Elucidating the local structure and properties of molten Na2CO3-K2CO3 salts using Machine Learning-Driven molecular dynamics.

• The structure and transport properties of molten Na 2 CO 3 -K 2 CO 3 salts are studied by machine learning molecular dynamics. • Deep potential models are trained on first-principles data and can accurately describe interatomic interactions. • The DP model achieves DFT-level precision, reproducing structural results from FPMD simulations. • Detailed structural information on the radial and angle distributions of ions is introduced. • The DP model is a more accurate predictor of properties than the empirical potential models. Molten Na 2 CO 3 -K 2 CO 3 binary salts have shown promise as electrolytes for fuel cell technology. Extensive investigations of the local structure and transport properties of these melts have been conducted via experiments and molecular dynamics (MD) simulations. With advancing machine learning capabilities, deep learning strategies are being widely incorporated into MD to generate highly accurate machine learning (ML) potentials. In this work, first-principle-derived Deep Potential is applied to study Na 2 CO 3 -K 2 CO 3 melts at specified temperatures. A deep neural network acts to fit the first-principle data and produce the model. This model enables large-scale MD simulations with accuracy comparable to density functional theory (DFT). The local structure is analyzed. Temperature and concentration impacts on ion arrangements are observed. The concentration effect has little influence on the melt structure, but the radial and angular distributions between the ions are strongly affected by temperature. Density, viscosity, and diffusion values predicted by the DP model agree closely with experimental measurements. In comparison to conventional empirical potentials, the DP model demonstrates DFT-level accuracy for structural analysis and experimental-level property predictions. This study provides detailed insights into the microstructure and transport properties of Na 2 CO 3 -K 2 CO 3 melts via the power of the DP model, showcasing their ability to advance understanding of complex molten salt systems. Meanwhile, a molten salt database is initially constructed using machine learning strategies. [ABSTRACT FROM AUTHOR]