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Noncollinear ferroic materials are sought after as testbeds to explore the intimate connections between topology and symmetry, which result in electronic, optical and magnetic functionalities not observed in collinear ferroic materials. For example, ferroaxial materials have ordered rotational structural distortions that break mirror symmetry and induce chirality. When ferroaxial order is coupled with ferroelectricity arising from a broken inversion symmetry, it offers the prospect of electric-field-control of the ferroaxial distortions and opens up new tunable functionalities. However, chiral multiferroics, especially ones stable at room temperature, are rare. We report the discovery of a strain-stabilized, room-temperature chiral multiferroic phase in single crystals of BaTiS$_3$, a quasi-one-dimensional (1D) hexagonal chalcogenide. Using first-principles calculations, we predict the stabilization of this multiferroic phase having $P6_3$ space group for biaxial tensile strains exceeding 1.5% applied on the basal ab-plane of the room temperature $P6_3cm$ phase of BaTiS$_3$. The chiral multiferroic phase is characterized by rotational distortions of select TiS$_6$ octahedra around the long $c$-axis and polar displacement of Ti atoms along the $c$-axis. We used an innovative approach using focused ion beam milling to make appropriately strained samples of BaTiS$_3$. The ferroaxial and ferroelectric distortions, and their domains in $P6_3$-BaTiS$_3$ were directly resolved using atomic resolution scanning transmission electron microscopy. Landau-based phenomenological modeling predicts a strong coupling between the ferroelectric and the ferroaxial order making $P6_3$-BaTiS$_3$ an attractive test bed for achieving electric-field control of chirality-related phenomena such as circular photo-galvanic current and the Rashba effect., Comment: 20 pages, 4 figures

The discovery of ferroelectricity in hafnia-based materials have boosted the potential of incorporating ferroelectrics in advanced electronics, thanks to their compatibility with silicon technology. However, comprehending why these materials defy the common trend of reduced ferroelectric ordering at the nanoscale, and the mechanism that stabilizes the ferroelectric phase (absent in hafnia phase diagram) presents significant challenges to traditional knowledge of ferroelectricity. In this work, we show that the formation of the orthorhombic ferroelectric phase (o-FE, space group Pca21) of the single-crystalline epitaxial films of 10% La-doped HfO2 (LHO) on (111)-oriented yttria stabilized zirconia (YSZ) relies on the stability of the high-pressure orthorhombic antiferroelectric phase (o-AFE, space group Pbca). Our detailed structural characterizations demonstrate that as-grown LHO films represent largely the o-AFE phase being thermodynamically stabilized by the compressive strain. Our Kelvin probe force microscopy studies show, under mechanical poling, the o-AFE phase is converted to the o-FE phase which remains stable under ambient conditions. We find that the orthorhombic phase stability is enhanced in thinner films down to one-unit-cell thickness, a trend that is unknown in any other ferroelectric films. This is due to the vanishing depolarization field of the o-AFE phase and the isomorphic LHO/YSZ interface, supporting strain-enhanced ferroelectricity in the ultrathin films. This results in an unprecedented increase of the Curie temperature up to 850 {\deg}C, the highest reported for sub-nanometer-thick ferroelectrics. Overall, our findings opens the way for advanced engineering of hafnia-based materials for ferroelectric applications and heralding a new frontier of high-temperature ferroelectrics at the two-dimensional limit.

We present the design and implementation of a side entry laser system designed for an ultra-high vacuum scanning transmission electron microscope. This system uses a versatile probe design enclosed in a vacuum envelope such that parts can be easily aligned, modified, or exchanged without disturbing the vacuum. The system uses a mirror mounted on the sample holder such that the sample can be illuminated without being tilted. Notably the mirror can be removed and replaced with an ablation target and a higher power laser used to ablate material directly onto the sample. We argue that new capabilities hold the potential to transform the electron microscope from an analysis tool towards a more flexible synthesis system, where atomic scale fabrication and atom-by-atom experiments can be performed.

Efforts aimed at scaling fabrication processes to the level of single atoms, dubbed atom-by-atom fabrication or atomic fabrication, invariably encounter the obstacle of atomic scale cleanliness. When considering atomic fabrication, cleanliness of the base material and purity of the source reservoir from which atomic structures will be built are invariable constraints imposed by laws of physics and chemistry. As obvious as such statements may be, and regardless of the inevitable consequences for successful atomic fabrication, there is a poignant lack of understanding of the "dirt" (contamination/impurities). Here, we examine hydrocarbon contamination on graphene. Graphene has formed the base substrate for many e-beam-based atomic fabrication studies and many strategies for cleaning graphene have been presented in the literature. One popular method is heating to high temperatures (>500 {\deg}C). It is usually inferred that volatile hydrocarbons evaporate into the microscope vacuum system leaving behind pristine graphene. Here, we show through direct image intensity analysis that what appears to be clean graphene can be coated with a thin layer of dynamically diffusing hydrocarbons. This result holds significant implications for approaches to e-beam based atomic fabrication, updates the conceptual model of e-beam induced hydrocarbon deposition, and may extend to hot surfaces generally.

Structural disorder has been shown to enhance and modulate magnetic, electrical, dipolar, electrochemical, and mechanical properties of materials. However, the possibility of obtaining novel optical and optoelectronic properties from structural disorder remains an open question. Here, we show unambiguous evidence of disorder in the form of anisotropic, picoscale atomic displacements modulating the refractive index tensor and resulting in the giant optical anisotropy observed in BaTiS$_3$, a quasi-one-dimensional hexagonal chalcogenide. Single crystal X-ray diffraction studies reveal the presence of antipolar displacements of Ti atoms within adjacent TiS$_6$ chains along the c-axis, and three-fold degenerate Ti displacements in the a-b plane. $^{47/49}$Ti solid-state NMR provides additional evidence for those Ti displacements in the form of a three-horned NMR lineshape resulting from a low symmetry local environment around Ti atoms. We used scanning transmission electron microscopy to directly observe the globally disordered Ti a-b plane displacements and find them to be ordered locally over a few unit cells. First-principles calculations show that the Ti a-b plane displacements selectively reduce the refractive index along the ab-plane, while having minimal impact on the refractive index along the chain direction, thus resulting in a giant enhancement in the optical anisotropy. By showing a strong connection between structural disorder with picoscale displacements and the optical response in BaTiS$_3$, this study opens a pathway for designing optical materials with high refractive index and functionalities such as large optical anisotropy and nonlinearity., Comment: 24 pages, 3 figures

Focused electron beam induced deposition (FEBID) is a direct write technique for depositing materials on a support substrate akin to 3D printing with an electron beam (e-beam). Opportunities exist for merging this existing technique with aberration-corrected scanning transmission electron microscopy to achieve molecular- or atomic-level spatial precision. Several demonstrations have been performed using graphene as the support substrate. A common challenge that arises during this process is e-beam-induced hydrocarbon deposition, suggesting greater control over the sample environment is needed. Various strategies exist for cleaning graphene in situ. One of the most effective methods is to rapidly heat to high temperatures, e.g., 600 C or higher. While this can produce large areas of what appears to be atomically clean graphene, mobile hydrocarbons can still be present on the surfaces. Here, we show that these hydrocarbons are primarily limited to surface migration and demonstrate an effective method for interrupting the flow using e-beam deposition to form corralled hydrocarbon regions. This strategy is effective for maintaining atomically clean graphene at high temperatures where hydrocarbon mobility can lead to substantial accumulation of unwanted e-beam deposition.

Atomic-scale fabrication is an outstanding challenge and overarching goal for the nanoscience community. The practical implementation of moving and fixing atoms to a structure is non-trivial considering that one must spatially address the positioning of single atoms, provide a stabilizing scaffold to hold structures in place, and understand the details of their chemical bonding. Free-standing graphene offers a simplified platform for the development of atomic-scale fabrication and the focused electron beam in a scanning transmission electron microscope can be used to locally induce defects and sculpt the graphene. In this scenario, the graphene forms the stabilizing scaffold and the experimental question is whether a range of dopant atoms can be attached and incorporated into the lattice using a single technique and, from a theoretical perspective, we would like to know which dopants will create technologically interesting properties. Here, we demonstrate that the electron beam can be used to selectively and precisely insert a variety of transition metal atoms into graphene with highly localized control over the doping locations. We use first-principles density functional theory calculations with direct observation of the created structures to reveal the energetics of incorporating metal atoms into graphene and their magnetic, electronic, and quantum topological properties.

Chromium atoms in graphene have been proposed to exhibit magnetic moments and spin-selective conducting states depending on the local bonding geometry within the graphene structure, which could lead to interesting applications in spintronics. Despite this interest, there are few direct experimental reports of Cr dopants in graphene even though it is theorized to be stable. Here, we demonstrate the introduction of single Cr dopant atoms into the graphene lattice and onto graphene edges through the controlled use of a focused electron beam in a scanning transmission electron microscope. We show local control of doping locations and when coupled with targeted in situ milling during scanning of the e-beam, these strategies demonstrate an important component of the fabrication of tailored nanostructured devices in the electron microscope. The approach is validated with first principles calculations to understand synthesis pathways during fabrication and reveal the energetics and local properties of Cr atoms embedded in graphene; e.g., Cr doping can convert graphene into a magnetic and semiconducting material, which suggests Cr-doped graphene can be used as a building block for potential electronic devices and a means to construct them.

Graphene is of great scientific interest due to a variety of unique properties such as ballistic transport, spin selectivity, the quantum hall effect, and other quantum properties. Nanopatterning and atomic scale modifications of graphene are expected to enable further control over its intrinsic properties, providing ways to tune the electronic properties through geometric and strain effects, introduce edge states and other local or extended topological defects, and sculpt circuit paths. The focused beam of a scanning transmission electron microscope (STEM) can be used to remove atoms, enabling milling, doping, and deposition. Utilization of a STEM as an atomic scale fabrication platform is increasing; however, a detailed understanding of beam-induced processes and the subsequent cascade of aftereffects is lacking. Here, we examine the electron beam effects on atomically clean graphene at a variety of temperatures ranging from 400 to 1000 C. We find that temperature plays a significant role in the milling rate and moderates competing processes of carbon adatom coalescence, graphene healing, and the diffusion (and recombination) of defects. The results of this work can be applied to a wider range of 2D materials and introduce better understanding of defect evolution in graphite and other bulk layered materials.

The engineering of quantum materials requires the development of tools able to address various synthesis and characterization challenges. These include the establishment and refinement of growth methods, material manipulation, and defect engineering. Material modification at the atomic level will be a key enabling factor for the engineering of quantum materials where desired phenomena are critically determined by local atomic structures. Successful use of scanning transmission electron microscopes (STEMs) for atomic scale material manipulation has opened the door for a transformed view of what can be accomplished using electron-beam-based strategies. However, serious obstacles exist on the pathway from possibility to practical reality. One such obstacle is the in situ delivery of atomized material in the STEM to the region of interest for further fabrication processes. Here, we present progress on this front with a view toward performing synthesis (deposition and growth) processes in a scanning transmission electron microscope. An in situ thermal deposition platform is presented, tested, and deposition and growth processes are demonstrated. In particular, we show that isolated Sn atoms can be evaporated from a filament and caught on the nearby sample, demonstrating atomized material delivery. This platform, and future variations, are envisioned to facilitate real-time atomic resolution imaging of growth processes and open new pathways toward atomic fabrication.

The performance of electron energy-loss spectrometers can often be limited by their electron-optical aberrations. Due to recent developments in high energy-resolution and momentum-resolved electron energy loss spectroscopy (EELS), there is renewed interest in optimizing the performance of such spectrometers. For example, the "{\omega}-q" mode of momentum-resolved EELS, which uses a small convergence angle and requires aligning diffraction spots with a slot aperture, presents a challenge for realigning the spectrometer after adjusting the projection lenses. Automated and robust alignment can greatly benefit such a process. The first step towards this goal is automatic and quantitative measurement of spectrometer aberrations. Here we demonstrate the measurement of geometric aberrations and distortions in EELS within a monochromated scanning transmission electron microscope(STEM). To better understand the results, we present a wave mechanical simulation of the experiment. Using the measured aberration and distortion coefficients as inputs to the simulation, we find a good match between the simulation and experiment, verifying the approach used in the simulation, allowing us to assess the accuracy of the measurements. Understanding the errors and inaccuracies in the procedure can guide further progress in aberration measurement and correction for new spectrometer developments., Comment: Submitted to Microscopy and Microanalysis

Electronic states within superconducting vortices hold crucial information about paring mechanisms and topology. While scanning tunneling microscopy/spectroscopy(STM/S) can image the vortices, it is difficult to isolate the intrinsic electronic states from extrinsic effects like subsurface defects and disorders. We combine STM/S with unsupervised machine learning to develop a method for screening out the vortices pinned by embedded disorder in Fe-based superconductors. The approach provides an unbiased way to reveal intrinsic vortex-core states and may address puzzles on Majorana zero modes.

The scanning transmission electron microscope, a workhorse instrument in materials characterization, is being transformed into an atomic-scale material manipulation platform. With an eye on the trajectory of recent developments and the obstacles toward progress in this field, we provide a vision for a path toward an expanded set of capabilities and applications. We reconceptualize the microscope as an instrument for fabrication and synthesis with the capability to image and characterize atomic-scale structural formation as it occurs. Further development and refinement of this approach may have substantial impact on research in microelectronics, quantum information science, and catalysis where precise control over atomic scale structure and chemistry of a few "active sites" can have a dramatic impact on larger scale functionality and where developing a better understanding of atomic scale processes can help point the way to larger scale synthesis approaches.

Direct-write processes enable the alteration or deposition of materials in a continuous, directable, sequential fashion. In this work we demonstrate an electron beam direct-write process in an aberration-corrected scanning transmission electron microscope. This process has several fundamental differences from conventional electron beam induced deposition techniques, where the electron beam dissociates precursor gases into chemically reactive products that bond to a substrate. Here, we use elemental tin (Sn) as a precursor and employ a different mechanism to facilitate deposition. The atomic-sized electron beam is used to generate chemically reactive point defects at desired locations in a graphene substrate. Temperature control of the sample is used to enable the precursor atoms to migrate across the surface and bond to the defect sites thereby enabling atom-by-atom direct-writing.

Atomic-scale engineering typically involves bottom-up approaches, leveraging parameters such as temperature, partial pressures, and chemical affinity to promote spontaneous arrangement of atoms. These parameters are applied globally, resulting in atomic scale features scattered probabilistically throughout the material. In a top-down approach, different regions of the material are exposed to different parameters resulting in structural changes varying on the scale of the resolution. In this work, we combine the application of global and local parameters in an aberration corrected scanning transmission electron microscope (STEM) to demonstrate atomic scale precision patterning of atoms in twisted bilayer graphene. The focused electron beam is used to define attachment points for foreign atoms through the controlled ejection of carbon atoms from the graphene lattice. The sample environment is staged with nearby source materials, such that the sample temperature can induce migration of the source atoms across the sample surface. Under these conditions, the electron-beam (top-down) enables carbon atoms in the graphene to be replaced spontaneously by diffusing adatoms (bottom-up). Using image-based feedback-control, arbitrary patterns of atoms and atom clusters are attached to the twisted bilayer graphene with limited human interaction. The role of substrate temperature on adatom and vacancy diffusion is explored by first-principles simulations.

Recent studies of secondary electron (SE) emission in scanning transmission electron microscopes suggest that material's properties such as electrical conductivity, connectivity, and work function can be probed with atomic scale resolution using a technique known as secondary electron e-beam-induced current (SEEBIC). Here, we apply the SEEBIC imaging technique to a stacked 2D heterostructure device to reveal the spatially resolved electron orbital ionization cross section of an encapsulated WSe2 layer. We find that the double Se lattice site shows higher emission than the W site, which is at odds with first-principles modelling of ionization of an isolated WSe2 cluster. These results illustrate that atomic level SEEBIC contrast within a single material is possible and that an enhanced understanding of atomic scale SE emission is required to account for the observed contrast. In turn, this suggests that subtle information about interlayer bonding and the effect on electron orbitals can be directly revealed with this technique.

Advanced electron microscopy workflows require an ecosystem of microscope instruments and computing systems possibly located at different sites to conduct remotely steered and automated experiments. Current workflow executions involve manual operations for steering and measurement tasks, which are typically performed from control workstations co-located with microscopes; consequently, their operational tempo and effectiveness are limited. We propose an approach based on separate data and control channels for such an ecosystem of Scanning Transmission Electron Microscopes (STEM) and computing systems, for which no general solutions presently exist, unlike the neutron and light source instruments. We demonstrate automated measurement transfers and remote steering of Nion STEM physical instruments over site networks. We propose a Virtual Infrastructure Twin (VIT) of this ecosystem, which is used to develop and test our steering software modules without requiring access to the physical instrument infrastructure. Additionally, we develop a VIT for a multiple laboratory scenario, which illustrates the applicability of this approach to ecosystems connected over wide-area networks, for the development and testing of software modules and their later field deployment., Comment: 11 pages, 16 figures, accepted at IEEE eScience 2022 conference

The advent of modern, high-speed electron detectors has made the collection of multidimensional hyperspectral transmission electron microscopy datasets, such as 4D-STEM, a routine. However, many microscopists find such experiments daunting since such datasets' analysis, collection, long-term storage, and networking remain challenging. Some common issues are the large and unwieldy size of the said datasets, often running into several gigabytes, non-standardized data analysis routines, and a lack of clarity about the computing and network resources needed to utilize the electron microscope fully. However, the existing computing and networking bottlenecks introduce significant penalties in each step of these experiments, and thus, real-time analysis-driven automated experimentation for multidimensional TEM is exceptionally challenging. One solution is integrating microscopy with edge computing, where moderately powerful computational hardware performs the preliminary analysis before handing off the heavier computation to HPC systems. In this perspective, we trace the roots of computation in modern electron microscopy, demonstrate deep learning experiments running on an edge system, and discuss the networking requirements for tying together microscopes, edge computers, and HPC systems., Comment: Perspective on automated microscopy. 3 figures

Electron-beam (e-beam) manipulation of single dopant atoms in an aberration-corrected scanning transmission electron microscope is emerging as a method for directed atomic motion and atom-by-atom assembly. Until now, the dopant species have been limited to atoms closely matched to carbon in terms of ionic radius and capable of strong covalent bonding with carbon atoms in the graphene lattice. In situ dopant insertion into a graphene lattice has thus far been demonstrated only for Si, which is ubiquitously present as a contaminant in this material. Here, we achieve in situ manipulation of Pt atoms and their insertion into the graphene host matrix using the e-beam deposited Pt on graphene as a host system. We further demonstrate a mechanism for stabilization of the Pt atom, enabled through the formation of Si-stabilized Pt heteroatomic clusters attached to the graphene surface. This study provides evidence toward the universality of the e-beam assembly approach, opening a pathway for exploring cluster chemistry through direct assembly., Comment: 23 pages, 6 figures

Graphene has been proposed for use in various nanodevice designs, many of which harness emergent quantum properties for device functionality. However, visualization, measurement, and manipulation become non-trivial at nanometer and atomic scales, representing a significant challenge for device fabrication, characterization, and optimization at length scales where quantum effects emerge. Here, we present proof of principle results at the crossroads between 2D nanoelectronic devices, e-beam-induced modulation, and imaging with secondary electron e-beam induced currents (SEEBIC). We introduce a device platform compatible with scanning transmission electron microscopy investigations. We then show how the SEEBIC imaging technique can be used to visualize conductance and connectivity in single layer graphene nanodevices, even while supported on a thicker substrate (conditions under which conventional imaging fails). Finally, we show that the SEEBIC imaging technique can detect subtle differences in charge transport through time in non-ohmic graphene nanoconstrictions indicating the potential to reveal dynamic electronic processes., Comment: 25 pages, 15 figures

Over the last few years, a new mode for imaging in the scanning transmission electron microscope (STEM) has gained attention as it permits the direct visualization of sample conductivity and electrical connectivity. When the electron beam (e-beam) is focused on the sample in the STEM, secondary electrons (SEs) are generated. If the sample is conductive and electrically connected to an amplifier, the SE current can be measured as a function of the e-beam position. This scenario is similar to the better-known scanning electron microscopy (SEM)-based technique, electron beam induced current (EBIC) imaging except the signal in STEM is generated by the emission of SEs, hence the name SEEBIC, and in this case the current flows in the opposite direction. Here, we provide a brief review of recent work in this area, examine the various contrast generation mechanisms associated with SEEBIC, and illustrate its use for the characterization of graphene nanoribbon devices., Comment: 49 pages, 13 figures

Over the last two decades, Electron Energy Loss Spectroscopy (EELS) imaging with a scanning transmission electron microscope (STEM) has emerged as a technique of choice for visualizing complex chemical, electronic, plasmonic, and phononic phenomena in complex materials and structures. The availability of the EELS data necessitates the development of methods to analyze multidimensional datasets with complex spatial and energy structures. Traditionally, the analysis of these data sets has been based on analysis of individual spectra, one at a time, whereas the spatial structure and correlations between individual spatial pixels containing the relevant information of the physics of underpinning processes have generally been ignored and analyzed only via the visualization as 2D maps. Here we develop a machine learning-based approach and workflows for the analysis of spatial structures in 3D EELS data sets using a combination of dimensionality reduction and multichannel rotationally-invariant variational autoencoders. This approach is illustrated for the analysis of both the plasmonic phenomena in a system of nanowires and in the core excitations in functional oxides using low loss and core loss EELS, respectively. The code developed in this manuscript is open sourced and freely available and provided as a Jupyter notebook for the interested reader here., Comment: Associated Jupyter Notebook: https://github.com/kevinroccapriore/Multichannel-rVAE

Elucidating the symmetry of intertwined orders in exotic superconductors is at the quantum frontier. Recent surface sensitive studies of the topological kagome superconductor CsV$_3$Sb$_5$ discovered a cascade 4a$_0$ superlattice below the charge density wave (CDW) ordering temperature, which can be related to the pair density modulations in the superconducting state. If the 4a$_0$ phase is a bulk and intrinsic property of the kagome lattice, this would form a striking analogy to the stripe order and pair density wave discovered in the cuprate high-temperature superconductors, and the cascade ordering found in twisted bilayer graphene. High-resolution X-ray diffraction has recently been established as an ultra-sensitive probe for bulk translational symmetry-breaking orders, even for short-range orders at the diffusive limit. Here, combining high-resolution X-ray diffraction, scanning tunneling microscopy and scanning transmission electron microscopy, we demonstrate that the 4a$_0$ superstructure emerges uniquely on the surface and hence exclude the 4a$_0$ phase as the origin of any bulk transport or spectroscopic anomaly. Crucially, we show that our detected 2$\times$2$\times$2 CDW order breaks the bulk rotational symmetry to C2, which can be the driver for the bulk nematic orders and nematic surface superlattices including the 4a$_0$ phase. Our high-resolution data impose decisive spatial symmetry constraints on emergent electronic orders in the kagome superconductor CsV$_3$Sb$_5$.

Quantum materials (QMs) with strong correlation and non-trivial topology are indispensable to next-generation information and computing technologies. Exploitation of topological band structure is an ideal starting point to realize correlated topological QMs. Herein, we report that strain-induced symmetry modification in correlated oxide SrNbO3 thin films creates an emerging topological band structure. Dirac electrons in strained SrNbO3 films reveal ultra-high mobility (100,000 cm2/Vs), exceptionally small effective mass (0.04me), and non-zero Berry phase. More importantly, strained SrNbO3 films reach the extreme quantum limit, exhibiting a sign of fractional occupation of Landau levels and giant mass enhancement. Our results suggest that symmetry-modified SrNbO3 is a rare example of a correlated topological QM, in which strong correlation of Dirac electrons leads to the realization of fractional occupation of Landau levels., Comment: accepted in Sci. Adv

Hexagonal perovskites are an attractive group of materials due to their various polymorph phases and rich structure-property relationships. BaRuO3 (BRO) is a prototypical hexagonal perovskite, in which the electromagnetic properties are significantly modified depending on its atomic structure. Whereas thin-film epitaxy would vastly expand the application of hexagonal perovskites by epitaxially stabilizing various metastable polymorphs, the atomic structure of epitaxial hexagonal perovskites, especially at the initial growth stage, has rarely been investigated. In this study, we show that an intriguing nucleation behavior takes place during the initial stabilization of a hexagonal perovskite 9R BaRuO3 (BRO) thin film on a (111) SrTiO3 (STO) substrate. We use high-resolution high-angle annular dark field scanning transmission electron microscopy in combination with geometrical phase analysis to understand the local strain relaxation behavior. We find that nano-scale strained layers, composed of different RuO6 octahedral stacking, are initially formed at the interface, followed by a relaxed single crystal9R BRO thin film. Within the interface layer, hexagonal BROs are nucleated on the STO (111) substrate by both corner- and face-sharing. More interestingly, we find that the boundaries between the differently-stacked nucleation layers, i.e. heterostructural boundaries facilitates strain relaxation, in addition to the formation of conventional misfit dislocations evolving from homostructural boundaries. Our observations reveal an important underlying mechanism to understand the thin-film epitaxy and strain accommodation in hexagonal perovskites., Comment: 18 pages, 3 figures

The precise positioning of dopant atoms within bulk crystal lattices could enable novel applications in areas including solid-state sensing and quantum computation. Established scanning probe techniques are capable tools for the manipulation of surface atoms, but at a disadvantage due to their need to bring a physical tip into contact with the sample. This has prompted interest in electron-beam techniques, followed by the first proof-of-principle experiment of bismuth dopant manipulation in crystalline silicon. Here, we use first principles modeling to discover a novel indirect exchange mechanism that allows electron impacts to non-destructively move dopants with atomic precision within the silicon lattice. However, this mechanism only works for the two heaviest group V donors with split-vacancy configurations, Bi and Sb. We verify our model by directly imaging these configurations for Bi, and by demonstrating that the promising nuclear spin qubit Sb can be manipulated using a focused electron beam., Comment: 23 pages, 4 figures, 1 table

Twin domains are often found as structural defects in symmetry mismatched epitaxial thin films. The delafossite ABO2, which has a rhombohedral structure, is a good example that often forms twin domains. Although bulk metallic delafossites are known to be the most conducting oxides, the high conductivity is yet to be realized in thin film forms. Suppressed conductivity found in thin films is mainly caused by the formation of twin domains, and their boundaries can be a source of scattering centers for charge carriers. To overcome this challenge, the underlying mechanism for their formation must be understood, so that such defects can be controlled and eliminated. Here, we report the origin of structural twins formed in a CuCrO2 delafossite thin film on a substrate with hexagonal or triangular symmetries. A robust heteroepitaxial relationship is found for the delafossite film with the substrate, and the surface termination turns out to be critical to determine and control the domain structure of epitaxial delafossites. Based on such discoveries, we also demonstrate a twin-free epitaxial thin films grown on high-miscut substrates. This finding provides an important synthesis strategy for growing single domain delafossite thin films and can be applied to other delafossites for epitaxial synthesis of high-quality thin films.

Spatial confinement of matter in functional nanostructures has propelled these systems to the forefront of nanoscience, both as a playground for exotic physics and quantum phenomena and in multiple applications including plasmonics, optoelectronics, and sensing. In parallel, the emergence of monochromated electron energy loss spectroscopy (EELS) has enabled exploration of local nanoplasmonic functionalities within single nanoparticles and the collective response of nanoparticle assemblies, providing deep insight into the associated mechanisms. However, modern synthesis processes for plasmonic nanostructures are often limited in the types of accessible geometry and materials, and even then, limited to spatial precisions on the order of tens of nm, precluding the direct exploration of critical aspects of the structure-property relationships. Here, we use the atomic-sized probe of the scanning transmission electron microscope (STEM) to perform precise sculpting and design of nanoparticle configurations. Furthermore, using low-loss (EELS), we provide dynamic analyses of evolution of the plasmonic response during the sculpting process. We show that within self-assembled systems of nanoparticles, individual nanoparticles can be selectively removed, reshaped, or arbitrarily patterned with nanometer-level resolution, effectively modifying the plasmonic response in both space and energy domains. This process significantly increases the scope for design possibilities and presents opportunities for arbitrary structure development, which are ultimately key for nanophotonic design. Nanosculpting introduces yet another capability to the electron microscope.

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics through self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiment (AE) in imaging. Here, we aim to analyze the major pathways towards AE in imaging methods with sequential image formation mechanisms, focusing on scanning probe microscopy (SPM) and (scanning) transmission electron microscopy ((S)TEM). We argue that automated experiments should necessarily be discussed in a broader context of the general domain knowledge that both informs the experiment and is increased as the result of the experiment. As such, this analysis should explore the human and ML/AI roles prior to and during the experiment, and consider the latencies, biases, and knowledge priors of the decision-making process. Similarly, such discussion should include the limitations of the existing imaging systems, including intrinsic latencies, non-idealities and drifts comprising both correctable and stochastic components. We further pose that the role of the AE in microscopy is not the exclusion of human operators (as is the case for autonomous driving), but rather automation of routine operations such as microscope tuning, etc., prior to the experiment, and conversion of low latency decision making processes on the time scale spanning from image acquisition to human-level high-order experiment planning.

The 4D scanning transmission electron microscopy (STEM) method has enabled mapping of the structure and functionality of solids on the atomic scale, yielding information-rich data sets containing information on the interatomic electric and magnetic fields, structural and electronic order parameters, and other symmetry breaking distortions. A critical bottleneck on the pathway toward harnessing 4D-STEM for materials exploration is the dearth of analytical tools that can reduce complex 4D-STEM data sets to physically relevant descriptors. Classical machine learning (ML) methods such as principal component analysis and other linear unmixing techniques are limited by the presence of multiple point-group symmetric variants, where diffractograms from each rotationally equivalent position will form its own component. This limitation even holds for more complex ML methods, such as convolutional neural networks. Here, we propose and implement an approach for the systematic exploration of symmetry breaking phenomena from 4D-STEM data sets using rotationally invariant variational autoencoders (rrVAE), which is designed to disentangle the general rotation of the object from other latent representations. The implementation of purely rotational rrVAE is discussed as are applications to simulated data for graphene and zincblende structures that illustrate the effect of site symmetry breaking. Finally, the rrVAE analysis of 4D-STEM data of vacancies in graphene is illustrated and compared to the classical center-of-mass (COM) analysis. This approach is universal for probing of symmetry breaking phenomena in complex systems and can be implemented for a broad range of diffraction methods exploring the 2D diffraction space of the system, including X-ray ptychography, electron backscatter diffraction (EBSD), and more complex methods.

Advances in hyperspectral imaging modes including electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM) bring forth the challenges of exploratory and subsequently physics-based analysis of multidimensional data sets. The (by now common) multivariate unsupervised linear unmixing methods and their nonlinear analogs generally explore similarities in the energy dimension but ignore correlations in the spatial domain. At the same time, Gaussian process (GP) methods that explicitly incorporate spatial correlations in the form of kernel functions tend to be extremely computationally intensive, while the use of inducing point-based sparse methods often leads to reconstruction artefacts. Here, we suggest and implement a parallel GP method operating on the full spatial domain and reduced representations in the energy domain. In this parallel GP, the information between the components is shared via a common spatial kernel structure while allowing for variability in the relative noise magnitude or image morphology. We explore the role of common spatial structures and kernel constraints on the quality of the reconstruction and suggest an approach for estimating these factors from the experimental data. Application of this method to an example EELS dataset demonstrates that spatial information contained in higher-order components can be reconstructed and spatially localized. This approach can be further applied to other hyperspectral and multimodal imaging modes. The notebooks developed in this manuscript are freely available as part of a GPim package (https://github.com/ziatdinovmax/GPim).

Interface structures in complex oxides remain one of the active areas of condensed matter physics research, largely enabled by recent advances in scanning transmission electron microscopy (STEM). Yet the nature of the STEM contrast in which the structure is projected along the given direction precludes separation of possible structural models. Here, we utilize deep convolutional neural networks (DCNN) trained on simulated 4D scanning transmission electron microscopy (STEM) datasets to predict structural descriptors of interfaces. We focus on the widely studied interface between LaAlO3 and SrTiO3, using dynamical diffraction theory and leveraging high performance computing to simulate thousands of possible 4D STEM datasets to train the DCNN to learn properties of the underlying structures on which the simulations are based. We validate the DCNN on simulated data and show that it is possible (with >95% accuracy) to identify a physically rough from a chemically diffuse interface and achieve 85% accuracy in determination of buried step positions within the interface. The method shown here is general and can be applied for any inverse imaging problem where forward models are present., Comment: 18 pages, 4 figures

Four-dimensional scanning transmission electron microscopy (4D-STEM) is one of the most rapidly growing modes of electron microscopy imaging. The advent of fast pixelated cameras and the associated data infrastructure have greatly accelerated this process. Yet conversion of the 4D datasets into physically meaningful structure images in real-space remains an open issue. In this work, we demonstrate that, it is possible to systematically create filters that will affect the apparent resolution or even qualitative features of the real-space structure image, reconstructing artificially generated patterns. As initial efforts, we explore statistical model selection algorithms, aiming for robustness and reliability of estimated filters. This statistical model selection analysis demonstrates the need for regularization and cross-validation of inversion methods to robustly recover structure from high-dimensional diffraction datasets.

Materials science is undergoing profound changes due to advances in characterization instrumentation that have resulted in an explosion of data in terms of volume, velocity, variety and complexity. Harnessing these data for scientific research requires an evolution of the associated computing and data infrastructure, bridging scientific instrumentation with super- and cloud- computing. Here, we describe Universal Spectroscopy and Imaging Data (USID), a data model capable of representing data from most common instruments, modalities, dimensionalities, and sizes. We pair this schema with the hierarchical data file format (HDF5) to maximize compatibility, exchangeability, traceability, and reproducibility. We discuss a family of community-driven, open-source, and free python software packages for storing, processing and visualizing data. The first is pyUSID which provides the tools to read and write USID HDF5 files in addition to a scalable framework for parallelizing data analysis. The second is Pycroscopy, which provides algorithms for scientific analysis of nanoscale imaging and spectroscopy modalities and is built on top of pyUSID and USID. The instrument-agnostic nature of USID facilitates the development of analysis code independent of instrumentation and task in Pycroscopy which in turn can bring scientific communities together and break down barriers in the age of open-science. The interested reader is encouraged to be a part of this ongoing community-driven effort to collectively accelerate materials research and discovery through the realms of big data.

Four-dimensional scanning transmission electron microscopy (4D-STEM) of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomic structure and atomic electric fields. However, efficient processing and interpretation of large volumes of data remain challenging, especially for two-dimensional or light materials because the diffraction signal recorded on the pixelated arrays is weak. Here we employ data-driven manifold leaning approaches for straightforward visualization and exploration analysis of the 4D-STEM datasets, distilling real-space neighboring effects on atomically resolved deflection patterns from single-layer graphene, with single dopant atoms, as recorded on a pixelated detector. These extracted patterns relate to both individual atom sites and sublattice structures, effectively discriminating single dopant anomalies via multi-mode views. We believe manifold learning analysis will accelerate physics discoveries coupled between data-rich imaging mechanisms and materials such as ferroelectric, topological spin and van der Waals heterostructures.

Recent advances in scanning transmission electron and scanning tunneling microscopies allow researchers to measure materials structural and electronic properties, such as atomic displacements and charge density modulations, at an Angstrom scale in real space. At the same time, the ability to quickly acquire large, high-resolution datasets has created a challenge for rapid physics-based analysis of images that typically contain several hundreds to several thousand atomic units. Here we demonstrate a universal deep-learning based framework for locating and characterizing atomic species in the lattice, which can be applied to different types of atomically resolved measurements on different materials. Specifically, by inspecting and categorizing features in the output layer of a convolutional neural network, we are able to detect structural and electronic 'anomalies' associated with the presence of point defects in a tungsten disulfide monolayer, non-uniformity of the charge density distribution around specific lattice sites on the surface of strongly correlated oxides, and transition between different structural states of buckybowl molecules. We further extended our method towards tracking, from one image frame to another, minute distortions in the geometric shape of individual Si dumbbells in a 3-dimensional Si sample, which are associated with a motion of lattice defects and impurities. Due the applicability of our framework to both scanning tunneling microscopy and scanning transmission electron microscopy measurements, it can provide a fast and straightforward way towards creating a unified database of defect-property relationships from experimental data for each material.

Semiconductor fabrication is a mainstay of modern civilization, enabling the myriad applications and technologies that underpin everyday life. However, while sub-10 nanometer devices are already entering the mainstream, the end of the Moore's Law roadmap still lacks tools capable of bulk semiconductor fabrication on sub-nanometer and atomic levels, with probe-based manipulation being explored as the only known pathway. Here we demonstrate that the atomic-sized focused beam of a scanning transmission electron microscope can be used to manipulate semiconductors such as Si on the atomic level, inducing growth of crystalline Si from the amorphous phase, reentrant amorphization, milling, and dopant-front motion. These phenomena are visualized in real time with atomic resolution. We further implement active feedback control based on real-time image analytics to control the e-beam motion, enabling shape control and providing a pathway for atom-by-atom correction of fabricated structures in the near future. These observations open a new epoch for atom-by-atom manufacturing in bulk, the long-held dream of nanotechnology.