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Major advancements in fields as diverse as biology and quantum computing have relied on a multitude of microscopy techniques. Despite the considerable proliferation of these instruments, significant bottlenecks remain in terms of processing, analysis, storage, and retrieval of the acquired datasets. Aside from lack of file standards, individual domain‐specific analysis packages are often disjoint from the underlying datasets, and thus keeping track of analysis and processing steps remains tedious for the end‐user, hampering reproducibility. Here, the pycroscopy ecosystem of packages is introduced, an open‐source python‐based ecosystem underpinned by a common data model. The data model, termed the N‐dimensional spectral imaging data format, is realized in pycroscopy's sidpy package. This package is built on top of dask arrays, thus leveraging dask array attributes, but expanding them to accelerate microscopy relevant analysis and visualization. Several examples of the use of the pycroscopy ecosystem to create workflows for data ingestion and analysis of scanning transmission electron microscopy (STEM) and scanning probe microscopy data are shown. Adoption of such standardized routines will be critical to usher in the next generation of autonomous instruments where processing, computation, and meta‐data storage will be critical to overall experimental operations. [ABSTRACT FROM AUTHOR]

Along the same lines, Velli I et al. i [25] present another example that highlights integration of experimental and simulation data to improve predictive performance of a ML model aimed at mapping the processing parameters in laser-based manufacturing onto the observed material structure. B. Materials modeling and simulations In addition to enabling cheaper surrogate models for materials properties, ML algorithms have been quite successful in expediting, enhancing, and completing traditional domain-specific modeling and simulation capabilities. On the other hand, advances in ML-based image recognition methods have opened up new avenues for not only efficient on-the-fly analysis of materials characterization data to help address workflow bottlenecks in materials development but also to extract knowledge from materials characterization data with physics-informed models. After discussing the current status of the field and a brief background on various commonly used ML techniques, this editorial presented a brief overview of the papers included in our Special Topic on Machine Learning for Materials Design and Discovery. [Extracted from the article]

The universal tendency in scanning probe microscopy (SPM) over the last two decades is to transition from simple 2D imaging to complex detection and spectroscopic imaging modes. The emergence of complex SPM engines brings forth the challenge of reliable data interpretation, i.e., conversion from detected signals to descriptors specific to tip–surface interactions and subsequently to material's properties. Here, we implemented a Bayesian inference approach for the analysis of the image formation mechanisms in band excitation SPM. Compared to the point estimates in classical functional fit approaches, Bayesian inference allows for the incorporation of extant knowledge of materials and probe behavior in the form of corresponding prior distribution and return the information on the material functionality in the form of readily interpretable posterior distributions. We explore the nonlinear mechanical behaviors spatially in a classical ferroelectric material, PbTiO3. We observe the non-trivial evolution of the Duffing stiffness term and the nonlinearity of the sample surface, determine spatial clustering of the nonlinear response, and perform a Landau analysis on predicting the nonlinear coefficient, which indicates that ferroelectric behavior can be a cause of the observed results. These observations suggest that the spectrum of anomalous behaviors at the ferroelectric domain walls may be broader than previously believed and can extend to non-conventional mechanical properties in addition to static and microwave conductance. [ABSTRACT FROM AUTHOR]

Atom-by-atom assembly of functional materials and devices is perceived as one of the ultimate targets of nanotechnology. Recently it has been shown that the beam of a scanning transmission electron microscope can be used for targeted manipulation of individual atoms. However, the process is highly dynamic in nature rendering control difficult. One possible solution is to instead train artificial agents to perform the atomic manipulation in an automated manner without need for human intervention. As a first step to realizing this goal, we explore how artificial agents can be trained for atomic manipulation in a simplified molecular dynamics environment of graphene with Si dopants, using reinforcement learning. We find that it is possible to engineer the reward function of the agent in such a way as to encourage formation of local clusters of dopants under different constraints. This study shows the potential for reinforcement learning in nanoscale fabrication, and crucially, that the dynamics learned by agents encode specific elements of important physics that can be learned. [ABSTRACT FROM AUTHOR]

Compositional and charge disorder in ferroelectric relaxors lies at the heart of the unusual properties of these systems, such as aging and non-ergodicity, polarization rotations, and a host of temperature and field-driven phase transitions. However, much information about the fielddynamics of the polarization in the prototypical ferroelectric relaxor (1-x)Pb(Mg1/3Nb2/3)O3- xPbTiO3 (PMN-xPT) remains unprobed at the mesoscopic level. Here, we use a piezoresponse force microscopy-based dynamic multimodal relaxation spectroscopy technique, enabling the study of ferroelectric switching and polarization relaxation at mesoscopic length scales, and carry out measurements on a PMN-0.28PT sample with minimal polishing. Results indicate that beyond a threshold DC bias the average relaxation increases as the system attempts to relax to the previous state. Phenomenological fitting reveals the presence of mesoscale heterogeneity in relaxation amplitudes and clearly suggests the presence of two distinct amplitudes. Independent component analysis reveals the presence of a disorder component of the relaxation, which is found to be strongly anti-correlated with the maximum piezoresponse at that location, suggesting smaller disorder effects where the polarization reversal is large and vice versa. The disorder in the relaxation amplitudes is postulated to arise from rhombohedral and field-induced tetragonal phase in the crystal, with each phase associated with its own relaxation amplitude. These studies highlight the crucial importance of the mixture of ferroelectric phases in the compositions in proximity of the morphotropic phase boundary in governing the local response and further highlight the ability of PFM voltage and time spectroscopies, in conjunction with big-data multivariate analyses, to locally map disorder and correlate it with parameters governing the dynamic behavior. [ABSTRACT FROM AUTHOR]

The mechanisms of phase transitions have been previously explored at various theoretical and experimental levels. For a wide variety of compounds, the majority of studies are limited by observations at fixed temperature and composition, in which case, relevant information can be determined only from the behaviors at topological and structural defects. All analyses to date utilize macroscopic descriptors derived from structural information such as polarization or octahedral tilts extracted from the atomic positions, ignoring the multiple degrees of freedom observable from atomically resolved images. In this article, we provide a solution, by exploring the mechanisms of a phase transition between the trigonal prismatic and distorted octahedral phases of layered chalcogenides in the 2D MoS2–ReS2 system from the observations of local degrees of freedom, namely atomic positions by scanning transmission electron microscopy. We employ local crystallographic analysis based on statistical learning of atomic environments to build a picture of the transition from the atomic level up and determine local and global variables controlling the local symmetry breaking. We highlight how the dependence of the average symmetry-breaking distortion amplitude on global and local concentration can be used to separate local chemical as well as global electronic effects on the transition. This approach allows for the exploring of atomic mechanisms beyond the traditional macroscopic descriptions, utilizing the imaging of compositional fluctuations in solids to explore phase transitions over a range of observed local stoichiometries and atomic configurations. [ABSTRACT FROM AUTHOR]

Developing methods to understand and control defect formation in nanomaterials offers a promising route for materials discovery. Monolayer MX2 phases represent a particularly compelling case for defect engineering of nanomaterials due to the large variability in their physical properties as different defects are introduced into their structure. However, effective identification and quantification of defects remain a challenge even as high-throughput scanning transmission electron microscopy methods improve. This study highlights the benefits of employing first principles calculations to produce digital twins for training deep learning segmentation models for defect identification in monolayer MX2 phases. Around 600 defect structures were obtained using density functional theory calculations, with each monolayer MX2 structure being subjected to multislice simulations for the purpose of generating the digital twins. Several deep learning segmentation architectures were trained on this dataset, and their performances evaluated under a variety of conditions such as recognizing defects in the presence of unidentified impurities, beam damage, grain boundaries, and with reduced image quality from low electron doses. This digital twin approach allows benchmarking different deep learning architectures on a theory dataset, which enables the study of defect classification under a broad array of finely controlled conditions. It thus opens the door to resolving the underpinning physical reasons for model shortcomings and potentially chart paths forward for automated discovery of materials defect phases in experiments. [ABSTRACT FROM AUTHOR]

Ferroelectric materials have remained one of the major focal points of condensed matter physics and materials science for over 50 years. In the last 20 years, the development of voltage-modulated scanning probe microscopy techniques, exemplified by Piezoresponse force microscopy (PFM) and associated time- and voltage spectroscopies, opened a pathway to explore these materials on a singledigit nanometer level. Consequently, domain structures and walls and polarization dynamics can now be imaged in real space. More generally, PFM has allowed studying electromechanical coupling in a broad variety of materials ranging from ionics to biological systems. It can also be anticipated that the recent Nobel prize ["The Nobel Prize in Chemistry 2016," http://www.nobelprize.org/ nobel_prizes/chemistry/laureates/2016/ (Nobel Media, 2016)] in molecular electromechanical machines will result in rapid growth in interest in PFM as a method to probe their behavior on single device and device assembly levels. However, the broad introduction of PFM also resulted in a growing number of reports on the nearly ubiquitous presence of ferroelectric-like phenomena including remnant polar states and electromechanical hysteresis loops in materials which are non-ferroelectric in the bulk or in cases where size effects are expected to suppress ferroelectricity. While in certain cases plausible physical mechanisms can be suggested, there is remarkable similarity in observed behaviors, irrespective of the materials system. In this review, we summarize the basic principles of PFM, briefly discuss the features of ferroelectric surfaces salient to PFM imaging and spectroscopy, and summarize existing reports on ferroelectric-like responses in non-classical ferroelectric materials. We further discuss possible mechanisms behind observed behaviors and possible experimental strategies for their identification. [ABSTRACT FROM AUTHOR]

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]

Materials near structural phase transitions find applications in a wide range of devices. Typically, phase transitions are determined macroscopically through measurements of relevant order parameters and related property coefficients. Here, a method for understanding electric field induced phase transitions in ferroelectrically active materials at the nanometer scale via acoustic detection with band-excitation piezoresponse force microscopy (BE-PFM) is introduced. Specifically, the field-induced rhombohedral (R) to tetragonal (T) phase transition in single crystal 0.72PbMg1/3Nb2/3O3-0.28PbTiO3 (PMN-PT) is mapped. It is shown that due to sample heterogeneity, some regions are more prone to the R-T transition, and display signatures in the acquired piezoresponse loops, as well as pronounced softening in the elastic modulus (monitored via the resonant frequency and calibrated with models of cantilever dynamics) that occurs just prior to phase switching. Landau-Devonshire thermodynamic theory confirms the stability of the tetragonal phase under applied fields in PMN-PT, while phase-field modeling suggests that the transition evolves smoothly in the probed volume of the tip, both in agreement with the BE-PFM results. These results confirm the validity and utility of utilizing acoustic changes at phase transitions to detect their onset in nanoscale probed volumes, allowing spatial mapping of their onset with unprecedented resolution. [ABSTRACT FROM AUTHOR]

Relaxor-ferroelectrics are renowned for very large electrostrictive response, enabling applications in transducers, actuators, and energy harvesters. However, insight into the dissimilar contributions (polarization rotation, wall motion) to the electromechanical response from electrostrictive strain, and separation of such contributions from linear piezoelectric response are largely ignored at the mesoscale. Here, we employ a band-excitation piezoresponse force microscopy (BE-PFM) technique to explore the first and second harmonics of the piezoelectric response in prototypical relaxor-ferroelectric 0.72Pb(Mg1/3Nb2/3)O3-0.28PbTiO3 (PMN-0.28PT) single crystals. Third order polynomial fitting of the second harmonic reveals considerable correlation between the cubic coefficient map and the first harmonic piezoresponse amplitude. These results are interpreted under a modified Rayleigh framework, as evidence for domain wall contributions to enhanced electromechanical response. These studies highlight the contribution of domain wall motion in the electromechanical response of relaxor ferroelectrics, and further show the utility of harmonic BE-PFM measurements in spatially mapping the mesoscopic variability inherent in disordered systems. [ABSTRACT FROM AUTHOR]

Significant advances in atomically resolved imaging of crystals and surfaces have occurred in the last decade allowing unprecedented insight into local crystal structures and periodicity. Yet, the analysis of the long-range periodicity from the local imaging data, critical to correlation of functional properties and chemistry to the local crystallography, remains a challenge. Here, we introduce a Sliding Fast Fourier Transform (FFT) filter to analyze atomically resolved images of in-situ grown La5/8Ca3/8MnO3 (LCMO) films. We demonstrate the ability of sliding FFT algorithm to differentiate two sub-lattices, resulting from a mixed-terminated surface. Principal Component Analysis and Independent Component Analysis of the Sliding FFT dataset reveal the distinct changes in crystallography, step edges, and boundaries between the multiple sub-lattices. The implications for the LCMO system are discussed. The method is universal for images with any periodicity, and is especially amenable to atomically resolved probe and electron-microscopy data for rapid identification of the sub-lattices present. [ABSTRACT FROM AUTHOR]

The doped manganese oxides (manganites) have been widely studied for their colossal magnetoresistive effects, for potential applications in oxide spintronics, electroforming in resistive switching devices, and are materials of choice as cathodes in modern solid oxide fuel cells. However, little experimental knowledge of the dynamics of the surfaces of perovskite manganites at the atomic scale exists. Here, through in-situ scanning tunneling microscopy (STM), we demonstrate atomic resolution on samples of La0.625Ca0.375MnO3 grown on (001) SrTiO3 by pulsed laser deposition. Furthermore, by applying triangular DC waveforms of increasing amplitude to the STM tip, and measuring the tunneling current, we demonstrate the ability to both perform and monitor surface electrochemical processes at the atomic level, including formation of oxygen vacancies and removal and deposition of individual atomic units or clusters. Our work paves the way for better understanding of surface oxygen reactions in these systems. [ABSTRACT FROM AUTHOR]

Significant advances in atomically resolved imaging of crystals and surfaces have occurred in the last decade allowing unprecedented insight into local crystal structures and periodicity. Yet, the analysis of the long-range periodicity from the local imaging data, critical to correlation of functional properties and chemistry to the local crystallography, remains a challenge. Here, we introduce a Sliding Fast Fourier Transform (FFT) filter to analyze atomically resolved images of in-situ grown La5/8Ca3/8MnO3 (LCMO) films. We demonstrate the ability of sliding FFT algorithm to differentiate two sub-lattices, resulting from a mixed-terminated surface. Principal Component Analysis and Independent Component Analysis of the Sliding FFT dataset reveal the distinct changes in crystallography, step edges, and boundaries between the multiple sub-lattices. The implications for the LCMO system are discussed. The method is universal for images with any periodicity, and is especially amenable to atomically resolved probe and electron-microscopy data for rapid identification of the sub-lattices present. [ABSTRACT FROM AUTHOR]

Statistical physics models ranging from simple lattice to complex quantum Hamiltonians are one of the mainstays of modern physics that have allowed both decades of scientific discovery and provided a universal framework to understand a broad range of phenomena from alloying to frustrated and phase separated materials to quantum systems. Traditionally, exploration of the phase diagrams corresponding to multidimensional parameter spaces of Hamiltonians was performed using a combination of basic physical principles, analytical approximations, and extensive numerical modeling. However, exploration of complex multidimensional parameter spaces is subject to the classic dimensionality problem, and the behaviors of interest concentrated on low dimensional manifolds remain undiscovered. Here, we demonstrate that a combination of exploration and exploration–exploitation with Gaussian process modeling and Bayesian optimization allows effective exploration of the parameter space for lattice Hamiltonians and effectively maps the regions at which specific macroscopic functionalities or local structures are maximized. We argue that this approach is general and can be further extended well beyond the lattice Hamiltonians to effectively explore the parameter space of more complex off-lattice and dynamic models. [ABSTRACT FROM AUTHOR]

Advances in theoretical modeling across multiple disciplines have yielded generative models capable of high veracity in predicting macroscopic functional responses of materials emerging as a result of complex non-local interactions. Correspondingly, of interest is the inverse problem of finding the model parameter that will yield desired macroscopic responses, such as stress–strain curves, ferroelectric hysteresis loops, etc. Here, we suggest and implement Gaussian process based methods that allow to effectively sample the degenerate parameter space of a complex non-local model to output regions of parameter space which yield desired functionalities. We discuss the specific adaptation of the acquisition function and sampling function to make the process efficient and balance the efficient exploration of parameter space for multiple possible minima and exploitation to densely sample the regions of interest where target behaviors are optimized. This approach is illustrated via the hysteresis loop engineering in ferroelectric materials but can be adapted to other functionalities and generative models. [ABSTRACT FROM AUTHOR]

Magnetoelectric (ME) composite thin films are promising candidates for novel applications in future nanoelectronics, spintronics, memory, and other multifunctional devices as they exhibit much higher ME coupling and transition temperatures (Tc) than well-known single phase multiferroics discovered to date. Among the three types of multiferroic composite nanostructures, (2–2) layered and (1–3) vertically aligned composite nanostructures exhibit comparatively smaller ME coupling due to different shortcomings that restrict their use in many applications. Here, we study the morphological, piezoresponse force microscopic (PFM), ferroelectric, magnetic, and magnetodielectric properties of 0–3 [magnetic nanoparticles (0) homogeneously distributed in ferroelectric matrices (3)] multiferroic composite thin films. The Pb(Fe0.5Nb0.5)O3 (PFN)–Ni0.65Zn0.35Fe2O4 (NZFO) particulate composite films were synthesized by pulsed laser deposition. These particulate composite thin films are completely c-axis oriented with very low surface roughness. We observed magnetic and ferroelectric Tc above room temperature (RT) for all composite thin films. The PFN–NZFO 0–3 composites exhibit large polarization, high saturated magnetization with low coercive field, and low dielectric loss along with magnetodielectric coupling at RT. These nanocomposites might be utilized in next generation nano/microelectronics and spintronic devices. [ABSTRACT FROM AUTHOR]

Nanometer-scale electronic transport in engineered interfaces in ferroelectrics, such as domains and topological defects, has emerged as a topic of broad interest due to potential applications in information storage, sensors and photovoltaic devices. Scanning probe microscopy (SPM) methods led to rapid growth in the field by enabling correlation of the unique functional properties with microstructural features in the aforementioned highly localized phenomena. In addition to conduction localized at interfaces, polarization-mediated control of conduction through domains in nanoscale ferroelectrics suggests significant potential for use in memristor technologies. In parallel with experiment, theory based on thermodynamic Landau-Ginzburg-Devonshire (LGD) framework has seen rapid development, both rationalizing the observations, and hinting at possibilities for local, deterministic control of order parameters. These theories can successfully account for static interface conductivity at charged, nominally uncharged and topologically protected domain walls. Here, recent experimental and theoretical progress in SPM-motivated studies on domain wall conduction in both standard and improper ferroelectrics are reviewed. SPM studies on transport through ferroelectrics reveal that both domains and topological defects in oxides can be exploited as individual elements for use in functional nanoscale devices. Future prospects of the field are discussed. [ABSTRACT FROM AUTHOR]

Functional properties of ferroelectric materials depend both on the residual domain states and on the mobility of domain walls in response to the applied electric and stress fields. This paper reviews the use of multidimensional scanning probe microscopy to assess these factors in the time- and voltage domains, with an emphasis on the manner in which domain walls respond collectively to stimuli. It is found that in many PbZr1-xTixO3-based capacitors, domain wall motion is correlated over length scales that exceed the domain and grain sizes by orders of magnitude, suggesting emergent collective electromechanical behavior. The role of mechanical boundary conditions and field history on the domain wall contributions and the stability of the ferroelectric domain state are discussed. [ABSTRACT FROM AUTHOR]

The nonlinear response of a ferroic to an applied field has been studied through the phenomenological Rayleigh Law for over a hundred years. Yet, despite this, the fundamental physical mechanisms at the nanoscale that lead to macroscopic Rayleigh behavior have remained largely elusive, and experimental evidence at small length scales is limited. Here, it is shown using a combination of scanning probe techniques and phase field modeling, that nanoscale piezoelectric response in prototypical Pb(Zr,Ti)O3 films appears to follow a distinctly non-Rayleigh regime. Through statistical analysis, it is found that an averaging of local responses can lead directly to Rayleigh-like behavior of the strain on a macroscale. Phase-field modeling confirms the twist of the ferroelastic interface is key in enhancing piezoelectric response. The studies shed light on the nanoscale origins of nonlinear behavior in disordered ferroics. [ABSTRACT FROM AUTHOR]

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 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. A strain‐stabilized, room‐temperature chiral multiferroic phase in single crystals of BaTiS3 is reported here. Using first‐principles calculations, the stabilization of this multiferroic phase having P63 space group for biaxial tensile strains exceeding 1.5% applied on the basal ab‐plane of the room temperature P63cm phase of BaTiS3 is predicted. The chiral multiferroic phase is characterized by rotational distortions of TiS6 octahedra around the long c‐axis and polar displacement of Ti atoms along the c‐axis. The ferroaxial and ferroelectric distortions and their domains in P63‐BaTiS3 are 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 P63‐BaTiS3 an attractive test bed for achieving electric‐field‐control of chirality. [ABSTRACT FROM AUTHOR]

Microscopy has been pivotal in improving the understanding of structure‐function relationships at the nanoscale and is by now ubiquitous in most characterization labs. However, traditional microscopy operations are still limited largely by a human‐centric click‐and‐go paradigm utilizing vendor‐provided software, which limits the scope, utility, efficiency, effectiveness, and at times reproducibility of microscopy experiments. Here, a coupled software–hardware platform is developed that consists of a software package termed AEcroscopy (short for Automated Experiments in Microscopy), along with a field‐programmable‐gate‐array device with LabView‐built customized acquisition scripts, which overcome these limitations and provide the necessary abstractions toward full automation of microscopy platforms. The platform works across multiple vendor devices on scanning probe microscopes and electron microscopes. It enables customized scan trajectories, processing functions that can be triggered locally or remotely on processing servers, user‐defined excitation waveforms, standardization of data models, and completely seamless operation through simple Python commands to enable a plethora of microscopy experiments to be performed in a reproducible, automated manner. This platform can be readily coupled with existing machine‐learning libraries and simulations, to provide automated decision‐making and active theory‐experiment optimization to turn microscopes from characterization tools to instruments capable of autonomous model refinement and physics discovery. [ABSTRACT FROM AUTHOR]

In situ scanning transmission electron microscopy enables observation of the domain dynamics in ferroelectric materials as a function of externally applied bias and temperature. The resultant data sets contain a wealth of information on polarization switching and phase transition mechanisms. However, identification of these mechanisms from observational data sets has remained a problem due to a large variety of possible configurations, many of which are degenerate. Here, an approach based on a combination of deep learning‐based semantic segmentation, rotationally invariant variational autoencoder (VAE), and non‐negative matrix factorization to enable learning of a latent space representation of the data with multiple real‐space rotationally equivalent variants mapped to the same latent space descriptors is introduced. By varying the size of training sub‐images in the VAE, the degree of complexity in the structural descriptors is tuned from simple domain wall detection to the identification of switching pathways. This yields a powerful tool for the exploration of the dynamic data in mesoscopic electron, scanning probe, optical, and chemical imaging. Moreover, this work adds to the growing body of knowledge of incorporating physical constraints into the machine and deep‐learning methods to improve learned descriptors of physical phenomena. [ABSTRACT FROM AUTHOR]

Autonomous systems that combine synthesis, characterization, and artificial intelligence can greatly accelerate the discovery and optimization of materials, however platforms for growth of macroscale thin films by physical vapor deposition techniques have lagged far behind others. Here this study demonstrates autonomous synthesis by pulsed laser deposition (PLD), a highly versatile synthesis technique, in the growth of ultrathin WSe2 films. By combing the automation of PLD synthesis and in situ diagnostic feedback with a high‐throughput methodology, this study demonstrates a workflow and platform which uses Gaussian process regression and Bayesian optimization to autonomously identify growth regimes for WSe2 films based on Raman spectral criteria by efficiently sampling 0.25% of the chosen 4D parameter space. With throughputs at least 10x faster than traditional PLD workflows, this platform and workflow enables the accelerated discovery and autonomous optimization of the vast number of materials that can be synthesized by PLD. [ABSTRACT FROM AUTHOR]

Epitaxial crystallization of complex oxides provides the means to create materials with precisely selected composition, strain, and orientation, thereby controlling their functionalities. Extending this control to nanoscale three-dimensional geometries can be accomplished via a three-dimensional analog of oxide solid-phase epitaxy, lateral epitaxial crystallization. The orientation of crystals within laterally crystallized SrTiO3 systematically changes from the orientation of the SrTiO3 substrate. This evolution occurs as a function of lateral crystallization distance, with a rate of approximately 50° µm-1. The mechanism of the rotation is consistent with a steady-state stress of tens of megapascal over a 100-nanometer scale region near the moving amorphous/crystalline interface arising from the amorphous-crystalline density difference. Second harmonic generation and piezoelectric force microscopy reveal that the laterally crystallized SrTiO3 is noncentrosymmetric and develops a switchable piezoelectric response at room temperature, illustrating the potential to use lateral crystallization to control the functionality of complex oxides. [ABSTRACT FROM AUTHOR]

Experimental science is enabled by the combination of synthesis, imaging, and functional characterization organized into evolving discovery loop. Synthesis of new material is typically followed by a set of characterization steps aiming to provide feedback for optimization or discover fundamental mechanisms. However, the sequence of synthesis and characterization methods and their interpretation, or research workflow, has traditionally been driven by human intuition and is highly domain specific. Here, we explore concepts of scientific workflows that emerge at the interface between theory, characterization, and imaging. We discuss the criteria by which these workflows can be constructed for special cases of multiresolution structural imaging and functional characterization, as a part of more general material synthesis workflows. Some considerations for theory–experiment workflows are provided. We further pose that the emergence of user facilities and cloud labs disrupts the classical progression from ideation, orchestration, and execution stages of workflow development. To accelerate this transition, we propose the framework for workflow design, including universal hyperlanguages describing laboratory operation, ontological domain matching, reward functions and their integration between domains, and policy development for workflow optimization. These tools will enable knowledge-based workflow optimization; enable lateral instrumental networks, sequential and parallel orchestration of characterization between dissimilar facilities; and empower distributed research. [ABSTRACT FROM AUTHOR]

Machine learning (ML) methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-acquisition. The introduction of the application programming interfaces (APIs) by major instrument manufacturers now allows the deployment of ML workflows in microscopes, not only for data analytics but also for real-time decision-making and feedback for microscope operation. However, the number of use cases for real-time ML remains remarkably small. Here, we discuss some considerations in designing ML-based active experiments and pose that the likely strategy for the next several years will be human-in-the-loop automated experiments (hAE). In this paradigm, the ML learning agent directly controls beam position and image and spectroscopy acquisition functions, and a human operator monitors experiment progression in real and feature space of the system and tunes the policies of the ML agent to steer the experiment toward specific objectives. [ABSTRACT FROM AUTHOR]

We study the local conductivity properties of KTiOPO4 using conductive atomic force microscopy in ultrahigh vacuum (UHV). We show that domains with opposite orientations have different conductivity values. We investigate the temperature dependence of the local conductivity and report a difference in the activation energy of 25% between different domains. Furthermore, we show that the local conductivity increases with the number of biased-scans. Finally, it is found that the domain wall conductivity previously observed at ambient conditions vanishes in UHV. Our results are discussed in terms of the screening effects and surface conditions. [ABSTRACT FROM AUTHOR]

On page 478, N. Bassiri‐Gharb and co‐workers demonstrate acoustic detection in nanoscale volumes by use of an atomic force microscope tip technique. Elastic changes in volume are measured by detecting changes in resonance of the cantilever. The electric field in this case causes a phase transition, which is modeled by Landau theory. [ABSTRACT FROM AUTHOR]

Nanoscale hyperspectral techniques—such as electron energy loss spectroscopy (EELS)—are critical to understand the optical response in plasmonic nanostructures, but as systems become increasingly complex, the required sampling density and acquisition times become prohibitive for instrumental and specimen stability. As a result, there has been a recent push for new experimental methodologies that can provide comprehensive information about a complex system, while significantly reducing the duration of the experiment. Here, we present a pan-sharpening approach to hyperspectral EELS analysis, where we acquire two datasets from the same region (one with high spatial resolution and one with high spectral fidelity) and combine them to achieve a single dataset with the beneficial properties of both. This work outlines a straightforward, reproducible pathway to reduced experiment times and higher signal-to-noise ratios, while retaining the relevant physical parameters of the plasmonic response, and is generally applicable to a wide range of spectroscopy modalities. [ABSTRACT FROM AUTHOR]

The emergence of scanning probe and electron beam imaging techniques has allowed quantitative studies of atomic structure and minute details of electronic and vibrational structure on the level of individual atomic units. These microscopic descriptors, in turn, can be associated with local symmetry breaking phenomena, representing the stochastic manifestation of the underpinning generative physical model. Here, we explore the reconstruction of exchange integrals in the Hamiltonian for a lattice model with two competing interactions from observations of microscopic degrees of freedom and establish the uncertainties and reliability of such analysis in a broad parameter-temperature space. In contrast to other approaches, we specifically specify a loss function inherent to thermodynamic systems and utilize it to estimate uncertainty in simulated realizations of different models. As an ancillary task, we develop a machine learning approach based on histogram clustering to predict phase diagrams efficiently using a reduced descriptor space. We further demonstrate that reconstruction is possible well above the phase transition and in the regions of parameter space when the macroscopic ground state of the system is poorly defined due to frustrated interactions. This suggests that this approach can be applied to the traditionally complex problems of condensed matter physics such as ferroelectric relaxors and morphotropic phase boundary systems, spin and cluster glasses, and quantum systems once the local descriptors linked to the relevant physical behaviors are known. [ABSTRACT FROM AUTHOR]

Imaging mechanisms in contact Kelvin probe force microscopy (cKPFM) are explored via information theory-based methods. Gaussian processes are used to achieve super-resolution in the cKPFM signal, effectively extrapolating across the spatial and parameter space. Tensor factorization is applied to reduce the multidimensional signal to the tensor convolution of the scalar functions that show a clear trending behavior with the imaging parameters. These methods establish a workflow for the analysis of the multidimensional datasets that can then be related to the relevant physical mechanisms. We also provide an interactive Google Colab notebook that goes through all the analyses discussed in the paper. [ABSTRACT FROM AUTHOR]

Experimental observations suggest that nominally uncharged, as-grown domain walls in ferroelectrics can be conductive, yet comprehensive theoretical models to explain this behavior are lacking. Here, Landau-Ginzburg-Devonshire theory is used to evolve an analytical treatment of the anisotropic carrier accumulation by nominally uncharged domain walls in multiferroic BiFeO3. Strong angular dependence of the carrier accumulation by 180° domain walls originates from local band bending via angle-dependent electrostriction and flexoelectric coupling mechanisms. Theoretical results are in qualitative agreement with experimental data and provide a counterpart that is consistent with recent first-principles calculations. These studies suggest that a significantly more diverse range of domain wall structures could possess novel electronic properties than previously believed. Similarly, emergent electronic behaviors at ferroic walls are typically underpinned by multiple mechanisms, necessitating first-principles studies of corresponding coupling parameters. [ABSTRACT FROM AUTHOR]

4D-STEM datasets obtained from EMPAD detectors broke the TEM resolution limits of 0.4Å in 2018 [[37]] and 0.25Å in 2021 [[38]] by using electron ptychography. Optical microscopes inspired the first TEM, and since then several new imaging modalities have been implemented, such as electron holography [[6]], Lorentz electron microscopy [[8]], and scanning TEM (STEM) [[9]]. Multidimensional Electron Microscopy Enabled by Detector Advances Electronic detectors have been used for TEM image acquisition since the early nineties. Keywords: electron microscope; edge computer; high-performance computing; 4D STEM; electron detectors EN electron microscope edge computer high-performance computing 4D STEM electron detectors 10 19 10 11/29/22 20221101 NES 221101 Introduction Ernst Ruska built the first transmission electron microscope (TEM) during his doctoral studies, and it celebrates its eightieth anniversary this year [[1]-[3]]. [Extracted from the article]

Ferroelectrics are being increasingly called upon for electronic devices in extreme environments. Device performance and energy efficiency is highly correlated to clock frequency, operational voltage, and resistive loss. To increase performance it is common to engineer ferroelectric domain structure with highly‐correlated electrical and elastic coupling that elicit fast and efficient collective switching. Designing domain structures with advantageous properties is difficult because the mechanisms involved in collective switching are poorly understood and difficult to investigate. Collective switching is a hierarchical process where the nano‐ and mesoscale responses control the macroscopic properties. Using chemical solution synthesis, epitaxially nearly‐relaxed (100) BaTiO3 films are synthesized. Thermal strain induces a strongly‐correlated domain structure with alternating domains of polarization along the [010] and [001] in‐plane axes and 90° domain walls along the [011] or [011¯$\bar{1}$] directions. Simultaneous capacitance–voltage measurements and band‐excitation piezoresponse force microscopy revealed strong collective switching behavior. Using a deep convolutional autoencoder, hierarchical switching is automatically tracked and the switching pathway is identified. The collective switching velocities are calculated to be ≈500 cm s−1 at 5 V (7 kV cm−1), orders‐of‐magnitude faster than expected. These combinations of properties are promising for high‐speed tunable dielectrics and low‐voltage ferroelectric memories and logic. [ABSTRACT FROM AUTHOR]

With the rapid development of new device miniaturization technology, there is invigorated interest in magnetic nanostructures for potential application in novel multifunctional devices. In continuation to our search for a suitable magnetic material having Curie temperature (Tc) well above room temperature for multifunctional applications, we have studied the dielectric, optical, magnetic, and resistance switching characteristics of Ni0.65Zn0.35Fe2O4 (NZFO) thin films. The observation of only (004) reflection in the X-ray diffraction patterns confirms the c-axis orientation and high quality growth of NZFO thin films. The presence of mixed valences of Fe2+/Fe3+ cations is probed by X-ray photon spectroscopy, which supports the cationic ordering-mediated large dielectric response. Our investigations reveal NZFO to be an indirect band gap material (~1.8eV) with a direct gap at ~2.55eV. These nanostructures exhibit high saturation magnetization and a low coercive field with a ferrimagnetic-paramagnetic phase transition of ~713K. Magnetic force microscopy studies revealed the stripe-like domain structure of the investigated thin films. In addition, these thin films exhibit reliable and repeatable unipolar resistive switching characteristics. The observed high dielectric permittivity with low loss tangent, large magnetization with soft magnetic behavior, striped magnetic domain structure and reliable resistance switching in NZFO thin films above room temperature suggest potential application in memory, spintronics, and multifunctional devices. [ABSTRACT FROM AUTHOR]

The article in Small Methods, published in September 2024, discusses the autonomous synthesis of thin film materials through pulsed laser deposition. Harris, Xiao, and their team utilized in situ diagnostics, automation, and machine learning to efficiently explore a four-dimensional parameter space, leading to the discovery of two distinct growth windows for WSe2. This approach enables the accelerated discovery and automated optimization of novel materials, presenting a significant advancement in material science research. [Extracted from the article]

Topological defects in ferroelectric materials have attracted much attention due to the emergence of conductive, ferroic, and magnetic functionalities. However, many topological configurations dynamically evolve during the switching processes, making them a challenge to characterize via traditional techniques. Here, we implement an automated experimentation approach for the exploration of functional properties in BiFeO3 thin films. Specifically, we visualize the ferroelectric domain structures via single frequency piezoresponse force microscopy and implement a computer vision-based algorithm to discover features of interest at which spectroscopic measurements are taken. Subsequently, we employ dimensionality reduction techniques to reveal characteristic polarization behaviors at these features. This approach can be extended to other spectroscopies and modalities to probe only specific features of interest, ultimately enabling dynamical processes in ferroelectrics to be studied. [ABSTRACT FROM AUTHOR]

Hybrid organic–inorganic perovskites are one of the promising candidates for the next‐generation semiconductors due to their superlative optoelectronic properties. However, one of the limiting factors for potential applications is their chemical and structural instability in different environments. Herein, the stability of (FAPbI3)0.85(MAPbBr3)0.15 perovskite solar cell is explored in different atmospheres using impedance spectroscopy. An equivalent circuit model and distribution of relaxation times (DRTs) are used to effectively analyze impedance spectra. DRT is further analyzed via machine learning workflow based on the non‐negative matrix factorization of reconstructed relaxation time spectra. This exploration provides the interplay of charge transport dynamics and recombination processes under environment stimuli and illumination. The results reveal that in the dark, oxygen atmosphere induces an increased hole concentration with less ionic character while ionic motion is dominant under ambient air. Under 1 Sun illumination, the environment‐dependent impedance responses show a more striking effect compared with dark conditions. In this case, the increased transport resistance observed under oxygen atmosphere in equivalent circuit analysis arises due to interruption of photogenerated hole carriers. The results not only shed light on elucidating transport mechanisms of perovskite solar cells in different environments but also offer an effective interpretation of impedance responses. [ABSTRACT FROM AUTHOR]

Electron energy loss spectroscopy (EELS) enables direct exploration of plasmonic phenomena at the nanometer level. To isolate individual plasmon modes, linear unmixing methods can be used to separate different physical mechanisms, but in larger and more complex systems the interpretability of the components becomes uncertain. Here, infrared plasmonic resonances in self‐assembled heterogeneous monolayer films of doped‐semiconductor nanoparticles are examined beyond linear unmixing techniques, and both supervised and unsupervised machine‐learning‐based analyses of hyperspectral EELS datasets are demonstrated. In the supervised approach, a human operator labels a small number of pixels in the hyperspectral dataset corresponding to features of interest which are then propagated across the entire dataset. In the unsupervised approach, non‐linear autoencoders are used to create a highly‐reduced latent‐space representation of the dataset, within which insight into the relevant physics can be gleaned from straightforward distance metrics that do not depend on operator input and bias. The advantage of these approaches is that the labeling separates physical mechanisms without altering the data, enabling robust analyses of the influence of heterogeneities in mesoscale complex systems. [ABSTRACT FROM AUTHOR]

Parkinson's disease medication treatment planning is generally based on subjective data obtained through clinical, physician-patient interactions. The Personal KinetiGraph™ (PKG) and similar wearable sensors have shown promise in enabling objective, continuous remote health monitoring for Parkinson's patients. In this proof-of-concept study, we propose to use objective sensor data from the PKG and apply machine learning to cluster patients based on levodopa regimens and response. The resulting clusters are then used to enhance treatment planning by providing improved initial treatment estimates to supplement a physician's initial assessment. We apply k-means clustering to a dataset of within-subject Parkinson's medication changes—clinically assessed by the MDS-Unified Parkinson's Disease Rating Scale-III (MDS-UPDRS-III) and the PKG sensor for movement staging. A random forest classification model was then used to predict patients' cluster allocation based on their respective demographic information, MDS-UPDRS-III scores, and PKG time-series data. Clinically relevant clusters were partitioned by levodopa dose, medication administration frequency, and total levodopa equivalent daily dose—with the PKG providing similar symptomatic assessments to physician MDS-UPDRS-III scores. A random forest classifier trained on demographic information, MDS-UPDRS-III scores, and PKG time-series data was able to accurately classify subjects of the two most demographically similar clusters with an accuracy of 86.9%, an F1 score of 90.7%, and an AUC of 0.871. A model that relied solely on demographic information and PKG time-series data provided the next best performance with an accuracy of 83.8%, an F1 score of 88.5%, and an AUC of 0.831, hence further enabling fully remote assessments. These computational methods demonstrate the feasibility of using sensor-based data to cluster patients based on their medication responses with further potential to assist with medication recommendations. [ABSTRACT FROM AUTHOR]

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 associated uncertainties are consistent across the composition series. The associated generative physical mechanisms are discussed. It is also found that certain parameter combinations tend to predict the orthorhombic phase in the cases where rhombohedral phase is observed, suggesting a potential role of clamping and confinement phenomena in phase equilibrium in Sm-BiFeO3 system close to morphotropic phase boundary. We argue that predictability and uncertainty in observational data offer a new pathway to probe the physics of condensed matter systems from multimodal local observations. [ABSTRACT FROM AUTHOR]

Ordering of mobile defects in functional materials can give rise to fundamentally new phases possessing ferroic and multiferroic functionalities. Here we develop the Landau theory for strain induced ordering of defects (e.g. oxygen vacancies) in thin oxide films, considering both the ordering and wavelength of possible instabilities. Using derived analytical expressions for the energies of various defect-ordered states, we calculated and analyzed phase diagrams dependence on the film-substrate mismatch strain, concentration of defects, and Vegard coefficients. Obtained results open possibilities to create and control superstructures of ordered defects in thin oxide films by selecting the appropriate substrate and defect concentration. [ABSTRACT FROM AUTHOR]

The instability of hybrid organic–inorganic perovskite (HOIP) devices is one of the significant challenges preventing commercialization. Exploring these phenomena is severely limited by the complexity of the intrinsic electrochemistry of HOIPs, the presence of multiple volatile and mobile ionic species, and the possible role of environmentally induced reactions at surfaces and triple‐phase junctions. Here, in situ studies of the electrochemistry of methylammonium lead bromide perovskite with the Au electrode interface are reported via light‐ and voltage‐dependent time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) imaging of lateral perovskite heterostructures. While ToF‐SIMS allows for the visualization of the chemical composition along the surface and its evolution with light and electrical bias, the interpretation of the multidimensional data obtained is often limited due to strong correlations between chemical signatures and the need to track multiple peaks at once. Here, a machine learning workflow combining the Hough transform and non‐negative matrix factorization and non‐negative tensor decomposition is developed to avoid this limitation and extract salient features of associated chemical changes and to separate the light‐ and voltage‐dependent dynamics. Combining these in situ characterizations and the machine learning workflow provides comprehensive information on the chemical nature of moving species, ion accumulation, and interfacial electrochemical reactions in HOIP devices. [ABSTRACT FROM AUTHOR]

Multiferroic materials have attracted considerable attention as possible candidates for a wide variety of future microelectronic and memory devices, although robust magnetoelectric (ME) coupling between electric and magnetic orders at room temperature still remains difficult to achieve. In order to obtain robust ME coupling at room temperature, we studied the Pb(Fe0.5Nb0.5)O3/Ni0.65Zn0.35Fe2O4/Pb(Fe0.5Nb0.5)O3 (PFN/NZFO/PFN) trilayer structure as a representative FE/FM/FE system. We report the ferroelectric, magnetic and ME properties of PFN/NZFO/PFN trilayer nanoscale heterostructure having dimensions 70/20/70 nm, at room temperature. The presence of only (00l) reflection of PFN and NZFO in the X-ray diffraction (XRD) patterns and electron diffraction patterns in Transmission Electron Microscopy (TEM) confirm the epitaxial growth of multilayer heterostructure. The distribution of the ferroelectric loop area in a wide area has been studied, suggesting that spatial variability of ferroelectric switching behavior is low, and film growth is of high quality. The ferroelectric and magnetic phase transitions of these heterostructures have been found at ~575 K and ~650 K, respectively which are well above room temperature. These nanostructures exhibit low loss tangent, large saturation polarization (Ps ~ 38 µC/cm2) and magnetization (Ms ~ 48 emu/cm3) with strong ME coupling at room temperature revealing them as potential candidates for nanoscale multifunctional and spintronics device applications. [ABSTRACT FROM AUTHOR]

Abstract Extrusion based high-throughput Additive Manufacturing (AM) provides a rapid and versatile approach for producing complex structures by using a variety of polymer materials. An underexplored aspect of this technique is concerned with the formation of interfaces between successively deposited layers. This is particularly important for large-scale additive manufacturing of semi-crystalline polymers because of the highly non-isothermal conditions involved, which influence both nucleation and crystal growth. The objective of this work is to investigate the microstructure and the corresponding viscoelastic properties of carbon fiber (CF) reinforced polyphenylene sulfide (PPS) resulting from extrusion-based high-throughput AM process. Questions on development of morphology focus on polymer crystal structure and carbon fiber orientation in the vicinity of the interface between successive layers. This study attempts to establish a fundamental understanding of the role of the AM has in transferring a set of intrinsic material properties to the macroscopic properties of the final AM structure. [ABSTRACT FROM AUTHOR]

The article discusses the role of machine learning in the identification of material phase transitions and determining the presence of phase boundaries using experimental data, focusing on the collective dynamics in ferroelectric relaxors. It shows a new approach to data mining of collective dynamics toward the identification of the onset of a structural phase transition in nanometer-scale volunes evaluated by atomic force microscope tip.