Automated Crystal Orientation Mapping in py4DSTEM using Sparse Correlation Matching

Abstract

Crystalline materials used in technological applications are often complex assemblies composed of multiple phases and differently oriented grains. Robust identification of the phases and orientation relationships from these samples is crucial, but the information extracted from the diffraction condition probed by an electron beam is often incomplete. We have developed an automated crystal orientation mapping (ACOM) procedure which uses a converged electron probe to collect diffraction patterns from multiple locations across a complex sample. We provide an algorithm to determine the orientation of each diffraction pattern based on a fast sparse correlation method. We demonstrate the speed and accuracy of our method by indexing diffraction patterns generated using both kinematical and dynamical simulations. We have also measured orientation maps from an experimental dataset consisting of a complex polycrystalline twisted helical AuAgPd nanowire. From these maps we identify twin planes between adjacent grains, which may be responsible for the twisted helical structure. All of our methods are made freely available as open source code, including tutorials which can be easily adapted to perform ACOM measurements on diffraction pattern datasets.

Cryogenic 4D-STEM analysis of an amorphous-crystalline polymer blend: Combined nanocrystalline and amorphous phase mapping

“…Additionally, this work only maps in-plane rotation as opposed to a fully resolved 3D crystallographic rotation which would improve the ability of this work to draw conclusions about iScience Article correlated neighboring crystallography in these samples. But, with the very recent advent of new automatic indexing algorithms (Ophus et al, 2021) for electron diffraction data, this obstacle could be overcome in future applications. Finally, as with any electron microscopy technique, as electron dose is lowered in 4D-STEM, SNR is diminished.…”

Analysis of Interpretable Data Representations for 4D-STEM Using Unsupervised Learning

“…The AA and BD have a similar distribution of weighted dissimilarity scores, indicating that either of these representations may be reasonable for this dataset. Since previous work has described multiple overlapping grains within these NWs (Ophus et al, 2021;Wang et al, 2011), the AA feature may provide better overall measurements of the cluster areas and diameters.…”

“…In order to directly interpret the structure within these grains, it may be beneficial to use only the patterns that are detected in a single cluster to represent and interpret the full region. Categorization of the detected clusters into crystallographic orientations or phases can be completed by combining the NMF clustering workflow reported in this work with the ACOM workflow presented by Ophus et al (2021), for example, by consolidating the BD detected throughout the dataset into sets based on the detected clusters. The performance on experimental data may be validated by comparing the clusters detected through these methods and the results of performing the full ACOM method.…”

“…There are only a few literature examples of 4D-STEM analysis of ultrasmall (<10 nm) nanomaterials (Bruma et al, 2016; DaSilva et al, 2020; Ophus et al, 2021), and none to our knowledge that area have been established without prior knowledge of the material structure. Since this class of materials often contain significant levels of disorder at the single pattern level compared with their bulk counterparts, conventional methods designed for bulk materials often fall short.…”

Visualizing Grain Statistics in MOCVD WSe2 through Four-Dimensional Scanning Transmission Electron Microscopy

“…The structural information yielded by this investigation can serve as a useful starting point for models such as phase field calculations when predicting functionality (mechanical properties, polarizability, permeation, and so forth). With advancements in fast electron detectors and new data science techniques being applied to large diffraction data sets, we are enthusiastic that this technique will help to enable automated crystal orientation mapping (ACOM) 61 in TEM in a statistically meaningful way.…”