Machine learned potential for defected single layer hexagonal boron nitride

Abstract

Development of machine learned interatomic potentials (MLIP) is critical for performing reliable simulations of materials at length and time scales that are comparable to those in the laboratory. We present here a MLIP suitable for simulations of the temperature dependent structure and dynamics of single layer hexagonal boron nitride (h-BN) with defects and grain boundaries, developed using a strictly local equivariant deep neural network as formulated in the Allegro code. The training dataset consisted of about 30,000 images of h-BN with and without point defects generated with ab-initio molecular dynamics simulations, based on density functional theory (DFT), at 500, 1000, and 1500K. The developed MLIP predicts potential energies and forces with a mean absolute error (MAE) of 4 meV/atom and 60 meV/Angstrom , respectively. It also reproduces phonon dispersion curves and density of vibrational states of pristine bulk h-BN that are comparable with that obtained from density functional theory-based calculations. Molecular dynamics simulations of the motion of the 4|8 grain boundary unit in h-BN shows the first step to have an activation barrier ~2.2 eV, indicating immobility of the grain boundary. Moving the grain boundary units past the first shows much lower activation barriers of ~0.42eV, suggesting a facile motion of the grain boundary once the first movement is stimulated. These simulations yield a scaled mobility of 1.739*10^(-11) m^3/Js for a temperature of 1500K which, given the inherent differences in the set-ups, is not too far from the experimental value of 1.36*10^(-9) m^3/Js. The ability to predict grain boundary mobility within reasonable agreement with experiment demonstrates the robustness of the MLIP and its suitability for reliable simulations of defect structures and dynamics in single layer h-BN.