(683f) Multiscale, Multiphysics, Mechanistic Model for Computation of Face-Specific Growth Rates

The morphology or shape of the crystalline material is a critical determinant of their physical properties such as stability, flowability, wettability, bulk density, and dissolution rates. For example, the plate- or needle-like pharmaceutical crystals are difficult to granulate and compress into a tablet form. Identification of theoretical models to predict the effect of crystallization conditions on crystal morphologies is imperative to develop control strategies for preventing such undesired morphologies to grow. The dynamic and steady-state prediction of crystal morphology requires the knowledge of face-specific growth rates of crystals. The current models to predict crystal growth rates assumes the regimes of crystal growth mechanism (spiral, 2D nucleation and rough) based on the empirical calculations of interfacial free energies of solute and solvent molecules. The growth rate is then estimated using the height, spacing, and velocity of layers spreading on the crystal surface. However, a single face of the crystal can have multiple growth mechanisms on the surface and there need not be a strict barrier between different crystal growth mechanisms. This could be due to different processes – diffusion, desolvation and surface integration, and topography evolution - occurring simultaneously on the nucleated crystal surface. There is a need for a multiscale model which accounts for all these processes. In this talk, I will describe the multi-scale model for determining crystal growth rate using Kinetic Monte-Carlo method. This multi-scale model captures the most fundamental mechanism of crystallization – the association and dissociation of molecules – and find the most probabilistic realization of the surface. Our method considers probabilities of events such as nucleation of 2D islands and screw dislocations and spreading of the layer based on the energy barrier for integration of molecules onto the crystal surface. The prediction of growth rates of Glutamic acid crystals will be shown as an example.