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Electron diffraction has dramatically increased in popularity amongst chemists given its renewed application for ab initio structure determination from molecular nanocrystals. In one implementation, popularly referred to as 3D ED or MicroED, crystals nanocrystals orders of magnitude too small for conventional X-ray analysis are interrogated by an electron beam to determine atomic structures. However, these approaches are thwarted by disordered, overlapping, or otherwise poorly diffracting domains.

Spatially resolved diffraction mapping techniques can overcome some of these limitations, and have seen limited application in X-ray diffraction. In electron microscopy, such approaches, including 4D scanning electron microscopy (4D-STEM), have grown popular. We demonstrated that 4D-STEM can be used to determine ab initio structures of molecules by direct methods, from small ordered nanodomains of single microcrystals. In our approach 4D-STEM is used to generate diffraction scans that enable ex post facto reconstruction of digitally defined virtual apertures. The synthetic patterns derived from these scans are suitable for direct methods phasing of molecular structures.

In addition, this approach unveils that coherently diffracting zones (CDZs) in molecular crystals form unpredictably distributed striations. The observation of these zones and our ability to determine structures from these regions of nanocrystals empowers us to explore their atomic substructure and their response to radiolytic damage.

Considering the scale of the lithium ion battery (LIB)  industry, it is surprising how poorly the function of LIBs is understood at the molecular level. While much is certainly known, this knowledge has been gained via inference and expensive trial-and-error because it is difficult to look inside a functioning LIB to “see” what is going on. The battery is a bulk device with a liquid, air-sensitive organic electrolyte. With use, there forms on the LIB electrodes an almost magical solid-electrolyte interphase (SEI) that is an insulator for electrons but a conductor for Li⁺ ions. The main mysteries of LIB function involve the chemical composition and structure of this layer. We present the first images of the LIB SEI acquired under room-temperature operando conditions with high spatial and spectroscopic resolution. This combination gives us an unprecedented view of the SEI’s development, where we can make chemical identifications localized to nanometer precision while the electrode is in the very act of intercalating. We image the bulk SEI, not just its surface, by contriving electrochemical fluid cells that are only 50 nm thick. With these thin cells we can map the Li itself by its unique spectroscopic fingerprint, an achievement described as “practically impossible” just a few years ago.

In ultrafast electron diffraction (UED) experiments, accurate retrieval of time-resolved structural parameters, such as atomic coordinates and thermal displacement parameters, requires an accurate scattering model. In this article, we demonstrated dynamical scattering models that are suitable for matching ultrafast electron diffraction (UED) signals from single-crystal films and retrieving the lattice temperature dynamics. We first described the computational approaches used, including both a multislice and a Bloch wave method, and introduced adaptations to account for key physical parameters. We then illustrated the role of dynamical scattering in UED of single-crystal films by comparing static and temperature-dependent diffraction signals calculated using kinematical and dynamical models for gold films of varying thicknesses and rippling as well as varying electron probe energy. Lastly, we applied these models to analyze relativistic UED measurements of single-crystal gold films recorded at the High Repetition-rate Electron Scattering (HiRES) beamline of Lawrence Berkeley National Laboratory. Our results showed the importance of a dynamical scattering theory for quantitative analysis of UED and demonstrated models that can be practically applied to single-crystal materials and heterostructures.

Coherent diffractive imaging (CDI) is revolutionizing the physical and biological science fields by first measuring the diffraction patterns of nano-crystals or non-crystalline samples and then inverting them to high-resolution images. The well-known phase problem is solved by the combination of coherent illumination and iterative computational algorithms. In particular, ptychography – a powerful scanning CDI method – has found wide applications with synchrotron radiation, high harmonic generation, electron and optical microscopy. However, iterative algorithms are not only computationally expensive, but also require practitioners to get algorithmic training to optimize the parameters and obtain satisfactory results. These difficulties  have thus far prevented CDI from being accessible to an even broader user community. Here we demonstrated deep learning CDI using convolutional neural networks (CNNs) trained only by simulated data. The CNNs are subsequently used to directly retrieve the phase images of monolayer graphene, twisted hexagonal boron nitride and a Au nanoparticle from experimental electron diffraction patterns without any iteration. Quantitative analysis shows that the phase images recovered by the CNNs have comparable quality to those reconstructed by a conventional iterative method and the resolution of the phase images by the CNNs is in the range of 0.71-0.53 Å. Looking forward, we expect that deep learning CDI could become an important tool for real-time, atomic-scale imaging of a wide range of samples across different disciplines.