Local structural features elucidate crystallization of complex structures

TL;DR

This paper introduces an unsupervised machine learning approach to analyze local structural features during complex crystal growth, revealing insights into the emergence of order and defect formation in various structures.

Contribution

The study develops a new order parameter for classifying local environments in complex structures, aiding understanding of crystallization pathways and defect competition.

Findings

Identifies local motifs during crystallization using bond-orientational metrics

Discovers under-coordination in liquids relative to bulk crystals

Analyzes defect formation in Frank--Kasper phases

Abstract

Complex crystal structures are composed of multiple local environments, and how this type of order emerges spontaneously during crystal growth has yet to be fully understood. We study crystal growth across various structures and along different crystallization pathways, using self-assembly simulations of identical particles that interact via multi-well isotropic pair potentials. We apply an unsupervised machine learning method to features from bond-orientational order metrics to identify different local motifs present during a given structure's crystallization process. In this manner, we distinguish different crystallographic sites in highly complex structures. Tailoring this newly developed order parameter to structures of varying complexity and coordination number, we study the emergence of local order along a multi-step crystal growth pathway -- from a low-density fluid to a high-density, supercooled amorphous liquid droplet and to a bulk crystal. We find a consistent under-coordination of the liquid relative to the average coordination number in the bulk crystal. We use our order parameter to analyze the geometrically frustrated growth of a Frank--Kasper phase and discover how structural defects compete with the formation of crystallographic sites that are higher-coordinated than the liquid environments. The here-presented method for classifying order on a particle-by-particle level have broad applicability to future studies of structural self-assembly and crystal growth, and they can aid in the design of building blocks and for targeting pathways of formation of novel soft-matter structures.