Development of artificial neural network potential for hexagonal boron nitride with and without defects

Recently, defects in hexagonal boron nitride (h-BN) have been shown to play an important role in determining its novel chemical and optical properties, which may have a variety of possible technological applications. Characterization of the structure and dynamics of these defects would be most facilitated by the availability of accurate interatomic potentials that enable large length and time scale simulations. In this work, we will summarize our development of an artificial neural network (ANN) potential for h-BN with and without defects. The ANN potential was trained by using about 70000 data points obtained from ab initio molecular dynamic simulations of point defect on a (6x6) h-BN layer. The trained ANN potential is capable of producing system energetics in agreement with that obtained from density functional theory (within few meV per atom for both training and validation). The structure and dynamics of defects and grain boundaries in h-BN using the ANN potential will be presented and results compared with available experimental data.