Deep Potential fitting and mechanical properties study of MgAlSi alloy

MgAlSi alloy materials have the main properties of light weight and high strength, good electrical and thermal conductivity and corrosion resistance, and have various applications in the industrial field, making an important contribution to the realization of lightweight and high performance needs. In order to be able to predict the material properties of MgAlSi alloys with a high degree of accuracy, this paper develops for the first time an interatomic potential function for MgAlSi alloys based on a neural network machine learning approach. The effectiveness of the developed machine learning potentials is verified by analyzing the problems encountered during the training process and the errors of the finally obtained potential functions, and comparing some of the radial distribution functions, coordination numbers, and predictions of properties such as the equation of state, lattice constants, shear modulus and bulk modulus with those of AIMD. It is found that the performance error of the deep potential model is basically kept in the same order of magnitude as that of DFT calculations, the computational speed can be up to nearly a thousand times that of DFT, and the computational cost is linearly related to the atomic number, which is well suited for large-scale molecular dynamics simulations, and it will provide a promising solution for accurate large-scale molecular dynamics simulations.