The study of the optical phonon frequency of 3C-SiC by molecular dynamics simulations with deep neural network potential
Wei Chen, Liangsheng Li
Abstract
In this work, we implement molecular dynamics (MD) simulations with deep neural network (DNN) potential trained with the datasets from ab initio calculations to determine the dielectric spectra of crystal. The fluctuations of the total dipole moment of crystal, which are obtained from MD, can be directly related to the frequency-dependent permittivity according to the work of Neumann and Steinhauser [Chem. Phys. Lett. 102, 508–513 (1983)]. We generalize their theoretical work to express the permittivity in the form of a tensor and perform MD simulations for cubic silicon carbide (3C-SiC) with 8000 atoms to assess the accuracy. The infrared resonance frequency and the phonon linewidth obtained by the DNN potential are compared with those obtained by the empirical Vashishta potential and experiments. The results of the DNN potential are in good agreement with the experimental measurements. It shows that we can carry out MD simulations for large systems with the accuracy of ab initio calculations to obtain dielectric properties.