Microcrystal Growth Pathways Investigated with Machine Learning Segmentation and Classification in Scanning Electron Microscopy

Advances in electron microscopy have revolutionized material characterization on the nano- and microscales, providing important insights into local ordering, structure, and size and quality distributions. While shape and size can be rigorously quantified through microscopy, it is often limited to local structure analysis and fails to describe bulk sample quality. Herein, a flexible machine learning (ML) tool is described that can segment and classify faceted crystals in scanning electron microscopy (SEM) micrographs to determine sample quality through the crystal size and product distribution. As a case study, this tool was applied to investigate crystal growth pathways (classical nucleation and growth compared to nonclassical growth) in DNA-mediated nanoparticle assembly through size and product (single crystal, fused crystal, or noncrystal) distribution of samples containing over 13000 colloidal crystal products. Strong DNA bond strengths (controlled by DNA sequence) lead to fast nucleation that exhausts the monomer concentration, resulting in smaller colloidal crystals. Alternatively, increased thermal energy and crystallization time lead to nonclassical crystallization pathways (coalescence) that result in larger colloidal crystals. This tool is useful since experimental conditions can now be deliberately identified to control colloidal crystal size and size distribution, important considerations for researchers interested in designing and synthesizing colloidal crystal metamaterials.