  In this article, we present a systematic study in developing machine learning
force fields (MLFF) for crystalline silicon. While the main-stream approach of
fitting a MLFF is to use a small and localized training sets from molecular
dynamics simulation, it is unlikely to cover the global feature of the
potential energy surface. To remedy this issue, we used randomly generated
symmetrical crystal structures to train a more general Si-MLFF. Further, we
performed substantial benchmarks among different choices of materials
descriptors and regression techniques on two different sets of silicon data.
Our results show that neural network potential fitting with bispectrum
coefficients as the descriptor is a feasible method for obtaining accurate and
transferable MLFF.
