Kinetic Monte Carlo prediction of the morphology of pentaerythritol tetranitrate

Abstract: In this work, we develop an atomistic, graph-based kinetic Monte Carlo (KMC) simulation routine to predict crystal morphology. Within this routine, we encode the state of the supercell in a binary occupation vector and the topology of the supercell in a simple nearest-neighbor graph. From this encoding, we efficiently compute the interaction energy of the system as a quadratic form of the binary occupation vector, representing pairwise interactions. This encoding, coupled with a simple diffusion model for adsorption, is then used to model evaporation and adsorption dynamics at solid-liquid interfaces. The resulting intermolecular interaction-breaking energies are incorporated into a kinetic model to predict crystal morphology, which is implemented in the open-source Python package Crystal Growth Kinetic Monte Carlo (cgkmc). We then apply this routine to pentaerythritol tetranitrate (PETN), an important energetic material, showing excellent agreement with the attachment energy model.