A theoretical study of thermal properties and structural evolution in binary carbonates phase change material: Machine learning-enhanced sampling strategy.

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.

Development of NaCl-MgCl-CaCl Ternary Salt for High-Temperature Thermal Energy Storage Using Machine Learning.

NaCl-MgCl-CaCl eutectic ternary chloride salts are potential heat transfer and storage materials for high-temperature thermal energy storage. In this study, first-principles molecular dynamics simulation results were used as a data set to develop an interatomic potential for ternary chloride salts using a neural network machine learning method. Deep potential molecular dynamics (DPMD) simulations were performed to predict the microstructure and thermophysical properties of the NaCl-MgCl-CaCl ternary salt.