Kinetic Monte Carlo Prediction of the Morphology of Pentaerythritol Tetranitrate

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 model for diffusion within the solvent phase, 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 results in excellent agreement with the attachment energy model.