Melt crystallization mechanism analyzed with dimensional reduction of high-dimensional data representing distribution function geometries

Abstract

AbstractMelt crystallization is essential to many industrial processes, including semiconductor, ice, and food manufacturing. Nevertheless, our understanding of the melt crystallization mechanism remains poor. This is because the molecular-scale structures of melts are difficult to clarify experimentally. Computer simulations, such as molecular dynamics (MD), are often used to investigate melt structures. However, the time evolution of the structural order in a melt during crystallization must be analyzed properly. In this study, dimensional reduction (DR), which is an unsupervised machine learning technique, is used to evaluate the time evolution of structural order. The DR is performed for high-dimensional data representing an atom–atom pair distribution function and the distribution function of the angle formed by three nearest neighboring atoms at each period during crystallization, which are obtained by an MD simulation of a supercooled Lennard–Jones melt. The results indicate that crystallization occurs via the following activation processes: nucleation of a crystal with a distorted structure and reconstruction of the crystal to a more stable structure. The time evolution of the local structures during crystallization is also evaluated with this method. The present method can be applied to studies of the mechanism of crystallization from a disordered system for real materials, even for complicated multicomponent materials.