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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.

Scanning transmission electron microscopy (STEM) is widely used for imaging, diffraction, and spectroscopy of materials down to atomic resolution. Recent advances in detector technology and computational methods have enabled many experiments that record a full image of the STEM probe for many probe positions, either in diffraction space or real space. In this paper, we review the use of these four-dimensional STEM experiments for virtual diffraction imaging, phase, orientation and strain mapping, measurements of medium-range order, thickness and tilt of samples, and phase contrast imaging methods, including differential phase contrast, ptychography, and others.

Scanning transmission electron microscopy (STEM) allows for imaging, diffraction, and spectroscopy of materials on length scales ranging from microns to atoms. By using a high-speed, direct electron detector, it is now possible to record a full two-dimensional (2D) image of the diffracted electron beam at each probe position, typically a 2D grid of probe positions. These 4D-STEM datasets are rich in information, including signatures of the local structure, orientation, deformation, electromagnetic fields, and other sample-dependent properties. However, extracting this information requires complex analysis pipelines that include data wrangling, calibration, analysis, and visualization, all while maintaining robustness against imaging distortions and artifacts. In this paper, we present py4DSTEM, an analysis toolkit for measuring material properties from 4D-STEM datasets, written in the Python language and released with an open-source license. We describe the algorithmic steps for dataset calibration and various 4D-STEM property measurements in detail and present results from several experimental datasets. We also implement a simple and universal file format appropriate for electron microscopy data in py4DSTEM, which uses the open-source HDF5 standard. We hope this tool will benefit the research community and help improve the standards for data and computational methods in electron microscopy, and we invite the community to contribute to this ongoing project.

High-throughput grain mapping with sub-nanometer spatial resolution is demonstrated using scanning nanobeam electron diffraction (also known as 4D scanning transmission electron microscopy, or 4D-STEM) combined with high-speed direct-electron detection. An electron probe size down to 0.5 nm in diameter is used and the sample investigated is a gold–palladium nanoparticle catalyst. Computational analysis of the 4D-STEM data sets is performed using a disk registration algorithm to identify the diffraction peaks followed by feature learning to map the individual grains. Two unsupervised feature learning techniques are compared: principal component analysis (PCA) and non-negative matrix factorization (NNMF). The characteristics of the PCA versus NNMF output are compared and the potential of the 4D-STEM approach for statistical analysis of grain orientations at high spatial resolution is discussed.

For enhancing the electrochemical performance of solid‐state batteries (SSBs), protective coatings are applied on the cathode active material (CAM) to mitigate the degradation of the cathode/electrolyte interface. A comprehensive understanding of the structural properties of these coatings is crucial for further optimization. This study investigates the effect of LiNbO3‐related coatings on LiNi0.6Co0.2Mn0.2O2 (NCM622) CAM, focusing on the relationship between coating structure and electrochemical performance in battery cells. Therefore, three samples calcinated at 550, 350 and, 80 °C temperature are analyzed with scanning transmission electron microscopy (STEM), energy dispersive X‐ray spectroscopy (EDS), and scanning precession electron diffraction (SPED) in combination with a pair distribution function (PDF) analysis. The results reveal that only an amorphous LiNbO3 coating with a calcination temperature of 350 °C significantly improves the electrochemical performance of the CAM. In contrast, at higher calcination temperatures the coating crystallizes, while at lower calcination temperatures the coating becomes a mixed niobium oxide phase, both of which correlate with reduced battery performance. The amorphous structure of LiNbO3 coatings on LiNi0.6Co0.2Mn0.2O2 (NCM622) cathode active material is systematically investigated by scanning transmission electron microscopy. Theoretical simulations provide insight into the origin of structural transformations observed in the coating at lower calcination temperatures. By correlating the structural characteristics with the electrochemical performance of the coated NCM622 in battery cells, the optimal coating phase is identified.