Deep learning-driven molecular dynamics simulations of molten carbonates: 1. Local structure and transport properties of molten Li2CO3-Na2CO3 system

In recent years, metal carbonate-based melts have been investigated as the promising heat transfer fluid and thermal energy storage medium for concentrating solar power plants. However, there are some limiting factors for investigating their structural information and properties. As the deep neural network develops, machine learning method is widely used in MD simulations. In this paper, interatomic potential driven by machine learning is developed based on datasets generated by first-principle molecular dynamics simulations in order to research the local structure and properties of molten Li2CO3-Na2CO3 binary salts at target temperatures. The machine learning potential enables higher efficiency and similar accuracy relative to DFT and yields precise descriptions of microstructures and properties. Microstructural evolution is analyzed through partial radial distribution functions. We observed lithium cation exhibits more coordination modes and the strength of Na+ with CO32- is weaker than that of Li+. Further, comparing the inter-ionic partial radial distribution function diagrams of binary melts and those of pure melt, interatomic characteristics were obtained. The evolution of properties calculated by machine learning, including density, self-diffusion coefficients, thermal conductivity, and viscosity over the entire operating temperature range, is documented. The relationships between properties and temperature or properties and the fraction of Li2CO3 were explored by the trained machine learning potential. This work exhibits a thorough understanding about the local structure and property of Li2CO3-Na2CO3 melt and reveals the accuracy of machine learning potential on molten binary carbonates for the first time.