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Sergei V. Kalinin

Publications and source records attributed to Sergei V. Kalinin.

At least 199 records · Page 11Linked to original sources
arXiv2020

Robust multi-scale multi-feature deep learning for atomic and defect identification in Scanning Tunneling Microscopy on H-Si(100) 2x1 surface

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.

cond-mat.mtrl-sci↗
arXiv2020

Reconstruction of the lattice Hamiltonian models from the observations of microscopic degrees of freedom in the presence of competing interactions

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.

cond-mat.stat-mech↗
arXiv2020

Dynamic manipulation in piezoresponse force microscopy: creating non-equilibrium phases with large electromechanical response

Domains walls and topological defects in ferroelectric materials have emerged as a powerful new paradigm for functional electronic devices including memory and logic. Similarly, wall interactions and dynamics underpin a broad range of mesoscale phenomena ranging from giant electromechanical responses to memory effects. Exploring the functionalities of individual domain walls, their interactions, and controlled modifications of the domain structures is crucial for applications and fundamental physical studies. However, the dynamic nature of these features severely limits studies of their local physics since application of local biases or pressures in piezoresponse force microscopy induce wall displacement as a primary response. Here, we introduce a fundamentally new approach for the control and modification of domain structures based on automated experimentation whereby real space image-based feedback is used to control the tip bias during ferroelectric switching, allowing for modification routes conditioned on domain states under the tip. This automated experiment approach is demonstrated for the exploration of domain wall dynamics and creation of metastable phases with large electromechanical response.

cond-mat.mes-hall↗
arXiv2019

Density Functional Theory and Deep-learning to Accelerate Data Analytics in Scanning Tunneling Microscopy

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.

cond-mat.mtrl-sci↗
arXiv2019

Piezoresponse phase as variable in electromechanical characterization

Piezoresponse force microscopy (PFM) is a powerful characterization technique to readily image and manipulate ferroelectrics domains. PFM gives insight into the strength of local piezoelectric coupling as well as polarization direction through PFM amplitude and phase, respectively. Converting measured arbitrary units to physical material parameters, however, remains a challenge. While much effort has been spent on quantifying the PFM amplitude signal, little attention has been given to the PFM phase and it is often arbitrarily adjusted to fit expectations or processed as recorded. This is problematic when investigating materials with unknown or potentially negative sign of the probed effective electrostrictive coefficient or strong frequency dispersion of electromechanical responses since assumptions about the phase cannot be reliably made. The PFM phase can, however, provide important information on the polarization orientation and the sign of the electrostrictive coefficient. 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 has not been correctly processed. Here, we demonstrate a path to identify the phase offset required to extract correct meaning from 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 phase. The proposed procedures are verified on two materials with positive and negative piezoelectric coefficients.

physics.app-ph↗
arXiv2019

Imaging Mechanism for Hyperspectral Scanning Probe Microscopy via Gaussian Process Modelling

We investigate the ability to reconstruct and derive spatial structure from sparsely sampled 3D piezoresponse force microcopy data, captured using the band-excitation (BE) technique, via Gaussian Process (GP) methods. Even for weakly informative priors, GP methods allow unambiguous determination of the characteristic length scales of the imaging process both in spatial and frequency domains. We further show that BE data set tends to be oversampled, with ~30% of the original data set sufficient for high-quality reconstruction, potentially enabling the faster BE imaging. Finally, we discuss how the GP can be used for automated experimentation in SPM, by combining GP regression with non-rectangular scans. The full code for GP regression applied to hyperspectral data is available at https://git.io/JePGr.

physics.comp-ph↗
arXiv2019

Phase Diagrams of Single Layer Two-Dimensional Transition Metal Dichalcogenides: Landau Theory

Single layer (SL) two-dimensional transition metal dichalcogenides (TMDs), such as MoS2, ReS2, WSe2, and MoTe2 have now become the focus of intensive fundamental and applied researches due to their intriguing and tunable physical properties. These materials exhibit a broad range of structural phases that can be induced via elastic strain, chemical doping, and electrostatic field effect. These transitions in turn can open and close the band gap of SL-TMDs, leading to metal-insulator transitions, and lead to emergence of more complex quantum phenomena. These considerations necessitate detailed understanding of the mesoscopic mechanisms of these structural phase transitions. Here we develop the Landau-type thermodynamic description of SL-TMDs on example of SL-(MoS2)1-x-(ReS2)x system and analyze the free energy surfaces, phase diagrams, and order parameter behavior. Our results predict the existence of multiple structural phases with 2-, 6- and 12-fold degenerated energy minima for in-plane and out of plane order parameters. This analysis suggests that out-of-plane ferroelectricity can exist in many of these phases, with the switchable polarization being proportional to the out-of-plane order parameter. We further predict that the domain walls in SL-(MoS2)1-x-(ReS2)x should become conductive above a certain strain threshold.

cond-mat.mtrl-sci↗
arXiv2019

Melting of Spatially Modulated Phases in La-doped BiFeO3 at Surfaces and Surface-Domain Wall Junctions

The interplay between the surface and domain wall phenomena in multiferroic LaxBi1-xFeO3 in the vicinity of morphotropic phase transition is explored on the atomic level. Scanning Transmission Electron Microscopy (STEM) has enabled mapping of atomic structures of the material with picometer-level precision, providing direct insight into the spatial distribution of the order parameters in this material and their behavior at surfaces and interfaces. Here, we use the thermodynamic Landau-Ginzburg-Devonshire (LGD) approach to explain the emergence of spatially modulated phases (SMP) in La0.22Bi0.78FeO3 films, and establish that the change of polarization gradient coefficients caused by La-doping is the primary driving mechanisms. The suppression, or "melting", of the SMP in the vicinity of the domain wall surface junction is observed experimentally and simulated in the framework of LGD theory. The melting originated from the system tendency to minimize electrostatic energy caused by long-range stray electric fields outside the film and related depolarization effects inside it. The observed behavior provides insight to the origin of surface and interface behaviors in multiferroics.

cond-mat.mtrl-sci↗
arXiv2019

Inversion of lattice models from the observations of microscopic degrees of freedom: parameter estimation with uncertainty quantification

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.

cond-mat.stat-mech↗
arXiv2019

Structure retrieval from 4D-STEM: statistical analysis of potential pitfalls in high-dimensional data

Four-dimensional scanning transmission electron microscopy (4D-STEM) is one of the most rapidly growing modes of electron microscopy imaging. The advent of fast pixelated cameras and the associated data infrastructure have greatly accelerated this process. Yet conversion of the 4D datasets into physically meaningful structure images in real-space remains an open issue. In this work, we demonstrate that, it is possible to systematically create filters that will affect the apparent resolution or even qualitative features of the real-space structure image, reconstructing artificially generated patterns. As initial efforts, we explore statistical model selection algorithms, aiming for robustness and reliability of estimated filters. This statistical model selection analysis demonstrates the need for regularization and cross-validation of inversion methods to robustly recover structure from high-dimensional diffraction datasets.

stat.AP↗
arXiv2019

Competing Phases in Epitaxial Vanadium Dioxide at Nanoscale

Phase competition in correlated oxides offers tantalizing opportunities as many intriguing physical phenomena occur near the phase transitions. Owing to a sharp metal-insulator transition (MIT) near room temperature, correlated vanadium dioxide (VO2) exhibits a strong competition between insulating and metallic phases that is important for practical applications. However, the phase boundary undergoes strong modification when strain is involved, yielding complex phase transitions. Here, we report the emergence of the nanoscale M2 phase domains in VO2 epitaxial films under anisotropic strain relaxation. The phase states of the films are imaged by multi-length-scale probes, detecting the structural and electrical properties in individual local domains. Competing evolution of the M1 and M2 phases indicates a critical role of lattice-strain on both the stability of the M2 Mott phase and the energetics of the MIT in VO2 films. This study demonstrates how strain engineering can be utilized to design phase states, which allow deliberate control of MIT behavior at the nanoscale in epitaxial VO2 films.

cond-mat.str-el↗
arXiv2019

Self-Assembled Room Temperature Multiferroic BiFeO3-LiFe5O8 Nanocomposites

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.

cond-mat.mtrl-sci↗
arXiv2019

Order and randomness in dopant distributions: exploring the thermodynamics of solid solutions from atomically resolved imaging

Exploration of structure-property relationships as a function of dopant concentration is commonly based on mean field theories for solid solutions. However, such theories that work well for semiconductors tend to fail in materials with strong correlations, either in electronic behavior or chemical segregation. In these cases, the details of atomic arrangements are generally not explored and analyzed. The knowledge of the generative physics and chemistry of the material can obviate this problem, since defect configuration libraries as stochastic representation of atomic level structures can be generated, or parameters of mesoscopic thermodynamic models can be derived. To obtain such information for improved predictions, we use data from atomically resolved microscopic images that visualize complex structural correlations within the system and translate them into statistical mechanical models of structure formation. Given the significant uncertainties about the microscopic aspects of the material's processing history along with the limited number of available images, we combine model optimization techniques with the principles of statistical hypothesis testing. We demonstrate the approach on data from a series of atomically-resolved scanning transmission electron microscopy images of Mo$_x$Re$_{1-x}$S$_2$ at varying ratios of Mo/Re stoichiometries, for which we propose an effective interaction model that is then used to generate atomic configurations and make testable predictions at a range of concentrations and formation temperatures.

cond-mat.mtrl-sci↗
arXiv2019

Building Free Energy Functional from Atomically-Resolved Imaging: Atomic Scale Phenomena in La-doped BiFeO3

Scanning Transmission Electron Microscopy (STEM) has enabled mapping of atomic structures of solids with sub-pm precision, providing insight to the physics of ferroic phenomena and chemical expansion. However, only a subset of information is available, due to projective nature of imaging in the beam direction. Correspondingly, the analysis often relies on the postulated known form of macroscopic Landau-Ginzburg energy, and some predefined relationship between experimentally determined atomic coordinates and the order parameter field. Here, we propose an approach for exploring the structure of ferroics using reduced order parameter models constructed based on experimental data only. We develop a four sublattices model (FSM) for the analytical description of A-cation displacement in (anti)ferroelectric-antiferrodistortive perovskites of ABO3-type. The model describes the displacements of cation A in four neighboring unit cells and determines the conditions of different structural phases appearance and stability in ABO3. We show that FSM explains the coexistence of rhombohedral (R), orthorhombic (O) and spatially modulated (SM) phases, observed by atomic-resolution STEM in La-doped BiFeO3. Using this approach, we atomically resolve and theoretically model the sublattice asymmetry inherent to the case of the A-site La/Bi cation sublattice in LaxBi1-xFeO3 polymorphs. This approach allows exploring the ferroics behaviors from experimental data only, without additional assumptions on the nature of the order parameter.

cond-mat.mtrl-sci↗
arXiv2019

USID and Pycroscopy -- Open frameworks for storing and analyzing spectroscopic and imaging data

Materials science is undergoing profound changes due to advances in characterization instrumentation that have resulted in an explosion of data in terms of volume, velocity, variety and complexity. Harnessing these data for scientific research requires an evolution of the associated computing and data infrastructure, bridging scientific instrumentation with super- and cloud- computing. Here, we describe Universal Spectroscopy and Imaging Data (USID), a data model capable of representing data from most common instruments, modalities, dimensionalities, and sizes. We pair this schema with the hierarchical data file format (HDF5) to maximize compatibility, exchangeability, traceability, and reproducibility. We discuss a family of community-driven, open-source, and free python software packages for storing, processing and visualizing data. The first is pyUSID which provides the tools to read and write USID HDF5 files in addition to a scalable framework for parallelizing data analysis. The second is Pycroscopy, which provides algorithms for scientific analysis of nanoscale imaging and spectroscopy modalities and is built on top of pyUSID and USID. The instrument-agnostic nature of USID facilitates the development of analysis code independent of instrumentation and task in Pycroscopy which in turn can bring scientific communities together and break down barriers in the age of open-science. The interested reader is encouraged to be a part of this ongoing community-driven effort to collectively accelerate materials research and discovery through the realms of big data.

physics.data-an↗
arXiv2019

Building and exploring libraries of atomic defects in graphene: scanning transmission electron and scanning tunneling microscopy study

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.

cond-mat.mtrl-sci↗
arXiv2019

Giant negative electrostriction and dielectric tunability in a van der Waals layered ferroelectric

The interest in ferroelectric van der Waals crystals arises from the potential to realize ultrathin ferroic systems owing to the reduced surface energy of these materials and the layered structure that allows for exfoliation. Here, we quantitatively unravel giant negative electrostriction of van der Waals layered copper indium thiophosphate (CIPS), which exhibits an electrostrictive coefficient Q33 as high as -3.2 m4/C2 and a resulting bulk piezoelectric coefficient d33 up to -85 pm/V. As a result, the electromechanical response of CIPS is comparable in magnitude to established perovskite ferroelectrics despite possessing a much smaller spontaneous polarization of only a few uC/cm2. In the paraelectric state, readily accessible owing to low transition temperatures, CIPS exhibits large dielectric tunability, similar to widely-used barium strontium titanate, and as a result both giant and continuously tunable electromechanical response. The persistence of electrostrictive and tunable responses in the paraelectric state indicates that even few layer films or nanoparticles will sustain significant electromechanical functionality, offsetting the inevitable suppression of ferroelectric properties in the nanoscale limit. These findings can likely be extended to other ferroelectric transition metal thiophosphates and (quasi-) two-dimensional materials and might facilitate the quest towards novel ultrathin functional devices incorporating electromechanical response.

cond-mat.mtrl-sci↗