Using Machine learning to study nucleation in a model system of soft polymer colloids

Abstract

Despite the amount of work devoted on nucleation, the mechanism of nucleation is still not well understood. Many scenarios have been proposed such as a classical one-step nucleation mechanism or a non-classical two-step crystallization process, but both scenarios are still heavily debated. In this thesis, we investigate the crystal nucleation mechanism of Gaussian core particles using computer simulations, and quantify the results using machine learning. Using a Principal Component Analysis we will shed light on the nucleation mechanism of Gaussian core particles in the presence of different competing crystal structures.