arXiv subjects

Sergei V. Kalinin

Publications and source records attributed to Sergei V. Kalinin.

At least 217 records · Page 12Linked to original sources

arXiv2019

Atomic mechanisms for the Si atom dynamics in graphene: chemical transformations at the edge and in the bulk

Recent advances in scanning transmission electron microscopy (STEM) allow to observe solid-state transformations and reactions in materials induced by thermal stimulus or electron beam on the atomic level. However, despite the rate at which large volumes of data can be generated (sometimes in the gigabyte to terabyte range per single experiment), approaches for the extraction of material-specific knowledge on the kinetics and thermodynamics of these processes are still lacking. One of the critical issues lies in being able to map the evolution of various atomic structures and determine the associated transition probabilities directly from raw experimental data characterized by high levels of noise and missing structural elements. Here, we demonstrate an approach based on the combination of multiple machine learning techniques to study the dynamic behavior of e-beam irradiated Si atoms in the bulk and at the edges of single-layer graphene in STEM experiments. First, a deep learning network is used to convert experimental STEM movies into coordinates of individual Si and carbon atoms. Then, a Gaussian mixture model is further used to establish the elementary atomic configurations of the Si atoms, defining the bonding geometries and chemical species and accounting for the discrete rotational symmetry of the host lattice. Finally, the frequencies and Markov transition probabilities between these states are determined. This analysis enables insight into the thermodynamics of defect populations and chemical reaction networks from the atomically resolved STEM data. Here, we observe a clear tendency for the formation of a 1D Si crystal along zigzag direction of graphene edges and for the Si impurity coupling to topological defects in bulk graphene.

cond-mat.mtrl-sci↗

arXiv2019

Ferromagnetic-like behavior of Bi0.9La0.1FeO3-KBr nanocomposites

We studied magnetostatic response of the Bi0.9La0.1FeO3-KBr composites (BLFO-KBr) consisting of nanosized (about 100 nm) ferrite Bi0.9La0.1FeO3 (BLFO) conjugated with fine grinded ionic conducting KBr. When the fraction of KBr is rather small (less than 15 wt percent) the magnetic response of the composite is very weak and similar to that observed for the BLFO (pure KBr matrix without Bi1-xLaxFeO3 has no magnetic response as anticipated). However, when the fraction of KBr increases above 15percent, the magnetic response of the composite changes substantially and the field dependence of magnetization reveals ferromagnetic-like hysteresis loop with a remanent magnetization about 0.14 emu/g and coercive field about 1.8 Tesla (at room temperature). Nothing similar to the ferromagnetic-like hysteresis loop can be observed in BLFO ceramics, which magnetization quasi linearly increases with magnetic field. Different physical mechanisms were considered to explain the unusual experimental results for BLFO-KBr nanocomposites, but only those among them, which are highly sensitive to the interaction of antiferromagnetic Bi0.9La0.1FeO3 with ionic conductor KBr, can be relevant. An appropriate mechanism turned out to be ferro-magneto-ionic coupling.

cond-mat.mtrl-sci↗

arXiv2019

Manifold Learning of Four-dimensional Scanning Transmission Electron Microscopy

Four-dimensional scanning transmission electron microscopy (4D-STEM) of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomic structure and atomic electric fields. However, efficient processing and interpretation of large volumes of data remain challenging, especially for two-dimensional or light materials because the diffraction signal recorded on the pixelated arrays is weak. Here we employ data-driven manifold leaning approaches for straightforward visualization and exploration analysis of the 4D-STEM datasets, distilling real-space neighboring effects on atomically resolved deflection patterns from single-layer graphene, with single dopant atoms, as recorded on a pixelated detector. These extracted patterns relate to both individual atom sites and sublattice structures, effectively discriminating single dopant anomalies via multi-mode views. We believe manifold learning analysis will accelerate physics discoveries coupled between data-rich imaging mechanisms and materials such as ferroelectric, topological spin and van der Waals heterostructures.

eess.IV↗

arXiv2018

Intrinsic structural instabilities of domain walls driven by gradient couplings: meandering anferrodistortive-ferroelectric domain walls in BiFeO3

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.

cond-mat.mtrl-sci↗

arXiv2018

Possible Electrochemical Origin of Ferroelectricity in HfO2 Thin Films

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.

cond-mat.mtrl-sci↗

arXiv2018

Compressed Sensing of Scanning Transmission Electron Microscopy (STEM) on Non-Rectangular Scans

Scanning Transmission Electron Microscopy (STEM) has become the main stay for materials characterization on atomic level, with applications ranging from visualization of localized and extended defects to mapping order parameter fields. In the last several years, attention was attracted by potential of STEM to explore beam induced chemical processes and especially manipulating atomic motion, enabling atom-by-atom fabrication. These applications, as well as traditional imaging of beam sensitive materials, necessitate increasing dynamic range of STEM between imaging and manipulation modes, and increasing absolute scanning/imaging speeds, that can be achieved by combining sparse sensing methods with non-rectangular scanning trajectories. Here we developed a general method for real-time reconstruction of sparsely sampled images from high-speed, non-invasive and diverse scanning pathways. This approach is demonstrated on both the synthetic data where ground truth is known and the experimental STEM data. This work lays the foundation for future tasks such as optimal design of dose efficient scanning strategies and real-time adaptive inference and control of e-beam induced atomic fabrication.

physics.ins-det↗

arXiv2018

Oxygen vacancies induced ferroelectricity in relaxors with ABO3 structure

The consideration of oxygen vacancies influence on the relaxors with perovskite structure was considered in the framework of Landau-Ginzburg-Devonshire phenomenological theory. The theory applicability for relaxors is based on the existence of some hidden soft phonon polar mode in them, and its frequency could be zero at some negative temperature TC*. Main attention was paid to PZN-PLZT relaxor described by formula 0.3Pb(Zn1/3Nb2/3)O3-0.7(Pb0.96La0.04(ZrxTi1-x)0.99O3) with x = 0.52, where earlier experimental investigation of oxygen vacancies influence on the polar properties was performed and the evidence of oxygen vacancies induced ferroelectricity was obtained. Since the oxygen vacancies are known to be elastic dipoles, they influence upon elastic and electric fields due to Vegard and flexoelectric coupling. We include the vacancies elastic and electrostrictive contribution into free energy functional. The calculations of the vacancies impact on polar properties were performed using their concentration distribution function. It was shown that the negative Curie temperature of a relaxor TC* is renormalized by the elastic dipoles due to the electrostrictive coupling and can become positive at some large enough concentration of the vacancies. We calculated the local polarization and electric field induced by the flexo-chemical coupling in dependence on the concentration of oxygen vacancies. The coexistence of FE phase and relaxor state can take place because of inhomogeneity of vacancies concentration distribution.

cond-mat.mtrl-sci↗

arXiv2018

Deep Learning Analysis of Defect and Phase Evolution During Electron Beam Induced Transformations in WS2

Understanding elementary mechanisms behind solid-state phase transformations and reactions is the key to optimizing desired functional properties of many technologically relevant materials. Recent advances in scanning transmission electron microscopy (STEM) allow the real-time visualization of solid-state transformations in materials, including those induced by an electron beam and temperature, with atomic resolution. However, despite the ever-expanding capabilities for high-resolution data acquisition, the inferred information about kinetics and thermodynamics of the process and single defect dynamics and interactions is minima, due to the inherent limitations of manual ex-situ analysis of the collected volumes of data. To circumvent this problem, we developed a deep learning framework for dynamic STEM imaging that is trained to find the structures (defects) that break a crystal lattice periodicity and apply it for mapping solid state reactions and transformations in layered WS2 doped with Mo. This framework allows extracting thousands of lattice defects from raw STEM data (single images and movies) in a matter of seconds, which are then classified into different categories using unsupervised clustering methods. We further expanded our framework to extract parameters of diffusion for the sulfur vacancies and analyzed transition probabilities associated with switching between different configurations of defect complexes consisting of Mo dopant and sulfur vacancy, providing insight into point defect dynamics and reactions. This approach is universal and its application to beam induced reactions allows mapping chemical transformation pathways in solids at the atomic level.

cond-mat.mtrl-sci↗

arXiv2018

Learning from imperfections: constructing phase diagrams from atomic imaging of fluctuations

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.

cond-mat.mtrl-sci↗

arXiv2018

Assembling Di- and Multiatomic Si Clusters in Graphene via Electron Beam Manipulation

We demonstrate assembly of di-, tri- and tetrameric Si clusters on the graphene surface using sub-atomically focused electron beam of a scanning transmission electron microscope. Here, an electron beam is used to introduce Si substitutional defects and defect clusters in graphene with spatial control of a few nanometers, and enable controlled motion of Si atoms. The Si substitutional defects are then further manipulated to form dimers, trimers and more complex structures. The dynamics of a beam induced atomic scale chemical process is captured in a time-series of images at atomic resolution. These studies suggest that control of the e-beam induced local processes offers the next step toward atom-by-atom nanofabrication and provides an enabling tool for study of atomic scale chemistry in 2D materials.

cond-mat.mtrl-sci↗

arXiv2018

Labyrinthine domains in ferroelectric nanoparticles: a manifestation of gradient-driven morphological transition

In the framework of the Landau-Ginzburg-Devonshire (LGD) approach we studied finite size effects of the phase diagram and domain structure evolution in spherical nanoparticles of uniaxial ferroelectric. The particle surface is covered by a layer of screening charge characterized by finite screening length. The phase diagram, calculated in coordinates "particle radius vs. screening length" has a wide region of versatile poly-domain structures separating single-domain ferroelectric and nonpolar paraelectric phases. Unexpectedly, we revealed a region of stable irregular labyrinthine domains in the nanoparticles of uniaxial ferroelectric CuInP2S6 with the first order paraelectric-ferroelectric phase transition. We established that the origin of labyrinthine domains is the mutual balance of LGD, polarization gradient and electrostatic energies. The branching of the domain walls appears and increases rapidly when the polarization gradient energy decreases below the critical value. Allowing for the generality of LGD approach, we expect that the gradient-induced morphological transition can be the source of labyrinthine domains appearance in many spatially-confined ferroics with long-range order parameter, including relaxors, ferromagnetics, antiferrodistortive materials and materials with incommensurate ferroic phases.

cond-mat.mtrl-sci↗

arXiv2018

Control of polarization hysteresis temperature behavior by interfacial screening in thin ferroelectric films

Ferroelectric interfaces are unique model objects for fundamental studies of polar surface properties such as versatile screening mechanisms of spontaneous polarization by free carriers and possible ion exchange with ambient media. The effect of ionic adsorption by electrically-open (i.e. non-electroded) ferroelectric surface on polarization reversal in the ferroelectric had been investigated experimentally and theoretically, however the effect influence on the temperature behaviour of polarization hysteresis remains unexplored. Also the comparative theoretical analysis of the ferroelectric hysteresis for linear and nonlinear electronic screenings, and strongly nonlinear ionic screening of the spontaneous polarization was absent. In this work we study the free energy relief of a thin ferroelectric film covered by a screening charge layer of different nature and ultra-thin gap separating the film surface from the top electrode and calculate hysteresis loops of polarization and screening charge at different temperatures in the considered system. The dependence of the screening charge density on electric potential was considered for three basic models, namely for the linear Bardeen-type model of electronic surface states (BS), nonlinear Fermi-Dirac (FD) density of states describing two-dimensional electron gas at the film-gap interface, and strongly nonlinear Stephenson-Highland (SH) model describing the surface charge density of absorbed ions by Langmuir adsorption isotherms. Appeared that BS, FD and SH screening charges, which properties are principally different, determine the free energy relief (including polarization values corresponding to the free energy minima at different applied voltages) and control the shape of polarization hysteresis loops at different temperatures.

cond-mat.mes-hall↗

arXiv2018

Single atom force measurements: mapping potential energy landscapes via electron beam induced single atom dynamics

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.

cond-mat.mtrl-sci↗

arXiv2018

Data Mining for better material synthesis: the case of pulsed laser deposition of complex oxides

The pursuit of more advanced electronics, finding solutions to energy needs, and tackling a wealth of social issues often hinges upon the discovery and optimization of new functional materials that enable disruptive technologies or applications. However, the discovery rate of these materials is alarmingly low. Much of the information that could drive this rate higher is scattered across tens of thousands of papers in the extant literature published over several decades, and almost all of it is not collated and thus cannot be used in its entirety. Many of these limitations can be circumvented if the experimentalist has access to systematized collections of prior experimental procedures and results that can be analyzed and built upon. Here, we investigate the property-processing relationship during growth of oxide films by pulsed laser deposition. To do so, we develop an enabling software tool to (1) mine the literature of relevant papers for synthesis parameters and functional properties of previously studied materials, (2) enhance the accuracy of this mining through crowd sourcing approaches, (3) create a searchable repository that will be a community-wide resource enabling material scientists to leverage this information, and (4) provide through the Jupyter notebook platform, simple machine-learning-based analysis to learn the complex interactions between growth parameters and functional properties (all data and codes available on https://github.com/ORNL-DataMatls). The results allow visualization of growth windows, trends and outliers, and which can serve as a template for analyzing the distribution of growth conditions, provide starting points for related compounds and act as feedback for first-principles calculations. Such tools will comprise an integral part of the materials design schema in the coming decade.

cond-mat.mtrl-sci↗

arXiv2018

Mapping mesoscopic phase evolution during e-beam induced transformations via deep learning of atomically resolved images

Understanding transformations under electron beam irradiation requires mapping the structural phases and their evolution in real time. To date, this has mostly been a manual endeavor comprising of difficult frame-by-frame analysis that is simultaneously tedious and prone to error. Here, we turn towards the use of deep convolutional neural networks (DCNN) to automatically determine the Bravais lattice symmetry present in atomically-resolved images. A DCNN is trained to identify the Bravais lattice class given a 2D fast Fourier transform of the input image. Monte-Carlo dropout is used for determining the prediction probability, and results are shown for both simulated and real atomically-resolved images from scanning tunneling microscopy and scanning transmission electron microscopy. A reduced representation of the final layer output allows to visualize the separation of classes in the DCNN and agrees with physical intuition. We then apply the trained network to electron beam-induced transformations in WS2, which allows tracking and determination of growth rate of voids. These results are novel in two ways: (1) It shows that DCNNs can be trained to recognize diffraction patterns, which is markedly different from the typical "real image" cases, and (2) it provides a method with in-built uncertainty quantification, allowing the real-time analysis of phases present in atomically resolved images.

cond-mat.mtrl-sci↗

arXiv2018

Mapping fast evolution of transient surface photovoltage dynamics using G-Mode Kelvin probe force microscopy

Optoelectronic phenomena in materials such as organic/inorganic hybrid perovskites depend on a complex interplay between light induced carrier generation and fast (electronic) and slower (ionic) processes, all of which are known to be strongly affected by structural inhomogeneities such as interfaces and grain boundaries. Here, we develop a time resolved Kelvin probe force microscopy (KPFM) approach, based on the G-Mode SPM platform, allowing quantification of surface photovoltage (SPV) with microsecond temporal and nanoscale spatial resolution. We demonstrate the approach on methylammonium lead bromide (MAPbBr3) thin films and further highlight the usefulness of unsupervised clustering methods to quickly discern spatial variability in the information rich SPV dataset. Using this technique, we observe concurrent spatial and ultra-fast temporal variations in the SPV generated across the thin film, indicating that structure is likely responsible for the heterogenous behavior.

physics.app-ph↗

arXiv2018

Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations

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

cond-mat.mtrl-sci↗

arXiv2018

Deep analytics of atomically-resolved images: manifest and latent features

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.

cond-mat.mtrl-sci↗