  We developed and validated an accurate inter-atomic potential for molecular
dynamics simulation in cubic silicon carbide (3C-SiC) using a deep learning
framework combined with smooth Ziegler-Biersack-Littmark (ZBL) screened nuclear
repulsion potential interpolation. Comparisons of multiple important properties
were made between the deep-learning potential and existing analytical
potentials which are most commonly used in molecular dynamics simulations of
3C-SiC. Not only for equilibrium properties but also for significant properties
of radiation damage such as defect formation energies and threshold
displacement energies, our deep-learning potential gave closer predictions to
DFT criterion than analytical potentials. The deep-learning potential framework
solved the long-standing dilemma that traditional empirical potentials
currently applied in 3C-SiC radiation damage simulations gave large disparities
with each other and were inconsistent with ab-initio calculations. A more
realistic depiction of the primary irradiation damage process in 3C-SiC can be
given and the accuracy of classical molecular dynamics simulation for cubic
silicon carbide can be expected to the level of quantum mechanics.
