What is it about?
In this paper, machine learning enhanced sampling strategy is used to construct the deep potential energy function of Na2CO3-K2CO3 binary eutectic molten salt phase change material, and the deep potential energy molecular dynamics simulation of the thermal properties and structural evolution of the binary carbonate is carried out.
Featured Image
Photo by Timo Volz on Unsplash
Why is it important?
There are some problems in classical molecular dynamics simulation of polycarbonate molten salt, such as low efficiency and poor accuracy. The introduction of machine learning technology combines the precision of first-principles calculation with the simulation scale of classical molecular dynamics, providing a new solution for the study of the structure and thermal properties of polycarbonate molten salts.
Perspectives
In the face of the challenges of global energy structure transformation and climate change, the breakthrough of renewable energy technology has become the key. Carbonate has attracted much attention in the field of energy storage and conversion because of its good ion conductivity and high temperature stability.
Dr. Heqing Tian
Zhengzhou University
Read the Original
This page is a summary of: A theoretical study of thermal properties and structural evolution in binary carbonates phase change material: Machine learning-enhanced sampling strategy, The Journal of Chemical Physics, October 2024, American Institute of Physics,
DOI: 10.1063/5.0219401.
You can read the full text:
Resources
Contributors
The following have contributed to this page
