General-purpose moment tensor potential for Ga-In liquid alloys towards large-scale molecular dynamics with ab initio accuracy

Abstract Liquid metals demonstrate significant potential for applications in thermal management and flexible electronic circuits, necessitating a comprehensive understanding of their transport properties for technological advancements. Experimental measurement of these properties presents challenges due to factors like cost, corrosion and impurity control. Consequently, accurate computational simulations become essential for predicting the physical properties of these materials. In this research, molecular dynamics (MD) simulations were employed to model several properties of Gallium (Ga), Indium (In) and Ga-In alloys, including lattice structural parameters, radial distribution functions (RDF), structure factors, self-diffusion coefficients and viscosity. Due to the difficulty of traditional interatomic potentials in capturing the short-range interactions directly related to the mechanical behavior of liquid atoms, machine-learning interatomic potentials (MLIPs) have been constructed to precisely describe the liquid metals Ga, In, and Ga-In alloys. This was achieved by utilizing the moment tensor potential (MTP) framework in combination with an active learning strategy. MTP was trained using a comprehensive database generated from DFT and MD simulations, which include a variety of crystal structures, point defects and liquid structures. The calculations of physical properties in this research have shown strong consistency with experimental data, demonstrating that the MTP can accurately describe the interatomic interactions between Ga-Ga, In-In and Ga-In. Our work has established a novel paradigm for investigating the physical properties of various liquid metal systems, offering valuable insights and references for future research.