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.

It is well-known that surfactants tend to aggregate into clusters or micelles in aqueous solutions due to their special structures, and it is difficult for the surfactant molecules involved in the aggregation to move spontaneously to the oil−water interface. In this article, we developed a new grand-canonical molecular dynamics (GCMD) model to predict the saturated adsorption amount of surfactant with constant concentration of surfactant molecules in the bulk phase, which can prevent surfactants aggregating in the bulk phase and get the atomic details of the interfacial structural change with increase of the adsorption amount through a single GCMD run. The adsorption of anionic surfactant sodium dodecyl sulfate (SDS) at the heptane−water interface was studied to validate the model. The saturated adsorption amount obtained from the GCMD simulation is consistent with the experimental results. The adsorption kinetics of SDS molecules during the simulation can be divided into three stages: linear adsorption stage, transition adsorption stage, and dynamic equilibrium stage. We also carried out equilibrium molecular dynamics (EMD) simulations to compare with GCMD simulation. This GCMD model can effectively reduce the simulation time with correct prediction of the interfacial saturation adsorption. We believe the GCMD method could be especially helpful for the computational study of surfactant adsorption under complex environments or emulsion systems with the adsorption of multiple types of surfactants.