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Understanding the structure of materials is crucial for engineering devices and materials with enhanced performance. Four-dimensional scanning transmission electron microscopy (4D-STEM) is capable of mapping nanometer-scale local crystallographic structure over micron-scale field of views. However, 4D-STEM datasets can contain tens of thousands of images from a wide variety of material structures, making it difficult to automate detection and classification of structures. Traditional automated analysis pipelines for 4D-STEM focus on supervised approaches, which require prior knowledge of the material structure and cannot describe anomalous or deviant structures. In this article, a pipeline for engineering 4D-STEM feature representations for unsupervised clustering using non-negative matrix factorization (NMF) is introduced. Each feature is evaluated using NMF and results are presented for both simulated and experimental data. It is shown that some data representations more reliably identify overlapping grains. Additionally, real space refinement is applied to identify spatially distinct sample regions, allowing for size and shape analysis to be performed. This work lays the foundation for improved analysis of nanoscale structural features in materials that deviate from expected crystallographic arrangement using 4D-STEM.

A fast, robust pipeline for strain mapping of crystalline materials is important for many technological applications. Scanning electron nanodiffraction allows us to calculate strain maps with high accuracy and spatial resolutions, but this technique is limited when the electron beam undergoes multiple scattering. Deep-learning methods have the potential to invert these complex signals, but require a large number of training examples. We implement a Fourier space, complex-valued deep-neural network, FCU-Net, to invert highly nonlinear electron diffraction patterns into the corresponding quantitative structure factor images. FCU-Net was trained using over 200,000 unique simulated dynamical diffraction patterns from different combinations of crystal structures, orientations, thicknesses, and microscope parameters, which are augmented with experimental artifacts. We evaluated FCU-Net against simulated and experimental datasets, where it substantially outperforms conventional analysis methods. Our code, models, and training library are open-source and may be adapted to different diffraction measurement problems.

Magnetic skyrmions are topologically nontrivial spin textures with envisioned applications in energy-efficient magnetic information storage. Toggling the presence of magnetic skyrmions via writing/deleting processes is essential for spintronics applications, which usually require the application of a magnetic field, a gate voltage or an electric current. Here we demonstrate the reversible field-free writing/deleting of skyrmions at room temperature, via hydrogen chemisorption/desorption on the surface of Ni and Co films. Supported by Monte-Carlo simulations, the skyrmion creation/annihilation is attributed to the hydrogen-induced magnetic anisotropy change on ferromagnetic surfaces. We also demonstrate the role of hydrogen and oxygen on magnetic anisotropy and skyrmion deletion on other magnetic surfaces. Our results open up new possibilities for designing skyrmionic and magneto-ionic devices.

Abstract Polar vortices in oxide superlattices exhibit complex polarization topologies. Using a combination of electron energy loss near-edge structure analysis, crystal field multiplet theory, and first-principles calculations, we probe the electronic structure within such polar vortices in [(PbTiO 3 ) 16 /(SrTiO 3 ) 16 ] superlattices at the atomic scale. The peaks in Ti $$L$$ L -edge spectra shift systematically depending on the position of the Ti 4+ cations within the vortices i.e., the direction and magnitude of the local dipole. First-principles computation of the local projected density of states on the Ti $$3d$$ 3 d orbitals, together with the simulated crystal field multiplet spectra derived from first principles are in good agreement with the experiments.

Robust atomic-to-meso-scale chirality is now observed in the one-dimensional form of tellurium. This enables a large and counter-intuitive circular-polarization dependent second harmonic generation response above 0.2 which is not present in two-dimensional tellurium. Orientation variations in 1D tellurium nanowires obtained by four-dimensional scanning transmission electron microscopy (4D-STEM) and their correlation with unconventional non-linear optical properties by second harmonic generation circular dichroism (SHG-CD) uncovers an unexpected circular-polarization dependent SHG response from 1D nanowire bundles – an order-of-magnitude higher than in single-crystal two-dimensional tellurium structures – suggesting the atomic- and meso-scale crystalline structure of the 1D material possesses an inherent chirality not present in its 2D form; and which is strong enough to manifest even in the aggregate non-linear optical (NLO) properties of aggregates.