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Advances in electron and scanning probe microscopies have led to a wealth of atomically resolved structural and electronic data, often with ∼1-10 pm precision. However, knowledge generation from such data requires the development of a physics-based robust framework to link the observed structures to macroscopic chemical and physical descriptors, including single phase regions, order parameter fields, interfaces, and structural and topological defects. Here, we develop an approach based on a synergy of sliding window Fourier transform to capture the local analog of traditional structure factors combined with blind linear unmixing of the resultant 4D data set. This deep data analysis is ideally matched to the underlying physics of the problem and allows reconstruction of the a priori unknown structure factors of individual components and their spatial localization. We demonstrate the principles of this approach using a synthetic data set and further apply it for extracting chemical and physically relevant information from electron and scanning tunneling microscopy data. This method promises to dramatically speed up crystallographic analysis in atomically resolved data, paving the road toward automatic local structure-property determinations in crystalline and quasi-ordered systems, as well as systems with competing structural and electronic order parameters. [ABSTRACT FROM AUTHOR]

We performed investigations of the atomic-scale surface structure of epitaxial La5/8Ca3/8MnO3 thin films as a model system dependent on growth conditions in pulsed laser deposition with emphasis on film growth kinetics. Postdeposition in situ scanning tunneling microscopy was combined with in operando reflective high-energy electron diffraction to monitor the film growth and ex situ X-ray diffraction for structural analysis. We find a correlation between the out-of-plane lattice parameter and both adspecies mobility and height of the Ehrlich-Schwoebel barrier, with mobility of adatoms greater over the cationically stoichiometric terminations. The data suggest that the out-of-plane lattice parameter is dependent on the mechanism of epitaxial strain relaxation, which is controlled by the oxidative power of the deposition environment. [ABSTRACT FROM AUTHOR]

Scientific advancement is universally based on the dynamic interplay between theoretical insights, modeling, and experimental discoveries. However, this feedback loop is often slow, including delayed community interactions and the gradual integration of experimental data into theoretical frameworks. This challenge is particularly exacerbated in domains dealing with high-dimensional object spaces, such as molecules and complex microstructures. Hence, the integration of theory within automated and autonomous experimental setups, or theory in the loop-automated experiment, is emerging as a crucial objective for accelerating scientific research. The critical aspect is to use not only theory but also on-the-fly theory updates during the experiment. Here, we introduce a method for integrating theory into the loop through Bayesian conavigation of theoretical model space and experimentation. Our approach leverages the concurrent development of surrogate models for both simulation and experimental domains at the rates determined by latencies and costs of experiments and computation, alongside the adjustment of control parameters within theoretical models to minimize epistemic uncertainty over the experimental object spaces. This methodology facilitates the creation of digital twins of material structures, encompassing both the surrogate model of behavior that includes the correlative part and the theoretical model itself. While being demonstrated here within the context of functional responses in ferroelectric materials, our approach holds promise for broader applications, such as the exploration of optical properties in nanoclusters, microstructure-dependent properties in complex materials, and properties of molecular systems.

We demonstrate direct-write patterning of single and multilayer MoS 2 via a focused electron beam-induced etching (FEBIE) process mediated with the XeF 2 precursor. MoS 2 etching is performed at various currents, areal doses, on different substrates, and characterized using scanning electron and atomic force microscopies as well as Raman and photoluminescence spectroscopies. Scanning transmission electron microscopy reveals a sub-40 nm etching resolution and the progression of point defects and lateral etching of the consequent unsaturated bonds. The results confirm that the electron beam-induced etching process is minimally invasive to the underlying material in comparison to ion beam techniques, which damage the subsurface material. Single-layer MoS 2 field-effect transistors are fabricated, and device characteristics are compared for channels that are edited via the selected area etching process. The source-drain current at constant gate and source-drain voltage scale linearly with the edited channel width. Moreover, the mobility of the narrowest channel width decreases, suggesting that backscattered and secondary electrons collaterally affect the periphery of the removed area. Focused electron beam doses on single-layer transistors below the etching threshold were also explored as a means to modify/thin the channel layer. The FEBIE exposures showed demonstrative effects via the transistor transfer characteristics, photoluminescence spectroscopy, and Raman spectroscopy. While strategies to minimize backscattered and secondary electron interactions outside of the scanned regions require further investigation, here, we show that FEBIE is a viable approach for selective nanoscale editing of MoS 2 devices.

The dynamics of complex topological defects in ferroelectric materials is explored using automated experimentation in piezoresponse force microscopy. Specifically, a complex trigger system ( i.e. , "FerroBot") is employed to study metastable domain-wall dynamics in Pb 0.6 Sr 0.4 TiO 3 thin films. Several regimes of superdomain wall dynamics have been identified, including smooth domain-wall motion and significant reconfiguration of the domain structures. We have further demonstrated that microscopic mechanisms of the domain-wall dynamics can be identified; i.e. , domain-wall bending can be separated from irreversible domain reconfiguration regimes. In conjunction, phase-field modeling was used to corroborate the observed mechanisms. As such, the observed superdomain dynamics can provide a model system for classical ferroelectric dynamics, much like how colloidal crystals provide a model system for atomic and molecular systems.

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 to self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiments (AE) in imaging. Here, we aim to analyze the major pathways toward 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 prior knowledge 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. Overall, we argue that ML/AI can dramatically alter the (S)TEM and SPM fields; however, this process is likely to be highly nontrivial and initiated by combined human-ML workflows and will bring challenges both from the microscope and ML/AI sides. At the same time, these methods will enable opportunities and paradigms for scientific discovery and nanostructure fabrication.

Polarization dynamics in ferroelectric materials are explored via automated experiment in piezoresponse force microscopy/spectroscopy (PFM/S). A Bayesian optimization (BO) framework for imaging is developed, and its performance for a variety of acquisition and pathfinding functions is explored using previously acquired data. The optimized algorithm is then deployed on an operational scanning probe microscope (SPM) for finding areas of large electromechanical response in a thin film of PbTiO 3 , with results showing that, with just 20% of the area sampled, most high-response clusters were captured. This approach can allow performing more complex spectroscopies in SPM that were previously not possible due to time constraints and sample stability. Improvements to the framework to enable the incorporation of more prior information and improve efficiency further are modeled and discussed.

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 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.

Due to an extremely diverse phase space, La 1- x Sr x MnO 3 , as with other manganites, offers a wide range of tunability and applications including colossal magnetoresistance and use as spin-polarized electrodes. Here, we study an unprecedented, exotic surface reconstruction (6 × 6) in La 1- x Sr x MnO 3 ( x = 0.3) observed via low-energy electron diffraction (LEED). Scanning tunneling microscopy (STM) shows the surface is relatively flat, with unit-cell step heights, and X-ray photoelectron spectroscopy (XPS) reveals a strong degree of Sr segregation at the surface. By combining electron diffraction and first-principles computations, we propose that the long-range surface reconstruction consists of a Sr-segregated surface with La (6 × 6) ordering. This study expands our understanding of manganite systems and underscores their ability to form interesting surface reconstructions, driven largely by cation segregation that can potentially be controlled for tuning surface ordering.

Relaxor ferroelectrics exhibit a range of interesting material behavior, including high electromechanical response, polarization rotations, as well as temperature and electric field-driven phase transitions. The origin of this unusual functional behavior remains elusive due to limited knowledge on polarization dynamics at the nanoscale. Piezoresponse force microscopy and associated switching spectroscopy provide access to local electromechanical properties on the micro- and nanoscale, which can help to address some of these gaps in our knowledge. However, these techniques are inherently prone to artefacts caused by signal contributions emanating from electrostatic interactions between tip and sample. Understanding functional behavior of complex, disordered systems like relaxor materials with unknown electromechanical properties therefore requires a technique that allows distinguishing between electromechanical and electrostatic response. Here, contact Kelvin probe force microscopy (cKPFM) is used to gain insight into the evolution of local electromechanical and capacitive properties of a representative relaxor material lead lanthanum zirconate across the phase transition from a ferroelectric to relaxor state. The obtained multidimensional data set was processed using an unsupervised machine learning algorithm to detect variations in functional response across the probed area and temperature range. Further analysis showed the formation of two separate cKPFM response bands below 50 °C, providing evidence for polarization switching. At higher temperatures only one band is observed, indicating an electrostatic origin of the measured response. In addition, the junction potential difference, which was extracted from the cKPFM data, becomes independent of the temperature in the relaxor state. The combination of this multidimensional voltage spectroscopy technique and machine learning allows to identify the origin of the measured functional response and to decouple ferroelectric from electrostatic phenomena necessary to understand the functional behavior of complex, disordered systems like relaxor materials.

The coupling between ionic degrees of freedom and ferroelectricity has received renewed attention in recent years, given that surface electrochemical processes have been shown to be intrinsically linked to ferroelectric phase stability in ultrathin ferroelectric films. However, the coupling between bulk ionic transport and local polarization switching has received less attention, as typically the bulk ionic mobilities are low for common ferroelectrics at room temperature. Here, we use the coupled band-excitation method in conjunction with site-correlated time-of-flight secondary ion mass spectrometry, to determine the coupling between ferroelectric switching and ionic motion in single crystal KTiOPO 4 . The local scanning probe measurements indicate a substantial softening, as determined by resonant frequency changes, during reversal of polarization along one direction. These changes are correlated with the mass spectrometry measurements, showing a polarization-dependent accumulation of K ions at the polar surfaces, thus corroborating their role in the screening process. These studies shed light on the interplay between ionic dynamics and bulk ferroelectric switching and have implications for studies on domain wall conductivity, chemical switching, and bulk and surface-screening phenomena.

Polarization switching in ferroelectric materials underpins a multitude of applications ranging from nonvolatile memories to data storage to ferroelectric lithography. While traditionally considered to be a functionality of the material only, basic theoretical considerations suggest that switching is expected to be intrinsically linked to changes in the electrochemical state of the surface. Hence, the properties and dynamics of the screening charges can affect or control the switching dynamics. Despite being recognized for over 50 years, analysis of these phenomena remained largely speculative. Here, we explore polarization switching on the prototypical LiNbO 3 surface using the combination of contact mode Kelvin probe force microscopy and chemical imaging by time-of-flight mass-spectrometry and demonstrate pronounced chemical differences between the domains. These studies provide a consistent explanation to the anomalous polarization and surface charge behavior observed in LiNbO 3 and point to new opportunities in chemical control of polarization dynamics in thin films and crystals via control of surface chemistry, complementing traditional routes via bulk doping, and substrate-induced strain and tilt systems.

Recent advances in scanning transmission electron and scanning probe microscopies have opened exciting opportunities in probing the materials structural parameters and various functional properties in real space with angstrom-level precision. This progress has been accompanied by an exponential increase in the size and quality of data sets produced by microscopic and spectroscopic experimental techniques. These developments necessitate adequate methods for extracting relevant physical and chemical information from the large data sets, for which a priori information on the structures of various atomic configurations and lattice defects is limited or absent. Here we demonstrate an application of deep neural networks to extract information from atomically resolved images including location of the atomic species and type of defects. We develop a "weakly supervised" approach that uses information on the coordinates of all atomic species in the image, extracted via a deep neural network, to identify a rich variety of defects that are not part of an initial training set. We further apply our approach to interpret complex atomic and defect transformation, including switching between different coordination of silicon dopants in graphene as a function of time, formation of peculiar silicon dimer with mixed 3-fold and 4-fold coordination, and the motion of molecular "rotor". This deep learning-based approach resembles logic of a human operator, but can be scaled leading to significant shift in the way of extracting and analyzing information from raw experimental data.

We describe and test theoretical principles for consistent integration of experimental and ab initio data from diverse sources into a single statistical mechanical model. The approach is based on the recently introduced concept of statistical distance between partition functions, uses a simple vector algebra formalism to describe measurement outcomes and coarse-graining operations, and takes advantage of thermodynamic perturbation expressions for fast exploration of the model parameter space. The methodology is demonstrated on a combination of thermodynamic, structural, spectroscopic, and imaging pseudoexperimental data along with ab initio-type trajectories, which are incorporated into models describing the behavior of a near-critical fluid, liquid water, thin-film mixed oxides, and binary alloys. We evaluate how different target data constrain the model parameters and how the uncertainty associated with incomplete target information and limited sampling of the system's phase space might influence the choice of optimal parameters.

Tremendous strides in experimental capabilities of scanning transmission electron microscopy and scanning tunneling microscopy (STM) over the past 30 years made atomically resolved imaging routine. However, consistent integration and use of atomically resolved data with generative models is unavailable, so information on local thermodynamics and other microscopic driving forces encoded in the observed atomic configurations remains hidden. Here, we present a framework based on statistical distance minimization to consistently utilize the information available from atomic configurations obtained from an atomically resolved image and extract meaningful physical interaction parameters. We illustrate the applicability of the framework on an STM image of a FeSe x Te 1-x superconductor, with the segregation of the chalcogen atoms investigated using a nonideal interacting solid solution model. This universal method makes full use of the microscopic degrees of freedom sampled in an atomically resolved image and can be extended via Bayesian inference toward unbiased model selection with uncertainty quantification.

Measuring the diffusion of ions and vacancies at nanometer length scales is crucial to understanding fundamental mechanisms driving technologies as diverse as batteries, fuel cells, and memristors; yet such measurements remain extremely challenging. Here, we employ a multimodal scanning probe microscopy (SPM) technique to explore the interplay between electronic, elastic, and ionic processes via first-order reversal curve I-V measurements in conjunction with electrochemical strain microscopy (ESM). The technique is employed to investigate the diffusion of oxygen vacancies in model epitaxial nickel oxide (NiO) nanocrystals with resistive switching characteristics. Results indicate that opening of the ESM hysteresis loop is strongly correlated with changes to the resonant frequency, hinting that elastic changes stem from the motion of oxygen (or cation) vacancies in the probed volume of the SPM tip. These changes are further correlated to the current measured on each nanostructure, which shows a hysteresis loop opening at larger (∼2.5 V) voltage windows, suggesting the threshold field for vacancy migration. This study highlights the utility of local multimodal SPM in determining functional and chemical changes in nanoscale volumes in nanostructured NiO, with potential use to explore a wide variety of materials including phase-change memories and memristive devices in combination with site-correlated chemical imaging tools.

Scanning probe microscopy (SPM) techniques have opened the door to nanoscience and nanotechnology by enabling imaging and manipulation of the structure and functionality of matter at nanometer and atomic scales. Here, we analyze the scientific discovery process in SPM by following the information flow from the tip-surface junction, to knowledge adoption by the wider scientific community. We further discuss the challenges and opportunities offered by merging SPM with advanced data mining, visual analytics, and knowledge discovery technologies.

Epitaxial strain provides a powerful approach to manipulate physical properties of materials through rigid compression or extension of their chemical bonds via lattice-mismatch. Although symmetry-mismatch can lead to new physics by stabilizing novel interfacial structures, challenges in obtaining atomic-level structural information as well as lack of a suitable approach to separate it from the parasitical lattice-mismatch have limited the development of this field. Here, we present unambiguous experimental evidence that the symmetry-mismatch can be strongly controlled by dimensionality and significantly impact the collective electronic and magnetic functionalities in ultrathin perovskite LaCoO3/SrTiO3 heterojunctions. State-of-art diffraction and microscopy reveal that symmetry breaking dramatically modifies the interfacial structure of CoO6 octahedral building-blocks, resulting in expanded octahedron volume, reduced covalent screening, and stronger electron correlations. Such phenomena fundamentally alter the electronic and magnetic behaviors of LaCoO3 thin-films. We conclude that for epitaxial systems, correlation strength can be tuned by changing orbital hybridization, thus affecting the Coulomb repulsion, U, instead of by changing the band structure as the common paradigm in bulks. These results clarify the origin of magnetic ordering for epitaxial LaCoO3 and provide a route to manipulate electron correlation and magnetic functionality by orbital engineering at oxide heterojunctions.

The trend to reduce device dimensions demands increasing attention to atomic-scale details of structure of thin films as well as to pathways to control it. This is of special importance in the systems with multiple competing interactions. We have used in situ scanning tunneling microscopy to image surfaces of La5/8Ca3/8MnO3 films grown by pulsed laser deposition. The atomically resolved imaging was combined with in situ angle-resolved X-ray photoelectron spectroscopy. We find a strong effect of the background oxygen pressure during deposition on structural and chemical features of the film surface. Deposition at 50 mTorr of O2 leads to mixed-terminated film surfaces, with B-site (MnO2) termination being structurally imperfect at the atomic scale. A relatively small reduction of the oxygen pressure to 20 mTorr results in a dramatic change of the surface structure leading to a nearly perfectly ordered B-site terminated surface with only a small fraction of A-site (La,Ca)O termination. This is accompanied, however, by surface roughening at a mesoscopic length scale. The results suggest that oxygen has a strong link to the adatom mobility during growth. The effect of the oxygen pressure on dopant surface segregation is also pronounced: Ca surface segregation is decreased with oxygen pressure reduction.

Reflection high energy electron diffraction (RHEED) has by now become a standard tool for in situ monitoring of film growth by pulsed laser deposition and molecular beam epitaxy. Yet despite the widespread adoption and wealth of information in RHEED images, most applications are limited to observing intensity oscillations of the specular spot, and much additional information on growth is discarded. With ease of data acquisition and increased computation speeds, statistical methods to rapidly mine the data set are now feasible. Here, we develop such an approach to the analysis of the fundamental growth processes through multivariate statistical analysis of a RHEED image sequence. This approach is illustrated for growth of La(x)Ca(1-x)MnO(3) films grown on etched (001) SrTiO(3) substrates, but is universal. The multivariate methods including principal component analysis and k-means clustering provide insight into the relevant behaviors, the timing and nature of a disordered to ordered growth change, and highlight statistically significant patterns. Fourier analysis yields the harmonic components of the signal and allows separation of the relevant components and baselines, isolating the asymmetric nature of the step density function and the transmission spots from the imperfect layer-by-layer (LBL) growth. These studies show the promise of big data approaches to obtaining more insight into film properties during and after epitaxial film growth. Furthermore, these studies open the pathway to use forward prediction methods to potentially allow significantly more control over growth process and hence final film quality.

Development of magnetoelectric, electromechanical, and photovoltaic devices based on mixed-phase rhombohedral-tetragonal (R-T) BiFeO(3) (BFO) systems is possible only if the control of the engineered R phase variants is realized. Accordingly, we explore the mechanism of a bias induced phase transformation in this system. Single point spectroscopy demonstrates that the T → R transition is activated at lower voltages compared to T → -T polarization switching. With phase field modeling, the transition is shown to be electrically driven. We further demonstrate that symmetry of formed R-phase rosettes can be broken by a proximal probe motion, allowing controlled creation of R variants with defined orientation. This approach opens a pathway to designing next-generation magnetoelectronic and data storage devices in the nanoscale.

Using a combination of piezoresponse force microscopy (PFM) and phase-field modeling, we demonstrate ubiquitous formation of center-type and possible ferroelectric closure domain arrangements during polarization switching near the ferroelastic domain walls in (100) oriented rhombohedral BiFeO(3). The formation of these topological defects is determined from the vertical and lateral PFM data and confirmed from the reversible changes in surface topography. These observations provide insight into the mechanisms of tip-induced ferroelastic domain control and suggest that formation of topological defect states under the action of local defect- and tip-induced fields is much more common than previously believed.