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Kinetic Monte Carlo prediction of the morphology of pentaerythritol tetranitrate

Jacob Jeffries, Himanshu Singh, Romain Perriot, Christian Negre, Antonio Redondo, Enrique Martinez

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.

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.

Paper Structure

This paper contains 12 sections, 24 equations, 3 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. The kMC model
  3. Simulation Parameters
  4. Results
  5. Discussion
  6. Conclusions
  7. Data Availability
  8. Acknowledgements
  9. Disclaimer
  10. Appendix
  11. Adsorption Rate
  12. Evaporation barrier

Figures (3)

  • Figure 1: Example colored graph. Blue nodes represent sites $i$ with occupation $x_i = 1$, and white nodes represent sites $i$ with occupation $x_i = 0$. Coordination numbers $n_i$ determine the solid-liquid interface, i.e. possible candidate events.
  • Figure 2: Surface energy as a function of time compared to AE model surface energy $\gamma_\text{AE}$.
  • Figure 3: Final crystal shape with two different orientations. Bottom is with surface mesh built with the $\alpha$-shape method in OVITO with radius $7\AA$ and smoothing level $100$.