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Register a new userTowards Automating Structural Discovery in Scanning Transmission Electron Microscopy
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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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Physics-driven discovery in an autonomous experiment has emerged as a dream application of machine learning in physical sciences. Here we develop and experimentally implement deep kernel learning workflow combining the correlative prediction of the target functional response and its uncertainty from the structure, and physics-based selection of acquisition function guiding the navigation of the image space. Compared to classical Bayesian optimization methods, this approach allows to capture the complex spatial features present in the images of realistic materials, and dynamically learn structure-property relationships towards physical discovery. Here, this approach is illustrated for nanoplasmonic studies of the nanoparticles and experimentally implemented for bulk- and edge plasmon discovery in MnPS3, a lesser-known beam-sensitive layered 2D material. This approach is universal and is expected to be applicable to probe-based microscopic techniques including other STEM modalities and Scanning Probe Microscopies.
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.
Scanning transmission electron microscopy (STEM) has advanced rapidly in the last decade thanks to the ability to correct the major aberrations of the probe forming lens. Now atomic-sized beams are routine, even at accelerating voltages as low as 40 kV, allowing knock-on damage to be minimized in beam sensitive materials. The aberration-corrected probes can contain sufficient current for high quality, simultaneous, imaging and analysis in multiple modes. Atomic positions can be mapped with picometer precision, revealing ferroelectric domain structures, composition can be mapped by energy dispersive X-ray spectroscopy (EDX) and electron energy loss spectroscopy (EELS) and charge transfer can be tracked unit cell by unit cell using the EELS fine structure. Furthermore, dynamics of point defects can be investigated through rapid acquisition of multiple image scans. Today STEM has become an indispensable tool for analytical science at the atomic level, providing a whole new level of insights into the complex interplays that control materials properties.
Fast pixelated detectors incorporating direct electron detection (DED) technology are increasingly being regarded as universal detectors for scanning transmission electron microscopy (STEM), capable of imaging under multiple modes of operation. However, several issues remain around the post acquisition processing and visualisation of the often very large multidimensional STEM datasets produced by them. We discuss these issues and present open source software libraries to enable efficient processing and visualisation of such datasets. Throughout, we provide examples of the analysis methodologies presented, utilising data from a 256$times$256 pixel Medipix3 hybrid DED detector, with a particular focus on the STEM characterisation of the structural properties of materials. These include the techniques of virtual detector imaging; higher order Laue zone analysis; nanobeam electron diffraction; and scanning precession electron diffraction. In the latter, we demonstrate nanoscale lattice parameter mapping with a fractional precision $le 6times10^{-4}$ (0.06%).
Recent progress in phase modulation using nanofabricated electron holograms has demonstrated how the phase of an electron beam can be controlled. In this paper, we apply this concept to the correction of spherical aberration in a scanning transmission electron microscope and demonstrate an improvement in spatial resolution. Such a holographic approach to spherical aberration correction is advantageous for its simplicity and cost-effectiveness.
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