Discovering Assembly Pathways for Complex Crystals: An A15 Case Study

Understanding self-assembly pathways is important for predicting the characteristics of the resulting crystal such as the symmetries, unit cell, and defects. Many published studies have elucidated crystallization pathways for simple crystals with small unit cells, but we lack microscopic understanding of the structural changes that occur when liquids crystallize into structures with significantly greater complexity. We present a powerful workflow that applies machine learning, event detection, and a new Point Group Order Parameter (P_gOP) that describes the local point group symmetries of sets of particles and how they evolve during crystallization. As an example, we apply this workflow to molecular dynamics simulations of A15 crystals self-assembled via either slow cooling or rapid quenching. Our work demonstrates how this workflow can quantify the evolution of relevant local structural motifs and highlights differences in observed pathways using microscopically relevant order parameters.