Molecular dynamics simulations on AlCl3-LiCl molten salt with deep learning potential

AlCl3-LiCl molten salt is a promising candidate used in high-temperature batteries as cathode material to promote the development of renewable energy. Properties of AlCl3-LiCl molten salt are scarce, however, accurate and effective prediction from experienced molecular dynamics and ab initio dynamics remains a challenge. A fast and accurate simulation method based on ab initio datasets and deep neural networks, using machine learning technique, was adopted in this work. A deep potential model was constructed and trained to reproduce the energy and force of AlCl3-LiCl molten salt. Deep potential molecular dynamics simulations were carried out to investigate the local structure and properties using the deep potential model. Structural analysis including partial radial distribution function, coordination number distribution and angular distribution function suggests that the coordinated structure of Cl− around Al3+ is a stabilized and regular tetrahedron, these tetrahedrons form a sparse network liquid structure in mixtures mainly through corner-sharing. Meanwhile, properties like density, thermal expansion coefficient, specific heat capacity, self-diffusion coefficient and shear viscosity were discussed. Property discussion reveals that density and shear viscosity shows a negative relationship with temperature, the diffusivity of each ion species in AlCl3-LiCl molten salt mixture follows the order Li+ > Al3+ ≈ Cl− and the diffusivity increases with the rising temperature. This work enriches the fundamental data of property for AlCl3-LiCl molten salt and suggests an effective and accurate approach to other molten salt investigations in the future.