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Register a new userDeep analytics of atomically-resolved images: manifest and latent features
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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.
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An approach for the analysis of atomically resolved scanning transmission electron microscopy data with multiple ferroic variants in the presence of imaging non-idealities and chemical variabilities based on a rotationally invariant variational autoencoder (rVAE) is presented. We show that an optimal local descriptor for the analysis is a sub-image centered at specific atomic units, since materials and microscope distortions preclude the use of an ideal lattice as a reference point. The applicability of unsupervised clustering and dimensionality reduction methods is explored and are shown to produce clusters dominated by chemical and microscope effects, with a large number of classes required to establish the presence of rotational variants. Comparatively, the rVAE allows extraction of the angle corresponding to the orientation of ferroic variants explicitly, enabling straightforward identification of the ferroic variants as regions with constant or smoothly changing latent variables and sharp orientational changes. This approach allows further exploration of the chemical variability by separating the rotational degrees of freedom via rVAE and searching for remaining variability in the system. The code used in the manuscript is available at https://github.com/saimani5/ferroelectric_domains_rVAE.
Aurivillius ferroelectric $Bi_2WO_6$ (BWO) encompasses a broad range of functionalities, including robust fatigue-free ferroelectricity, high photocatalytic activity, and ionic conductivity. Despite these promising characteristics, an in-depth study on the growth of BWO thin films and ferroelectric characterization, especially at the atomic scale, is still lacking. Here, we report pulsed laser deposition (PLD) of BWO thin films on (001) $SrTiO_3$ substrates and characterization of ferroelectricity using the scanning transmission electron microscopy (STEM) and piezoresponse force microscopy (PFM) techniques. We show that the background oxygen gas pressure used during PLD growth mainly determines the phase stability of BWO films, whereas the influence of growth temperature is comparatively minor. Atomically resolved STEM study of a fully strained BWO film revealed collective in-plane polar off-centering displacement of W atoms. We estimated the spontaneous polarization value based on polar displacement mapping to be about 54 $pm$ 4 ${mu}C cm^{-2}$, which is in good agreement with the bulk polarization value. Furthermore, we found that pristine film is composed of type-I and type-II domains, with mutually orthogonal polar axes. Complementary PFM measurements further elucidated that the coexisting type-I and type-II domains formed a multidomain state that consisted of 90$deg$ domain walls (DWs) alongside multiple head-to-head and tail-to-tail 180$deg$ DWs. Application of an electrical bias led to in-plane 180$deg$ polarization switching and 90$deg$ polarization rotation, highlighting a unique aspect of domain switching, which is immune to substrate-induced strain.
Atomic structures and adatom geometries of surfaces encode information about the thermodynamics and kinetics of the processes that lead to their formation, and which can be captured by a generative physical model. Here we develop a workflow based on a machine learning-based analysis of scanning tunneling microscopy images to reconstruct the atomic and adatom positions, and a Bayesian optimization procedure to minimize statistical distance between the chosen physical models and experimental observations. We optimize the parameters of a 2- and 3-parameter Ising model describing surface ordering and use the derived generative model to make predictions across the parameter space. For concentration dependence, we compare the predicted morphologies at different adatom concentrations with the dissimilar regions on the sample surfaces that serendipitously had different adatom concentrations. The proposed workflow is universal and can be used to reconstruct the thermodynamic models and associated uncertainties from the experimental observations of materials microstructures. The code used in the manuscript is available at https://github.com/saimani5/Adatom_interactions.
The predictability of a certain effect or phenomenon is often equated with the knowledge of relevant physical laws, typically understood as a functional or numerically derived relationship between the observations and known states of the system. Correspondingly, observations inconsistent with prior knowledge can be used to derive new knowledge on the nature of the system or indicate the presence of yet unknown mechanisms. Here we explore the applicability of Gaussian Processes (GP) to establish predictability and uncertainty of local behaviors from multimodal observations, providing an alternative to this classical paradigm. Using atomic-resolution Scanning Transmission Electron Microscopy (STEM) of multiferroic Sm-doped BiFeO3 across a broad composition range, we directly visualize the atomic structure and structural, physical, and chemical order parameter fields for the material. GP regression is used to establish the predictability of the local polarization field from different groups of parameters, including the adjacent polarization values and several combinations of physical and chemical descriptors, including lattice parameters, column intensities, etc. We observe that certain elements of microstructure including charged and uncharged domain walls and interfaces with the substrate are best predicted with specific combinations of descriptors, and this predictability and their associated uncertainties are consistent across the composition series. The associated generative physical mechanisms are discussed. We argue that predictability and uncertainty in observational data offers a new pathway to probe the physics of condensed matter systems from multimodal local observations.
Atom probe tomography (APT) helps elucidate the link between the nanoscale chemical variations and physical properties, but it has limited structural resolution. Field ion microscopy (FIM), a predecessor technique to APT, is capable of attaining atomic resolution along certain sets of crystallographic planes albeit at the expense of elemental identification. We demonstrate how two commercially-available atom probe instruments, one with a straight flight path and one fitted with a reflectron-lens, can be used to acquire time-of-flight mass spectrometry data concomitant with a FIM experiment. We outline various experimental protocols making use of temporal and spatial correlations to best discriminate field evaporated signals from the large field ionised background signal, demonstrating an unsophisticated yet efficient data mining strategy to provide this discrimination. We discuss the remaining experimental challenges that need be addressed, notably concerned with accurate detection and identification of individual field evaporated ions contained within the high field ionised flux that contributes to a FIM image. Our hybrid experimental approach can, in principle, exhibit true atomic resolution with elemental discrimination capabilities, neither of which atom probe nor field ion microscopy can individually fully deliver - thereby making this new approach, here broadly termed analytical field ion microscope (aFIM), unique.
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