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Sergei V. Kalinin

Publications and source records attributed to Sergei V. Kalinin.

At least 91 records · Page 5Linked to original sources
arXiv2023

Ferroelectric Schottky diodes of CuInP$_2$S$_6$ nanosheet

Ferroelectricity in van der Waals (vdW) layered material has attracted a great deal of interest recently. CuInP$_2$S$_6$ (CIPS), the only vdW layered material whose ferroelectricity in the bulk was demonstrated by direct polarization measurements, was shown to remain ferroelectric down to a thickness of a few nanometers. However, its ferroelectric properties have just started to be explored in the context of potential device applications. We report here the preparation and measurements of metal-ferroelectric semiconductor-metal heterostructures using nanosheets of CIPS obtained by mechanical exfoliation. Four bias voltage and polarization dependent resistive states were observed in the current-voltage characteristics, which we attribute to the formation of ferroelectric Schottky diode, along with switching behavior.

cond-mat.mtrl-sci↗
arXiv2023

Strain-Induced Polarization Enhancement in BaTiO$_3$ Core-Shell Nanoparticles

Despite fascinating experimental results, the influence of defects and elastic strains on the physical state of nanosized ferroelectrics is still poorly explored theoretically. One of unresolved theoretical problems is the analytical description of the strongly enhanced spontaneous polarization, piezoelectric response, and dielectric properties of ferroelectric oxide thin films and core-shell nanoparticles induced by elastic strains and stresses. In particular, the 10-nm quasi-spherical BaTiO3 core-shell nanoparticles reveal a giant spontaneous polarization up to 130 mu_C/cm2, where the physical origin is a large Ti off-centering. The available theoretical description cannot explain the giant spontaneous polarization observed in these spherical nanoparticles. This work analyzes polar properties of BaTiO3 core-shell spherical nanoparticles using the Landau-Ginzburg-Devonshire approach, which considers the nonlinear electrostriction coupling and large Vegard strains in the shell. We reveal that a spontaneous polarization greater than 50 mu_C/cm2 can be stable in a (10-100) nm BaTiO3 core at room temperature, where a 5 nm paraelectric shell is stretched by (3-6)% due to Vegard strains, which contribute to the elastic mismatch at the core-shell interface. The polarization value 50 mu_C/cm2 corresponds to high tetragonality ratios (1.02 - 1.04), which is further increased up to 100 mu_C/cm2 by higher Vegard strains and/or intrinsic surface stresses leading to unphysically high tetragonality ratios (1.08 - 1.16). The nonlinear electrostriction coupling and the elastic mismatch at the core-shell interface are key physical factors of the spontaneous polarization enhancement in the core. Doping with the highly-polarized core-shell nanoparticles can be useful in optoelectronics and nonlinear optics, electric field enhancement, reduced switching voltages, catalysis, and electrocaloric nanocoolers.

cond-mat.mtrl-sci↗
arXiv2023

The interplay between ferroelectricity and electrochemical reactivity on the surface of binary ferroelectric Al$_x$B$_{1-x}$N

Polarization dynamics and domain structure evolution in ferroelectric Al$_{0.93}$B$_{0.07}$N are studied using piezoresponse force microscopy and spectroscopies in ambient and controlled atmosphere environments. The application of negative unipolar, and bipolar first-order reverse curve (FORC) waveforms leads to a protrusion-like feature on the Al$_{0.93}$B$_{0.07}$N surface and reduction of electromechanical response due to electrochemical reactivity. A surface change is also observed on the application of fast alternating current bias. At the same time, the application of positive biases does not lead to surface changes. Comparatively in a controlled glove box atmosphere, stable polarization patterns can be observed, with minuscule changes in surface morphology. This surface morphology change is not isolated to applying biases to free surface, a similar topographical change is also observed at the electrode edges when cycling a capacitor in ambient environment. The study suggests that surface electrochemical reactivity may have a significant impact on the functionality of this material in the ambient environment. However, even in the controlled atmosphere, the participation of the surface ions in polarization switching phenomena and ionic compensation is possible.

cond-mat.mtrl-sci↗
arXiv2023

Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis

Electron and scanning probe microscopy produce vast amounts of data in the form of images or hyperspectral data, such as EELS or 4D STEM, that contain information on a wide range of structural, physical, and chemical properties of materials. To extract valuable insights from these data, it is crucial to identify physically separate regions in the data, such as phases, ferroic variants, and boundaries between them. In order to derive an easily interpretable feature analysis, combining with well-defined boundaries in a principled and unsupervised manner, here we present a physics augmented machine learning method which combines the capability of Variational Autoencoders to disentangle factors of variability within the data and the physics driven loss function that seeks to minimize the total length of the discontinuities in images corresponding to latent representations. Our method is applied to various materials, including NiO-LSMO, BiFeO3, and graphene. The results demonstrate the effectiveness of our approach in extracting meaningful information from large volumes of imaging data. The fully notebook containing implementation of the code and analysis workflow is available at https://github.com/arpanbiswas52/PaperNotebooks

cs.LG↗
arXiv2023

Bending-induced isostructural transitions in ultrathin layers of van der Waals ferrielectrics

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.

cond-mat.mtrl-sci↗
arXiv2023

Disentangling stress and curvature effects in layered 2D ferroelectric CuInP2S6

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.

cond-mat.mtrl-sci↗
arXiv2023

Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space

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.

cs.LG↗
arXiv2023

Anomalous Polarization Reversal in Strained Thin Films of CuInP$_2$S$_6$

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.

cond-mat.mtrl-sci↗
arXiv2023

A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments

Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade, with examples ranging from measurements of diffraction on combinatorial alloys at synchrotrons, to searches through chemical space with automated synthesis robots for perovskites. In virtually all cases, the target property of interest for optimization is defined apriori with limited human feedback during operation. In contrast, here we present the development of a new type of human in the loop experimental workflow, via a Bayesian optimized active recommender system (BOARS), to shape targets on the fly, employing human feedback. We showcase examples of this framework applied to pre-acquired piezoresponse force spectroscopy of a ferroelectric thin film, and then implement this in real time on an atomic force microscope, where the optimization proceeds to find symmetric piezoresponse amplitude hysteresis loops. It is found that such features appear more affected by subsurface defects than the local domain structure. This work shows the utility of human-augmented machine learning approaches for curiosity-driven exploration of systems across experimental domains. The analysis reported here is summarized in Colab Notebook for the purpose of tutorial and application to other data: https://github.com/arpanbiswas52/varTBO

cs.LG↗
arXiv2023

Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy

Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation. The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centered experiment workflow design and optimization. Here, we discuss the associated challenges with the transition to active ML, including sequential data analysis and out-of-distribution drift effects, the requirements for the edge operation, local and cloud data storage, and theory in the loop operations. Specifically, we discuss the relative contributions of human scientists and ML agents in the ideation, orchestration, and execution of experimental workflows and the need to develop universal hyper languages that can apply across multiple platforms. These considerations will collectively inform the operationalization of ML in next-generation experimentation.

cond-mat.mtrl-sci↗
arXiv2023

Sub-10 nm Probing of Ferroelectricity in Heterogeneous Materials by Machine Learning Enabled Contact Kelvin Probe Force Microscopy

Reducing the dimensions of ferroelectric materials down to the nanoscale has strong implications on the ferroelectric polarization pattern and on the ability to switch the polarization. As the size of ferroelectric domains shrinks to nanometer scale, the heterogeneity of the polarization pattern becomes increasingly pronounced, enabling a large variety of possible polar textures in nanocrystalline and nanocomposite materials. Critical to the understanding of fundamental physics of such materials and hence their applications in electronic nanodevices, is the ability to investigate their ferroelectric polarization at the nanoscale in a non-destructive way. We show that contact Kelvin probe force microscopy (cKPFM) combined with a k-means response clustering algorithm enables to measure the ferroelectric response at a mapping resolution of 8 nm. In a BaTiO3 thin film on silicon composed of tetragonal and hexagonal nanocrystals, we determine a nanoscale lateral distribution of discrete ferroelectric response clusters, fully consistent with the nanostructure determined by transmission electron microscopy. Moreover, we apply this data clustering method to the cKPFM responses measured at different temperatures, which allows us to follow the corresponding change in polarization pattern as the Curie temperature is approached and across the phase transition. This work opens up perspectives for mapping complex ferroelectric polarization textures such as curled/swirled polar textures that can be stabilized in epitaxial heterostructures and more generally mapping the polar domain distribution of any spatially-highly-heterogeneous ferroelectric materials.

cond-mat.mtrl-sci↗
arXiv2023

Roadmap on Deep Learning for Microscopy

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.

physics.optics↗
arXiv2023

Revealing intrinsic vortex-core states in Fe-based superconductors through machine-learning-driven discovery

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.

cond-mat.supr-con↗
arXiv2023

Learning the right channel in multimodal imaging: automated experiment in Piezoresponse Force Microscopy

We report the development and experimental implementation of the automated experiment workflows for the identification of the best predictive channel for a phenomenon of interest in spectroscopic measurements. The approach is based on the combination of ensembled deep kernel learning for probabilistic predictions and a basic reinforcement learning policy for channel selection. It allows the identification of which of the available observational channels, sampled sequentially, are most predictive of selected behaviors, and hence have the strongest correlations. We implement this approach for multimodal imaging in Piezoresponse Force Microscopy (PFM), with the behaviors of interest manifesting in piezoresponse spectroscopy. We illustrate the best predictive channel for polarization-voltage hysteresis loop and frequency-voltage hysteresis loop areas is amplitude in the model samples. The same workflow and code are universal and applicable for any multimodal imaging and local characterization methods.

cond-mat.mtrl-sci↗
arXiv2023

Post-Experiment Forensics and Human-in-the-Loop Interventions in Explainable Autonomous Scanning Probe Microscopy

The broad adoption of machine learning (ML)-based automated and autonomous experiments (AE) in physical characterization and synthesis requires development of strategies for understanding and intervention in the experimental workflow. Here, we introduce and realize strategies for post-acquisition forensic analysis applied to the deep kernel learning based AE scanning probe microscopy. This approach yields real-time and post-acquisition indicators of the progression of an active learning process interacting with an experimental system. We further illustrate that this approach can be extended towards human-in-the-loop autonomous experiments, where human operators make high-level decisions at high latencies setting the policies for AE, and the ML algorithm performs low-level fast decisions. The proposed approach is universal and can be extended to other physical and chemical imaging techniques and applications such as combinatorial library analysis. The full forensic analysis notebook is publicly available on GitHub at https://github.com/yongtaoliu/Forensics-DKL-BEPS.

cond-mat.mtrl-sci↗
arXiv2023

Designing Workflows for Materials Characterization

Experimental science is enabled by the combination of synthesis, imaging, and functional characterization. Synthesis of a new material is typically followed by a set of characterization methods 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 multi-resolution structural imaging and structural and functional characterization. Some considerations for theory-experiment workflows are provided. We further pose that the emergence of user facilities and cloud labs disrupt the classical progression from ideation, orchestration, and execution stages of workflow development and necessitate development of universal frameworks for workflow design, including universal hyper-languages describing laboratory operation, 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.

cond-mat.mtrl-sci↗
arXiv2023

MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies

Machine learning (ML) algorithms are showing a growing trend in helping the scientific communities across different disciplines and institutions to address large and diverse data problems. However, many available ML tools are programmatically demanding and computationally costly. The MLExchange project aims to build a collaborative platform equipped with enabling tools that allow scientists and facility users who do not have a profound ML background to use ML and computational resources in scientific discovery. At the high level, we are targeting a full user experience where managing and exchanging ML algorithms, workflows, and data are readily available through web applications. Since each component is an independent container, the whole platform or its individual service(s) can be easily deployed at servers of different scales, ranging from a personal device (laptop, smart phone, etc.) to high performance clusters (HPC) accessed (simultaneously) by many users. Thus, MLExchange renders flexible using scenarios -- users could either access the services and resources from a remote server or run the whole platform or its individual service(s) within their local network.

cs.LG↗
arXiv2022

Exploring the microstructural origins of conductivity and hysteresis in metal halide perovskites via active learning driven automated scanning probe microscopy

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.

cond-mat.mtrl-sci↗