(438d) Tracking the Emergence of Crystalline Order Via Local Structural Analysis

Understanding how different crystal structures grow is crucial for the design of novel functional materials, yet the manner in which particle-particle interactions lead to the emergent formation of an ordered structure remains mysterious. We take a system-agnostic approach that is independent of a particular chemistry or length scale and analyze the growth of a diverse set of crystal structures, which we assemble via molecular dynamics simulations. With a machine-learning-powered order parameter*, we classify particles into different local environments during the crystallization process, isolating different phases and crystal sites. This analysis is performed and compared for both low- and high-coordinated structures as well as structures of different complexities. We track how particles change classifications during the progression of crystal growth to illuminate the emergence of long-range order from short-range interactions. Our approach will lead to a deeper understanding of intricate separations processes that lead to the formation of complex structures.

*M. Spellings, S. C. Glotzer, AIChE Journal 64 (6), 2198–2206 (2018).