Abstract: As one of the hot spots in virtual process engineering research in materials, the molecular dynamics of silicon crystal is simulated using Tersoff multi-body potential. In the multi-body potential, the interaction between particles is computationally intensive, and there are dependencies between data. Efficient and accurate large-scale simulation on parallel architectures faces two challenges: write conflict and low computational efficiency. To address these issues, a series of optimization methods tailored for silicon crystal molecular dynamics applications have been implemented based on the OpenMP shared-memory programming model to enhance simulation efficiency: (1)In the process of large-scale thread-level parallel simulation, the spatial decomposition graph coloring idea is used to eliminate the data dependence between particles and effectively solve the problem of write conflict； (2)For the core computing program segment, the overall vectorization is used to improve the computational efficiency, and the series estimation is used to realize the transcendental function to achieve the parallel optimization of multi-body potential Tersoff on multi-core processors. The experimental results show that the Tersoff potential has good optimization potential on the X86 platform, and the spatial decomposition graph coloring and vectorization are feasible and scalable for silicon crystal application, which can effectively solve the write conflict caused by data intersection and computationally intensive optimization problems, and the speedup of final result can reach 23.17. 

Key words: silicon crystal, molecular dynamics simulation, spatial decomposition coloring, vectorization, OpenMP