Neural Networks Potential from the Bispectrum Component: A Case Study on Crystalline Silicon

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.

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