UC Santa Barbara

Crystal morphology prediction tools have drawn scientific and industrial interest due to the close connection between morphology and crystal properties and performance. Existing multiscale models consider the behavior of growth units at the microscale to inform macroscale predictions, and are continually improving. Such tools aid in narrowing the design space to reduce experimental costs and inform rational process development. This dissertation seeks to improve the understanding of underlying crystal growth mechanisms and enable the development of more robust multiscale models via computational techniques.

Crystal growth is often governed by layered growth mechanisms at the surface. Simulation methodologies are instrumental in modeling surface rare events and provide a route toward calculating relevant parameters such as crystal face growth rates. In particular, we highlight the utility of kinetic Monte Carlo (kMC) simulation methods in this field to accurately model crystal surface dynamics and provide in silico morphology predictions. We consider cases of molecules both with centrosymmetric and noncentrosymmetric growth units and provide various morphology predictions in good agreement with experimental data. We also study impurity-mediated crystal growth systems and develop models to quantify the effect of impurities on crystal growth and on polymorphism. These studies offer computationally driven insights to elucidate mechanistic model expressions appropriate for a more extensive array of crystal systems