and liquid state

4. A review of performance investigation and enhancement of shell and tube thermal energy storage device containing molten salt based phase change materials for medium and high temperature applications

5. Molecular dynamics simulation of thermophysical properties of NaCl-SiO2 based molten salt composite phase change materials

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Local structure and thermophysical property prediction for CaCl2-KCl molten salt with machine learning potentials;Materials Today Communications;2024-12

2. Minimizing Redundancy and Data Requirements of Machine Learning Potential: A Case Study in Interface Combustion;Journal of Chemical Theory and Computation;2024-07-29

3. Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials;Journal of Applied Physics;2024-04-24