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

To describe the charge-polarization coupling in the nanostructure formed by a thin Hf$_x$Zr$_{1-x}$O$_2$ film with a single-layer graphene as a top electrode, we develop the phenomenological effective Landau-Ginzburg-Devonshire model. This approach is based on the parametrization of the Landau expansion coefficients for the polar and antipolar orderings in thin Hf$_x$Zr$_{1-x}$O$_2$ films from a limited number of polarization-field curves and hysteresis loops. The Landau expansion coefficients are nonlinearly dependent on the film thickness $h$ and Zr/[Hf+Zr] ratio $x$, in contrast to h-independent and linearly $x$-dependent expansion coefficients of a classical Landau energy. We explain the dependence of the Landau expansion coefficients by the strong nonmonotonic dependence of the Hf$_x$Zr$_{1-x}$O$_2$ film polar properties on the film thickness, grain size and surface energy. The proposed Landau free energy with five "effective" expansion coefficients, which are interpolation functions of $x$ and $h$, describes the continuous transformation of polarization dependences on applied electric field and hysteresis loop shapes induced by the changes of $x$ and $h$ in the range $0 < x < 1$ and 5 nm < $h$ < 35 nm. Using this effective free energy, we demonstrated that the polarization of Hf$_x$Zr$_{1-x}$O$_2$ film influences strongly on the graphene conductivity, and the full correlation between the distribution of polarization and charge carriers in graphene is revealed. In accordance with our modeling, the polarization of the (5 - 25) nm thick Hf$_x$Zr$_{1-x}$O$_2$ films, which are in the ferroelectric-like or antiferroelectric-like states for the chemical compositions $0.35 < x < 0.95$, determine the concentration of carriers in graphene and can control its field dependence. The result can be promising for creation of next generation Si-compatible nonvolatile memories and graphene-ferroelectric FETs., 30 pages including 8 figures and 1 Appendix with 2 figures

Discovery of the molecular candidates for applications in drug targets, biomolecular systems, catalysts, photovoltaics, organic electronics, and batteries, necessitates development of machine learning algorithms capable of rapid exploration of the chemical spaces targeting the desired functionalities. Here we introduce a novel approach for the active learning over the chemical spaces based on hypothesis learning. We construct the hypotheses on the possible relationships between structures and functionalities of interest based on a small subset of data and introduce them as (probabilistic) mean functions for the Gaussian process. This approach combines the elements from the symbolic regression methods such as SISSO and active learning into a single framework. The primary focus of constructing this framework is to approximate physical laws in an active learning regime toward a more robust predictive performance, as traditional evaluation on hold-out sets in machine learning doesn't account for out-of-distribution effects and may lead to a complete failure on unseen chemical space. Here, we demonstrate it for the QM9 dataset, but it can be applied more broadly to datasets from both domains of molecular and solid-state materials sciences.

Using Landau-Ginzburg-Devonshire (LGD) phenomenological approach we analyze the bending-induced re-distribution of electric polarization and field, elastic stresses and strains inside ultrathin layers of van der Waals ferrielectrics. We consider a CuInP2S6 (CIPS) thin layer with fixed edges and suspended central part, the bending of which is induced by external forces. The unique aspect of CIPS is the existence of two ferrielectric states, FI1 and FI2, corresponding to big and small polarization values, which arise due to the specific four-well potential of the eighth-order LGD functional. When the CIPS layer is flat, the single-domain FI1 state is stable in the central part of the layer, and the FI2 states are stable near the fixed edges. With an increase of the layer bending below the critical value, the sizes of the FI2 states near the fixed edges decreases, and the size of the FI1 region increases. When the bending exceeds the critical value, the edge FI2 states disappear being substituted by the FI1 state, but they appear abruptly near the inflection regions and expand as the bending increases. The bending-induced isostructural FI1-FI2 transition is specific for the bended van der Waals ferrielectrics described by the eighth (or higher) order LGD functional with consideration of linear and nonlinear electrostriction couplings. The isostructural transition, which is revealed in the vicinity of room temperature, can significantly reduce the coercive voltage of ferroelectric polarization reversal in CIPS nanoflakes, allowing for the curvature-engineering control of various flexible nanodevices., Comment: 26 pages, 7 figures and Appendices A-C

Strain-induced transitions of polarization reversal in thin films of a ferrielectric CuInP$_2$S$_6$ (CIPS) with ideally-conductive electrodes is explored using the Landau-Ginzburg-Devonshire (LGD) approach with an eighth-order free energy expansion in polarization powers. Due to multiple potential wells, the height and position of which are temperature- and strain-dependent, the energy profiles of CIPS can flatten in the vicinity of the non-zero polarization states. This behavior differentiates these materials from classical ferroelectrics with the first or second order ferroelectric-paraelectric phase transition, for which potential energy profiles can be shallow or flat near the transition point only, corresponding to zero spontaneous polarization. Thereby we reveal an unusually strong effect of the mismatch strain on the out-of-plane polarization reversal, hysteresis loops shape, dielectric susceptibility, and piezoelectric response of CIPS films. In particular, by varying the sign of the mismatch strain and its magnitude in a narrow range, quasi-static hysteresis-less paraelectric curves can transform into double, triple, and other types of pinched and single hysteresis loops. The strain effect on the polarization reversal is opposite, i.e., "anomalous", in comparison with many other ferroelectric films in that the out-of-plane remanent polarization and coercive field increases strongly for tensile strains, meanwhile the polarization decreases or vanish for compressive strains. We explain the effect by "inverted" signs of linear and nonlinear electrostriction coupling coefficients of CIPS and their strong temperature dependence. For definite values of temperature and mismatch strain, the low-frequency hysteresis loops of polarization may exhibit negative slope in the relatively narrow range of external field amplitude and frequency., Comment: 26 pages, including 8 figures and 1 Appendix

Nanoscale ferroelectric 2D materials offer unique opportunity to investigate curvature and strain effects on materials functionalities. Among these, CuInP2S6 (CIPS) has attracted tremendous research interest in recent years due to combination of room temperature ferroelectricity, scalability to a few layers thickness, and unique ferrielectric properties due to coexistence of 2 polar sublattices. Here, we explore the local curvature and strain effect on the polarization in CIPS via piezoresponse force microscopy and spectroscopy. To explain the observed behaviors and decouple the curvature and strain effects in 2D CIPS, we introduce finite element Landau-Ginzburg-Devonshire model. The results show that bending induces ferrielectric domains in CIPS, and the polarization-voltage hysteresis loops differ in bending and non-bending regions. Our simulation indicates that the flexoelectric effect can affect local polarization hysteresis. These studies open a novel pathway for the fabrication of curvature-engineered nanoelectronic devices., Comment: 20 pages; 7 figures

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

Local current-voltage characteristics for low-dimensional transition metal dichalcogenides (LD-TMD), as well as the reconstruction of their local density of states (LDOS) from scanning tunneling microscopy (STM) experiments is of fundamental interest and can be useful for advanced applications. Most of existing models are either hardly applicable for the LD-TMD of complex shape (e.g., those based on Simmons approach), or necessary for solving an ill-defined integral equation to deconvolute the unknown LDOS (e.g., those based on Tersoff approach). Using a serial expansion of Tersoff formulae, we propose a flexible method how to reconstruct the LDOS from local current-voltage characteristics measured in STM experiments. We established a set of key physical parameters, which characterize the tunneling current of a STM probe - sample contact and the sample LDOS expanded in Gaussian functions. Using a direct variational method coupled with a probabilistic analysis, we determine these parameters from the STM experiments for MoS2 nanoflakes with different number of layers. The main result is the reconstruction of the LDOS in a relatively wide energy range around a Fermi level, which allows insight in the local band structure of LD-TMDs. The reconstructed LDOS reveals pronounced size effects for the single-layer, bi-layer and three-layer MoS$_2$ nanoflakes, which we relate with low dimensionality and strong bending/corrugation of the nanoflakes. We hope that the proposed elaboration of the Tersoff approach allowing LDOS reconstruction will be of urgent interest for quantitative description of STM experiments, as well as useful for the microscopic physical understanding of the surface, strain and bending contribution to LD-TMDs electronic properties., Comment: 26 pages, 6 figures, 3 appendices

The robust approach for real-time analysis of the scanning transmission electron microscopy (STEM) data streams, based on the ensemble learning and iterative training (ELIT) of deep convolutional neural networks, is implemented on an operational microscope, enabling the exploration of the dynamics of specific atomic configurations under electron beam irradiation via an automated experiment in STEM. Combined with beam control, this approach allows studying beam effects on selected atomic groups and chemical bonds in a fully automated mode. Here, we demonstrate atomically precise engineering of single vacancy lines in transition metal dichalcogenides and the creation and identification of topological defects graphene. The ELIT-based approach opens the pathway toward the direct on-the-fly analysis of the STEM data and engendering real-time feedback schemes for probing electron beam chemistry, atomic manipulation, and atom by atom assembly.

Ferroelectric domain walls have emerged as one of the most fascinating objects in condensed matter physics due to the broad variability of functional behaviors they exhibit. However, the vast majority of domain walls studies have been focused on bias-induced dynamics and transport behaviors. Here, we introduce the scanning probe microscopy approach based on piezoresponse force microscopy (PFM) with a dynamically heated probe, combining local heating and local biasing of the material. This approach is used to explore the thermal polarization dynamics in soft Sn2P2S6 ferroelectrics, and allows for the exploration of phase transitions at individual domain walls. The strong and weak modulation regimes for the thermal PFM are introduced. The future potential applications of heated probe approach for functional SPM measurements including piezoelectric, elastic, microwave, and transport measurements are discussed.

Recent advances in electron, scanning probe, optical, and chemical imaging and spectroscopy yield bespoke data sets containing the information of structure and functionality of complex systems. In many cases, the resulting data sets are underpinned by low-dimensional simple representations encoding the factors of variability within the data. The representation learning methods seek to discover these factors of variability, ideally further connecting them with relevant physical mechanisms. However, generally the task of identifying the latent variables corresponding to actual physical mechanisms is extremely complex. Here, we explore an approach based on conditioning the data on the known (continuous) physical parameters, and systematically compare it with the previously introduced approach based on the invariant variational autoencoders. The conditional variational autoencoders (cVAE) approach does not rely on the existence of the invariant transforms, and hence allows for much greater flexibility and applicability. Interestingly, cVAE allows for limited extrapolation outside of the original domain of the conditional variable. However, this extrapolation is limited compared to the cases when true physical mechanisms are known, and the physical factor of variability can be disentangled in full. We further show that introducing the known conditioning results in the simplification of the latent distribution if the conditioning vector is correlated with the factor of variability in the data, thus allowing to separate relevant physical factors. We initially demonstrate this approach using 1D and 2D examples on a synthetic dataset and then extend it to the analysis of experimental data on ferroelectric domain dynamics visualized via Piezoresponse Force Microscopy., Comment: 20 pages, 8 figures

Electronic transport and hysteresis in metal halide perovskites (MHPs) are key to the applications in photovoltaics, light emitting devices, and light and chemical sensors. These phenomena are strongly affected by the materials microstructure including grain boundaries, ferroic domain walls, and secondary phase inclusions. Here, we demonstrate an active machine learning framework for 'driving' an automated scanning probe microscope (SPM) to discover the microstructures responsible for specific aspects of transport behavior in MHPs. In our setup, the microscope can discover the microstructural elements that maximize the onset of conduction, hysteresis, or any other characteristic that can be derived from a set of current-voltage spectra. This approach opens new opportunities for exploring the origins of materials functionality in complex materials by SPM and can be integrated with other characterization techniques either before (prior knowledge) or after (identification of locations of interest for detail studies) functional probing., Comment: 19 pages; 7 figures

We report the development and implementation of a hypothesis learning based automated experiment, in which the microscope operating in the autonomous mode identifies the physical laws behind the material's response. Specifically, we explore the bias induced transformations that underpin the functionality of broad classes of devices and functional materials from batteries and memristors to ferroelectrics and antiferroelectrics. Optimization and design of these materials require probing the mechanisms of these transformations on the nanometer scale as a function of the broad range of control parameters such as applied potential and time, often leading to experimentally intractable scenarios. At the same time, often the behaviors of these systems are understood within potentially competing theoretical models, or hypotheses. Here, we develop a hypothesis list that covers the possible limiting scenarios for the domain growth, including thermodynamic, domain wall pinning, and screening limited. We further develop and experimentally implement the hypothesis driven automated experiment in Piezoresponse Force Microscopy, autonomously identifying the mechanisms of the bias induced domain switching. This approach can be applied for a broad range of physical and chemical experiments with relatively low dimensional control parameter space and for which the possible competing models of the system behavior that ideally cover the full range of physical eventualities are known or can be created. These include other scanning probe microscopy modalities such as force distance curve measurements and nanoindentation, as well as materials synthesis and optimization., Comment: 25 pages, 6 figures

Ferroelectricity in binary oxides including hafnia and zirconia have riveted the attention of the scientific community due to highly unconventional physical mechanisms and the potential for integration of these materials into semiconductor workflows. Over the last decade, it has been argued that behaviors such as wake-up phenomena and an extreme sensitivity to electrode and processing conditions suggests that ferroelectricity in these materials is strongly coupled with additional mechanisms, with possible candidates including the ionic subsystem or strain. Here we argue that the properties of these materials emerge due to the interplay between the bulk competition between ferroelectric and structural instabilities, similar to that in classical antiferroelectrics, coupled with non-local screening mediated by the finite density of states at surfaces and internal interfaces. Via decoupling of electrochemical and electrostatic controls realized via environmental and ultra-high vacuum PFM, we show that these materials demonstrate a rich spectrum of ferroic behaviors including partial pressure- and temperature-induced transitions between FE and AFE behaviors. These behaviors are consistent with an antiferroionic model and suggest novel strategies for hafnia-based device optimization.

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 Pb0.6Sr0.4TiO3 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.

The proliferation of optical, electron, and scanning probe microscopies gives rise to large volumes of imaging data of objects as diversified as cells, bacteria, pollen, to nanoparticles and atoms and molecules. In most cases, the experimental data streams contain images having arbitrary rotations and translations within the image. At the same time, for many cases, small amounts of labeled data are available in the form of prior published results, image collections, and catalogs, or even theoretical models. Here we develop an approach that allows generalizing from a small subset of labeled data with a weak orientational disorder to a large unlabeled dataset with a much stronger orientational (and positional) disorder, i.e., it performs a classification of image data given a small number of examples even in the presence of a distribution shift between the labeled and unlabeled parts. This approach is based on the semi-supervised rotationally invariant variational autoencoder (ss-rVAE) model consisting of the encoder-decoder "block" that learns a rotationally (and translationally) invariant continuous latent representation of data and a classifier that encodes data into a finite number of discrete classes. The classifier part of the trained ss-rVAE inherits the rotational (and translational) invariances and can be deployed independently of the other parts of the model. The performance of the ss-rVAE is illustrated using the synthetic data sets with known factors of variation. We further demonstrate its application for experimental data sets of nanoparticles, creating nanoparticle libraries and disentangling the representations defining the physical factors of variation in the data. The code reproducing the results is available at https://github.com/ziatdinovmax/Semi-Supervised-VAE-nanoparticles.

The development of high-resolution imaging methods such as electron and scanning probe microscopy and atomic probe tomography have provided a wealth of information on structure and functionalities of solids. The availability of this data in turn necessitates development of approaches to derive quantitative physical information, much like the development of scattering methods in the early XX century which have given one of the most powerful tools in condensed matter physics arsenal. Here, we argue that this transition requires adapting classical macroscopic definitions, that can in turn enable fundamentally new opportunities in understanding physics and chemistry. For example, many macroscopic definitions such as symmetry can be introduced locally only in a Bayesian sense, balancing the prior knowledge of materials' physics and experimental data to yield posterior probability distributions. At the same time, a wealth of local data allows fundamentally new approaches for the description of solids based on construction of statistical and physical generative models, akin to Ginzburg-Landau thermodynamic models. Finally, we note that availability of observational data opens pathways towards exploring causal mechanisms underpinning solid structure and functionality.

Piezoresponse force microscopy (PFM) is a powerful characterization technique to readily image and manipulate the ferroelectric domains. PFM gives an insight into the strength of local piezoelectric coupling and polarization direction through PFM amplitude and phase, respectively. Converting measured arbitrary units into units of effective piezoelectric constant remains a challenge, and insufficient methods are often used. While most quantification efforts have been spent on quantifying the PFM amplitude signal, little attention has been given to the PFM phase, which is often arbitrarily adjusted to fit expectations. This is problematic when investigating materials with unknown or negative sign of the probed effective electrostrictive coefficient or strong frequency dispersion of electromechanical responses, because assumptions about the PFM phase cannot be reliably made. The PFM phase can, however, provide important information on the polarization orientation and the sign of the effective electrostrictive coefficient probed by PFM. Most notably, the orientation of the PFM hysteresis loop is determined by the PFM phase. Moreover, when presenting PFM data as a combined signal, the resulting response can be artificially lowered or asymmetric if the phase data have not been correctly processed. Here, we explain the PFM amplitude quantification process and demonstrate a path to identify the phase offset required to extract correct meaning from the PFM phase data. We explore different sources of phase offsets including the experimental setup, instrumental contributions, and data analysis. We discuss the physical working principles of PFM and develop a strategy to extract physical meaning from the PFM amplitude and phase.

The use of advanced data analytics and applications of statistical and machine learning approaches (‘AI’) to materials science is experiencing explosive growth recently. In this prospective, we review recent work focusing on generation and application of libraries from both experiment and theoretical tools, across length scales. The available library data both enables classical correlative machine learning, and also opens the pathway for exploration of underlying causative physical behaviors. We highlight the key advances facilitated by this approach, and illustrate how modeling, macroscopic experiments and atomic-scale imaging can be combined to dramatically accelerate understanding and development of new material systems via a statistical physics framework. These developments point towards a data driven future wherein knowledge can be aggregated and used collectively, accelerating the advancement of materials science.

The emergence of scanning probe and electron beam imaging techniques have 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 the local symmetry breaking phenomena, representing stochastic manifestation of underpinning generative physical model. Here, we explore the reconstruction of exchange integrals in the Hamiltonian for the lattice model with two competing interactions from the observations of the microscopic degrees of freedom and establish the uncertainties and reliability of such analysis in a broad parameter-temperature space. 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 the 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, quantum systems once the local descriptors linked to the relevant physical behaviors are known., Comment: 20 pages and 9 figures

Traditionally, phase transitions are explored using a combination of macroscopic functional characterization and scattering techniques, providing insight into average properties and symmetries of the lattice but local atomic level mechanisms during phase transitions generally remain unknown. Here we explore the mechanisms of a phase transition between the trigonal prismatic and distorted octahedral phases of layered chalogenides in the MoS2 ReS2 system from the observations of local degrees of freedom, namely atomic positions by Scanning Transmission Electron Microscopy (STEM). We employ local crystallographic analysis based on machine 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. In particular, we argue that the dependence of the average symmetry breaking distortion amplitude on global and local concentration can be used to separate local chemical and global electronic effects on transition. This approach allows exploring atomic mechanisms beyond the traditional macroscopic descriptions, utilizing the imaging of compositional fluctuations in solids to explore phase transitions over a range of realized and observed local stoichiometries and atomic configurations., Comment: 5 figures, 20 pages including supplementary

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

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

The nature of the atomic defects on the hydrogen passivated Si (100) surface is analyzed using deep learning and scanning tunneling microscopy (STM). A robust deep learning framework capable of identifying atomic species, defects, in the presence of non-resolved contaminates, step edges, and noise is developed. The automated workflow, based on the combination of several networks for image assessment, atom-finding and defect finding, is developed to perform the analysis at different levels of description and is deployed on an operational STM platform. This is further extended to unsupervised classification of the extracted defects using the mean-shift clustering algorithm, which utilizes features automatically engineered from the combined output of neural networks. This combined approach allows the identification of localized and extended defects on the topographically non-uniform surfaces or real materials. Our approach is universal in nature and can be applied to other surfaces for building comprehensive libraries of atomic defects in quantum materials.

Experimental advances in condensed matter physics and material science have enabled ready access to atomic-resolution images, with resolution of modern tools often sufficient to extract minute details of symmetry-breaking distortions such as polarization, octahedra tilts, or other structure-coupled order parameters. The patterns of observed distortions in turn contain the information on microscopic driving forces defining the development of materials microstructure and associated thermodynamics. However, the analysis of underpinning physical models from experimentally observed microscopic degrees of freedom remains a largely unresolved issue. Here, we explore such an approach using the paradigmatic Ising model on a square lattice. We show that the microscopic parameters of the Ising model both for ferromagnetic and antiferromagnetic case can be extracted from the spin configurations for temperatures an order of magnitude higher than the phase transition and perform uncertainty analysis for such reconstructions. This suggests that microscopic observations of materials with sufficiently high precision can provide information on generative physics at temperatures well above corresponding phase transition, opening new horizons for scientific exploration via high-resolution imaging.

Multiferroic materials have driven significant research interest due to their promising technological potential. Developing new room-temperature multiferroics and understanding their fundamental properties are important to reveal unanticipated physical phenomena and potential applications. Here, a new room temperature multiferroic nanocomposite comprised of an ordered ferrimagnetic spinel LiFe5O8 (LFO) and a ferroelectric perovskite BiFeO3 (BFO) is presented. We observed that lithium (Li)-doping in BFO favors the formation of LFO spinel as a secondary phase during the synthesis of LixBi1-xFeO3 nanoceramics. Multimodal functional and chemical imaging methods are used to map the relationship between doping-induced phase separation and local ferroic properties in both the BFO-LFO composite ceramics and self-assembled nanocomposite thin films. The energetics of phase separation in Li doped BFO and the formation of BFO-LFO composites is supported by first principles calculations. These findings shed light on Li-ion role in the formation of a functionally important room temperature multiferroic and open a new approach in the synthesis of light element doped nanocomposites.

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., Comment: 30 pages, 5 figures, 1 appendix

We introduce the first systematic database of scanning tunneling microscope (STM) images obtained using density functional theory (DFT) for two-dimensional (2D) materials, calculated using the Tersoff-Hamann method. It currently contains data for 716 exfoliable 2D materials. Examples of the five possible Bravais lattice types for 2D materials and their Fourier-transforms are discussed. All the computational STM images generated in this work will be made available on the JARVIS-DFT website (https://www.ctcms.nist.gov/~knc6/JVASP.html). We find excellent qualitative agreement between the computational and experimental STM images for selected materials. As a first example application of this database, we train a convolution neural network (CNN) model to identify Bravais lattices from the STM images. We believe the model can aid high-throughput experimental data analysis. These computational STM images can directly aid the identification of phases, analyzing defects and lattice-distortions in experimental STM images, as well as be incorporated in the autonomous experiment workflows.

Many energy conversion, sensing, and microelectronic applications based on ferroic materials are determined by the domain structure evolution under applied stimuli. New hyperspectral, multidimensional spectroscopic techniques now probe dynamic responses at relevant length and time scales to provide an understanding of how these nanoscale domain structures impact macroscopic properties. Such approaches, however, remain limited in use because of the difficulties that exist in extracting and visualizing scientific insights from these complex datasets. Using multidimensional band-excitation scanning probe spectroscopy and adapting tools from both computer vision and machine learning, an automated workflow is developed to featurize, detect, and classify signatures of ferroelectric/ferroelastic switching processes in complex ferroelectric domain structures. This approach enables the identification and nanoscale visualization of varied modes of response and a pathway to statistically meaningful quantification of the differences between those modes. Among other things, the importance of domain geometry is spatially visualized for enhancing nanoscale electromechanical energy conversion.

Materials characterization and property measurements are a cornerstone of material science, providing feedback from synthesis to applications. Traditionally, a single sample is used to derive information on a single point in composition space, and imperfections, impurities and stochastic details of material structure are deemed irrelevant or complicating factors in analysis. Here we demonstrate that atomic-scale studies of a single nominal composition can provide information on a finite area of chemical space. This information can be used to reconstruct the material properties in a finite composition and temperature range. We develop a statistical physics-based framework that incorporates chemical and structural data to infer effective atomic interactions driving segregation in a La5/8Ca3/8MnO3 thin-film. A variational autoencoder is used to determine anomalous behaviors in the composition phase diagram. This study provides a framework for creating generative models from diverse data and provides direct insight into the driving forces for cation segregation in manganites., Comment: 34 pages, 5 figures and supplementary

Population and distribution of defects is one of the primary parameters controlling materials functionality, are often non-ergodic and strongly dependent on synthesis history, and are rarely amenable to direct theoretical prediction. Here, dynamic electron beam-induced transformations in Si deposited on a graphene monolayer are used to create libraries of the possible Si and carbon vacancy defects. Automated image analysis and recognition based on deep learning networks is developed to identify and enumerate the defects, creating a library of (meta) stable defect configurations. The electronic properties of the sample surface are further explored by atomically resolved scanning tunneling microscopy (STM). Density functional theory is used to estimate the STM signatures of the classified defects from the created library, allowing for the identification of several defect types across the imaging platforms. This approach allows automatic creation of defect libraries in solids, exploring the metastable configurations always present in real materials, and correlative studies with other atomically-resolved techniques, providing comprehensive insight into defect functionalities. Such libraries will be of critical importance in automated AI-assisted workflows for materials prediction and atom-by atom manipulation via electron beams and scanning probes., Comment: Updated Figure 1 and References. Expanded Methods section. Added Supplementary Material. Minor text edits

In the last decade, the atomically focused beam of a scanning transmission electron microscope (STEM) was shown to induce a broad set of transformations of material structure, open pathways for probing atomic-scale reactions and atom-by-atom matter assembly. However, the mechanisms of beam-induced transformations remain largely unknown, due to an extreme mismatch between the energy and time scales of electron passage through solids and atomic and molecular motion. Here, we demonstrate that a single dopant Si atom in the graphene lattice can be used as an atomic scale force sensor, providing information on the random force exerted by the beam on chemically-relevant time scales. Using stochastic reconstruction of molecular dynamic simulations, we recover the potential energy landscape of the atom and use it to determine the beam-induced effects in the thermal (i.e. white noise) approximation. We further demonstrate that the moving atom under beam excitation can be used to map potential energy along step edges, providing information about atomic-scale potentials in solids. These studies open the pathway for quantitative studies of beam-induced atomic dynamics, elementary mechanisms of solid-state transformations, and predictive atom-by-atom fabrication.

Using Landau-Ginzburg-Devonshire approach, we predict the intrinsic instability of the ferroelectric-ferroelastic domain walls in the multiferroic BiFeO3 emerging from the interplay between the gradient terms of the antiferrodistortive and ferroelectric order parameters at the walls. These instabilities are the interface analogue of the structural instabilities in the vicinity of phase coexistence in the bulk; and so they do not steam from incomplete polarization screening in thin films or its spatial confinement, electrostrictive or flexoelectric coupling. The effect of BiFeO3 material parameters on the 71 degree, 109 degree, and 180 degree walls is explored, and it is shown that the meandering instability appears at 109 degree, and 180 degree walls for small gradient energies, and the walls become straight and broaden for higher gradients. In contrast to the 180 degree and 109 degree domain walls, uncharged 71 degree walls are always straight, and their width increases with increasing the tilt gradient coefficient. The wall instability and associated intrinsic meandering provide a new insight into the behavior of morphotropic and relaxor materials, wall pinning, and mechanisms of interactions between order parameter fields and local microstructure., Comment: 37 pages, 12 figures, 2 tables

Recent observations of unusual ferroelectricity in thin films of HfO_2 and related materials have attracted broad interest to the materials and led to the emergence of a number of competing models for observed behaviors. Here we develop the electrochemical mechanism of observed ferroelectric-like behaviors, namely the collective phenomena of elastic and electric dipoles originated from oxygen vacancies formation in the vicinity of film surfaces, as well as from grain boundaries and other types of inhomogeneities inside the film. The ferroelectric phase is induced by the "electrochemical" coupling, that is the joint action of the omnipresent electrostriction and "chemical" pressure, which lead to the sign change of the positive coefficient alfa in the quadratic term alfa*P^2 in the order-disorder type thermodynamic functional depending on polarization P. Negative coefficient alfa becomes the driving force of the transition to the long-range ordered ferroelectric phase with the spontaneous polarization P in the direction normal to the film surface. Using the above ideas, we estimated that the reversible ferroelectric polarization, as high as (0.05 - 0.2) C/m^2, can be induced by oxygen vacancies in HfO_2 films of thickness less than (20 - 30) nm. Semi-quantitative agreement with available experimental data is demonstrated., Comment: 29 pages, 5 figures

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.

Leveraging competition between energetically degenerate states to achieve large field-driven responses is a hallmark of functional materials, but routes to such competition are limited. Here, a new route to such effects involving domain-structure competition is demonstrated, which arises from strain-induced spontaneous partitioning of PbTiO3 thin films into nearly energetically degenerate, hierarchical domain architectures of coexisting c/a and a1 /a2 domain structures. Using band-excitation piezoresponse force microscopy, this study manipulates and acoustically detects a facile interconversion of different ferroelastic variants via a two-step, three-state ferroelastic switching process (out-of-plane polarized c+ → in-plane polarized a → out-of-plane polarized c- state), which is concomitant with large nonvolatile electromechanical strains (≈1.25%) and tunability of the local piezoresponse and elastic modulus (>23%). It is further demonstrated that deterministic, nonvolatile writing/erasure of large-area patterns of this electromechanical response is possible, thus showing a new pathway to improved function and properties.

Fundamental mechanisms of energy storage, corrosion, sensing, and multiple biological functionalities are directly coupled to electrical processes and ionic dynamics at solid-liquid interfaces. In many cases, these processes are spatially inhomogeneous taking place at grain boundaries, step edges, point defects, ion channels, etc. and possess complex time and voltage dependent dynamics. This necessitates time-resolved and real-space probing of these phenomena. In this review, we discuss the applications of force-sensitive voltage modulated scanning probe microscopy (SPM) for probing electrical phenomena at solid-liquid interfaces. We first describe the working principles behind electrostatic and Kelvin Probe Force microscopies (EFM & KPFM) at the gas-solid interface, review the state of the art in advanced KPFM methods and developments to (i) overcome limitations of classical KPFM, (i) expand the information accessible from KPFM, and (iii) extend KPFM operation to liquid environments. We briefly discuss the theoretical framework of the electrical double layer (EDL) forces and dynamics, the implications and breakdown of classical EDL models for highly charged interfaces, or under high ion concentrations, and briefly describe recent modifications of the classical EDL theory relevant for understanding nanoscale electrical measurements at the solid-liquid interface. We further review the latest achievements in mapping surface charge, dielectric constants, and electrodynamic and electrochemical processes in liquids. Finally, we outline the key challenges and opportunities that exist in the field of nanoscale electrical measurements in liquid as well as provide a roadmap for the future development of liquid KPFM., 173 pages 69 Figures

Using the self-consistent Landau-Ginzburg-Devonshire approach we simulate and analyze the spontaneous formation of the domain structure in thin ferroelectric films covered with the surface screening charge of the specific nature (Bardeen-type surface states). Hence we consider the competition between the screening and the domain formation as alternative ways to reduce the electrostatic energy and reveal unusual peculiarities of distributions of polarization, electric and elastic fields conditioned by the surface screening length and the flexocoupling strength. We have established that the critical thickness of the film and its transition temperature to a paraelectric phase strongly depend on the Bardeen screening length, while the flexocoupling affects the polarization rotation and closure domain structure and induces ribbon-like nano-scale domains in the film depth far from the top open surface. Hence the joint action of the surface screening (originating from e.g. the adsorption of ambient ions or surface states) and flexocoupling may remarkably modify polar and electromechanical properties of thin ferroelectric films., Comment: 33 pages, 5 figures

The presence of mobile ions complicates the implementation of voltage-modulated scanning probe microscopy techniques such as Kelvin probe force microscopy (KPFM). Overcoming this technical hurdle, however, provides a unique opportunity to probe ion dynamics and electrochemical processes in liquid environments and the possibility to unravel the underlying mechanisms behind important processes at the solid–liquid interface, including adsorption, electron transfer and electrocatalysis. Here we describe the development and implementation of electrochemical force microscopy (EcFM) to probe local bias- and time-resolved ion dynamics and electrochemical processes at the solid–liquid interface. Using EcFM, we demonstrate contact potential difference measurements, consistent with the principles of open-loop KPFM operation. We also demonstrate that EcFM can be used to investigate charge screening mechanisms and electrochemical reactions in the probe–sample junction. We further establish EcFM as a force-based imaging mode, allowing visualization of the spatial variability of sample-dependent local electrochemical properties.

We consider a typical heterostructure domain patterned ferroelectric film/ultra thin dielectric layer/ semiconductor, where the semiconductor can be an electrolyte, paraelectric or multi layered graphene. Unexpectedly we have found that the space charge modulation profile and amplitude in the semiconductor, that screens the spontaneous polarization of a 180-degree domain structure of ferroelectric, depends on the domain structure period, dielectric layer thickness and semiconductor screening radius in a rather non-trivial nonlinear way. Multiple size effects appearance and manifestation are defined by the relationship between these three parameters. In addition, we show that the concept of effective gap can be introduced in a simple way only for a single domain limit. Obtained analytical results open the way for understanding of current AFM maps of contaminated ferroelectric surfaces in ambient atmosphere as well as explore the possibilities of conductivity control in ultra-thin semiconductor layers., Comment: 28 pages, 9 figures, 2 tables, Supplementary materials

Using Landau-Ginzburg-Devonshire theory we describe 180-degree domain wall structure, intrinsic energy and carrier accumulation in rhombohedral phase of BaTiO3 as a function of the wall orientation and flexoelectric coupling strength. Two types of domain wall structures (phases of the wall) exist depending on the wall orientation. The low-energy 'achiral' phase occurs in the vicinity of the {110} wall orientation and has odd polarization profile invariant with respect to inversion about the wall center. The second 'chiral' phase occurs around {211} wall orientations and corresponds to mixed parity domain walls that may be of left-handed or right-handed chirality. The transformation between the phases is abrupt, accompanied with 20-30% change of the domain wall thickness and can happen at fixed wall orientation with temperature change. We suggest that the phase transition may be detected through domain wall thickness change or by c-AFM. The structure of the domain wall is correlated to its conductivity through polarization component normal to the domain wall, which causes free carriers accumulation. Depending on the temperature and flexoelectric coupling strength relative conductivity of the wall becomes at least one order of magnitude higher than in the single-domain region, creating c-AFM contrast enhancement pronounced and detectable., Comment: 31 pages, 10 figures, Supplementary materials

Perovskite transition-metal oxides are networks of corner-sharing octahedra whose tilts and distortions are known to affect their electronic and magnetic properties. We report calculations on a model interfacial structure to avoid chemical influences and show that the symmetry mismatch imposes an interfacial layer with distortion modes that do not exist in either bulk material, creating new interface properties driven by symmetry alone. Depending on the resistance of the octahedra to deformation, the interface layer can be as small as one unit cell or extend deep into the thin film.

The interaction of ferroelectric 180 degree domain wall with a strongly inhomogeneous electric field of biased Scanning Probe Microscope tip is analyzed within continuous Landau-Ginzburg-Devonshire theory. Equilibrium shape of the initially flat domain wall boundary bends, attracts or repulses from the probe apex, depending on the sign and value of the applied bias. For large tip-wall separations, the probe-induced domain nucleation is possible. The approximate analytical expressions for the polarization distribution are derived using direct variational method. The expressions provide insight how the equilibrium polarization distribution depends on the wall finite-width, correlation and depolarization effects, electrostatic potential distribution of the probe and ferroelectric material parameters., Comment: 37 pages, 9 figures, 4 Appendices, to be submitted to Phys. Rev. B

Piezoresponse Force Spectroscopy (PFS) has emerged as a powerful tool for probing polarization dynamics on the nanoscale. Application of a dc bias to a nanoscale probe in contact with a ferroelectric surface results in the nucleation and growth of a ferroelectric domain below the probe apex. The latter affects local electromechanical response detected by the probe. Resulting hysteresis loop contains information on local ferroelectric switching. The self-consistent analysis of the PFS data requires (a) deriving the thermodynamic parameters of domain nucleation and (b) establishing the relationships between domain parameters and PFM signal. Here, we analyze the effect of screening at the domain wall on local polarization reversal mechanism. It is shown that the screening control both the domain nucleation activation energy and hysteresis loop saturation rate., Comment: 22 pages, 6 figures, 1 appendix, invited paper for special issue of Ferroelectrics devoted to academician V.L.Ginsburg anniversary

Piezoresponse Force Spectroscopy (PFS) has emerged as a powerful technique for probing highly localized switching behavior and the role of microstructure and defects on switching. The application of a dc bias to a scanning probe microscope tip in contact with a ferroelectric surface results in the nucleation and growth of a ferroelectric domain below the tip, resulting in changes in local electromechanical response. Resulting hysteresis loops contains information on local ferroelectric switching behavior. The signal in PFS is the convolution of the volume of the nascent domain and the probing volume of the tip. Here, we analyze the signal formation mechanism in PFS by deriving the main parameters of domain nucleation in a semi-infinite material and establishing the relationships between domain parameters and PFM signal using a linear Greens function theory. The effect of surface screening and finite Debye length on the switching behavior is established. In particular, we predict that the critical nucleus size in PFM is controlled by the surface screening mechanism and in the absence of screening, tip-induced switching is impossible. Future prospects of PFS to study domain nucleation in the vicinity of defects, local switching centers in ferroelectrics, and unusual polarization states in low-dimensional ferroelectrics are discussed., Comment: 74 pages, 18 figures, 3 appendices, sent to Phys. Rev. B

Piezoresponse Force Microscopy (PFM) has emerged as a primary tool for imaging, domain engineering, and switching spectroscopy on ferroelectric materials. Quantitative interpretation of PFM data including measurements of the intrinsic width of the domain walls, geometric parameters of the domain below the tip in local hysteresis loop measurements, as well as interpretation of switching and coercive biases in terms of materials properties and switching mechanisms, requires reliable knowledge on electrostatic field structure produced by the tip. Using linear imaging theory, we develop a theoretical approach for interpretation of these measurements and determination of tip parameters from a calibration standard. The resolution and object transfer functions in PFM are derived and effect of materials parameters on resolution is determined. Closed form solutions for domain wall profiles in vertical and lateral PFM and signal from cylindrical domain in transversally isotropic piezoelectric are derived for point-charge and sphere-plane geometry of the tip., Comment: 76 pages, 18 figures, 3 Appendices, submitted to Phys. Rev. B

Strong coupling between electrical and mechanical phenomena and the presence of switchable polarization have enabled applications of ferroelectric materials for nonvolatile memories (FeRAM), data storage, and ferroelectric lithography. Understanding the local functionality of inorganic ferroelectrics including crystallographic orientation, piezoresponse, elasticity, and mechanisms for polarization switching, requires probing material structure and properties on the level of a single ferroelectric grain or domain. Here, I present recent studies on electromechanical, mechanical, and spectroscopic characterization of ferroelectric materials by Scanning Probe Microscopy. Three-dimensional electromechanical imaging, referred to as Vector Piezoresponse Force Microscopy, is presented. Nanoelectromechanics of PFM, including the structure of coupled electroelastic fields and tip-surface contact mechanics, is analyzed. This establishes a complete continuum mechanics description of the PFM and Atomic Force Acoustic Microscopy imaging mechanisms. Mechanism for local polarization switching is analyzed. The hysteresis loop shape is shown to be determined by the formation of the transient domain below the tip, the size of which increases with the tip bias. Spectroscopic imaging that allows relevant characteristics of switching process, such as imprint bias, pinning strength, remanent and saturation response, is introduced. Finally, resolution in PFM and vector PFM imaging of local crystallographic and molecular orientation and disorder is introduced., Comment: 8 pages, 7 figures. Extended abstract for 12th US-Japanese Seminar on Dielectric and Piezoelectric Ceramics, Annapolis, MD, November 6-9 2005

Scanning Probe Microscopy is used to study and quantify the nanoscale electric phenomena in the two classes of oxide systems, namely transport at electroactive grain boundaries and surface behavior of ferroelectric materials. Scanning Impedance Microscopy is developed to study the capacitance and local C-V characteristic of the interfaces combining the spatial resolution of traditional SPMs with the precision of conventional electrical measurements. SPM of SrTiO3 grain boundaries in conjunction with variable temperature impedance spectroscopy and I-V measurements allowed to find and theoretically justify the effect of field suppression of dielectric constant in the vicinity of the electroactive interfaces in strontium titanate. Similar approaches were used to study ferroelectric properties and ac and dc transport behavior in a number of polycrystalline oxides. In the second part, the effects of local charge density on the chemistry and physics of ferroelectric surfaces are studied. The kinetics and thermodynamics parameters of adsorption are assessed by variable temperature SPM. Piezoresponse force microscopy is used to engineer domain patterns on ferroelectric surfaces. Localized photochemical activity of ferroelectric surfaces is explored as a new tool for metallic nanostructures fabrication., Comment: Ph.D. Thesis, September 2002, 304 pages, 108 figures, 2.4 MB PDF file, Higher quality version available at sergei2.kalininweb.com