arXiv subjects

Maxim Ziatdinov

Publications and source records attributed to Maxim Ziatdinov.

104 records · Page 6Linked to original sources

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

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

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

Nanoscale Interlayer Defects in Iron Arsenides

Using a local real-space microscopy probe, we discover evidence of nanoscale interlayer defects along the c-crystallographic direction in BaFe2As2 (122) based iron-arsenide superconductors. We find ordered 122 atomic arrangements within the ab-plane, and within regions of ~10 to 20 nm size perpendicular to this plane. While the FeAs substructure is very rigid, Ba ions are relatively weakly bound and can be displaced from the 122, forming stacking faults resulting in the physical separation of the 122 between adjacent ordered domains. The evidence for interlayer defects between the FeAs superconducting planes gives perspective on the minimal connection between interlayer chemical disorder and high-temperature superconductivity. In particular, the Cooper pairs may be finding a way around such localized interlayer defects through a percolative path of the ordered layered 122 lattice that may not affect Tc.

cond-mat.supr-con↗

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

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↗

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

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

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↗

arXiv2016

Deep data mining in a real space: Separation of intertwined electronic responses in a lightly-doped BaFe2As2

Electronic interactions present in material compositions close to the superconducting dome play a key role in the manifestation of high-Tc superconductivity. In many correlated electron systems, however, the parent or underdoped states exhibit strongly inhomogeneous electronic landscape at the nanoscale that may be associated with competing, coexisting, or intertwined chemical disorder, strain, magnetic, and structural order parameters. Here we demonstrate an approach based on a combination of scanning tunneling microscopy/spectroscopy (STM/S) and advanced statistical learning for an automatic separation and extraction of statistically significant electronic behaviors in the spin density wave (SDW) regime of a lightly (~1%) gold-doped BaFe2As2. We show that the decomposed STS spectral features have a direct relevance to fundamental physical properties of the system, such as SDW-induced gap, pseudogap-like state, and impurity resonance states.

cond-mat.mtrl-sci↗

arXiv2016

Data Mining Graphene: Correlative Analysis of Structure and Electronic Degrees of Freedom in Graphenic Monolayers with Defects

The link between changes in the material crystal structure and its mechanical, electronic, magnetic, and optical functionalities - known as the structure-property relationship - is the cornerstone of the contemporary materials science research. The recent advances in scanning transmission electron and scanning probe microscopies (STEM and SPM) have opened an unprecedented path towards examining the materials structure-property relationships on the single-impurity and atomic-configuration levels. Lacking, however, are the statistics-based approaches for cross-correlation of structure and property variables obtained in different information channels of the STEM and SPM experiments. Here we have designed an approach based on a combination of sliding window fast Fourier transform, Pearson correlation matrix, and linear and kernel canonical correlation, to study a relationship between lattice distortions and electron scattering from SPM data on graphene with defects. Our analysis revealed that the strength of coupling to strain is altered between different scattering channels, which can explain coexistence of several quasiparticle interference patterns in nanoscale regions of interest. In addition, the application of kernel functions allowed us to extract a non-linear component of the relationship between the lattice strain and scattering intensity in graphene. The outlined approach can be further utilized towards analyzing correlations in various multi-modal imaging techniques where the information of interest is spatially distributed and generally has a complex multidimensional nature.

cond-mat.mtrl-sci↗

arXiv2014

Direct imaging of monovacancy-hydrogen complexes in single graphitic layer

Understanding how foreign chemical species bond to atomic vacancies in graphene layers can advance our ability to tailor the electronic and magnetic properties of defective graphenic materials. Here we use ultra-high vacuum scanning tunneling microscopy (UHV-STM) and density functional theory to identify the precise structure of hydrogenated single atomic vacancies in a topmost graphene layer of graphite and establish a connection between the details of hydrogen passivation and the electronic properties of a single atomic vacancy. Monovacancy-hydrogen complexes are prepared by sputtering of the graphite surface layer with low energy ions and then exposing it briefly to an atomic hydrogen environment. High-resolution experimental UHV-STM imaging allows us to determine unambiguously the positions of single missing atoms in the defective graphene lattice and, in combination with the ab initio calculations, provides detailed information about the distribution of low-energy electronic states on the periphery of the monovacancy-hydrogen complexes. We found that a single atomic vacancy where each sigma-dangling bond is passivated with one hydrogen atom shows a well-defined signal from the non-bonding pi-state which penetrates into the bulk with a (\sqrt 3 \times \sqrt 3)R30^ \circ periodicity. However, a single atomic vacancy with full hydrogen termination of sigma-dangling bonds and additional hydrogen passivation of the extended pi-state at one of the vacancy's monohydrogenated carbon atoms is characterized by complete quenching of low-energy localized states. In addition, we discuss the migration of hydrogen atoms at the periphery of the monovacancy-hydrogen complexes which dramatically change the vacancy's low-energy electronic properties, as observed in our low-bias high-resolution STM imaging.

cond-mat.mtrl-sci↗