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Register a new userEfficient first principles simulation of electron scattering factors for transmission electron microscopy
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Electron microscopy is a powerful tool for studying the properties of materials down to their atomic structure. In many cases, the quantitative interpretation of images requires simulations based on atomistic structure models. These typically use the independent atom approximation that neglects bonding effects, which may, however, be measurable and of physical interest. Since all electrons and the nuclear cores contribute to the scattering potential, simulations that go beyond this approximation have relied on computationally highly demanding all-electron calculations. Here, we describe a new method to generate ab initio electrostatic potentials when describing the core electrons by projector functions. Combined with an interface to quantitative image simulations, this implementation enables an easy and fast means to model electron microscopy images. We compare simulated transmission electron microscopy images and diffraction patterns to experimental data, showing an accuracy equivalent to earlier all-electron calculations at a much lower computational cost.
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The simulation of transmission electron microscopy (TEM) images or diffraction patterns is often required to interpret their contrast and extract specimen features. This is especially true for high-resolution phase-contrast imaging of materials, but electron scattering simulations based on atomistic models are widely used in materials science and structural biology. Since electron scattering is dominated by the nuclear cores, the scattering potential is typically described by the widely applied independent atom model. This approximation is fast and fairly accurate, especially for scanning TEM (STEM) annular dark-field contrast, but it completely neglects valence bonding and its effect on the transmitting electrons. However, an emerging trend in electron microscopy is to use new instrumentation and methods to extract the maximum amount of information from each electron. This is evident in the increasing popularity of techniques such as 4D-STEM combined with ptychography in materials science, and cryogenic microcrystal electron diffraction in structural biology, where subtle differences in the scattering potential may be both measurable and contain additional insights. Thus, there is increasing interest in electron scattering simulations based on electrostatic potentials obtained from first principles, mainly via density functional theory, which was previously mainly required for holography. In this Review, we discuss the motivation and basis for these developments, survey the pioneering work that has been published thus far, and give our outlook for the future. We argue that a physically better justified $textit{ab initio}$ description of the scattering potential is both useful and viable for an increasing number of systems, and we expect such simulations to steadily gain in popularity and importance.
Single atoms can be considered as basic objects for electron microscopy to test the microscope performance and basic concepts for modeling of image contrast. In this work high-resolution transmission electron microscopy was applied to image single platinum atoms in an aberration-corrected transmission electron microscope. The atoms are deposited on a self-assembled monolayer substrate which induces only negligible contrast. Single-atom contrast simulations were performed on the basis of Weickenmeier-Kohl and Doyle-Turner scattering factors. Experimental and simulated intensities are in full agreement on an absolute scale.
Thin film oxides are a source of endless fascination for the materials scientist. These materials are highly flexible, can be integrated into almost limitless combinations, and exhibit many useful functionalities for device applications. While precision synthesis techniques, such as molecular beam epitaxy (MBE) and pulsed laser deposition (PLD), provide a high degree of control over these systems, there remains a disconnect between ideal and realized materials. Because thin films adopt structures and chemistries distinct from their bulk counterparts, it is often difficult to predict what properties will emerge. The complex energy landscape of the synthesis process is also strongly influenced by non-equilibrium growth conditions imposed by the substrate, as well as the kinetics of thin film crystallization and fluctuations in process variables, all of which can lead to significant deviations from targeted outcomes. High-resolution structural and chemical characterization techniques, as described in this volume, are needed to verify growth models, bound theoretical calculations, and guide materials design. While many characterization options exist, most are spatially-averaged or indirect, providing only partial insight into the complex behavior of these systems. Over the past several decades, scanning transmission electron microscopy (STEM) has become a cornerstone of oxide heterostructure characterization owing to its ability to simultaneously resolve structure, chemistry, and defects at the highest spatial resolution. STEM methods are an essential complement to averaged scattering techniques, offering a direct picture of resulting materials that can inform and refine the growth process to achieve targeted properties. There is arguably no other technique that can provide such a broad array of information at the atomic-scale, all within a single experimental session.
A method is described for the reconstruction of the amplitude and phase of the object exit wave function by phase-plate transmission electron microscopy. The proposed method can be considered as in-line holography and requires three images, taken with different phase shifts between undiffracted and diffracted electrons induced by a suitable phase-shifting device. The proposed method is applicable for arbitrary object exit wave functions and non-linear image formation. Verification of the method is performed for examples of a simulated crystalline object wave function and a wave function acquired with off-axis holography. The impact of noise on the reconstruction of the wave function is investigated.
Scanning transmission electron microscopy (STEM) is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls, extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of active learning methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance a priori knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO$_3$ matrix, ferroelectric domains in BiFeO$_3$, and topological defects in graphene. The code developed in this manuscript are open sourced and will be released at github.com/creangnc/AE_Workflows.
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