Maxim Ziatdinov

Direct Imaging of Monovacancy-Hydrogen Complexes in a 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 vacancys 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 vacancys low-energy electronic properties, as observed in our low-bias high-resolution STM imaging.

Bowl Inversion and Electronic Switching of Buckybowls on Gold

Bowl-shaped π-conjugated compounds, or buckybowls, are a novel class of sp(2)-hybridized nanocarbon materials. In contrast to tubular carbon nanotubes and ball-shaped fullerenes, the buckybowls feature structural flexibility. Bowl-to-bowl structural inversion is one of the unique properties of the buckybowls in solutions. Bowl inversion on a surface modifies the metal-molecule interactions through bistable switching between bowl-up and bowl-down states on the surface, which makes surface-adsorbed buckybowls a relevant model system for elucidation of the mechano-electronic properties of nanocarbon materials. Here, we report a combination of scanning tunneling microscopy (STM) measurements and ab initio atomistic simulations to identify the adlayer structure of the sumanene buckybowl on Au(111) and reveal its unique bowl inversion behavior. We demonstrate that the bowl inversion can be induced by approaching the STM tip toward the molecule. By tuning the local metal-molecule interaction using the STM tip, the sumanene buckybowl exhibits structural bistability with a switching rate that is two orders of magnitude faster than that of the stochastic inversion process.

Atomic-scale observation of structural and electronic orders in the layered compound α-RuCl3

A pseudospin-1/2 Mott phase on a honeycomb lattice is proposed to host the celebrated two-dimensional Kitaev model which has an elusive quantum spin liquid ground state, and fascinating physics relevant to the development of future templates towards topological quantum bits. Here we report a comprehensive, atomically resolved real-space study by scanning transmission electron and scanning tunnelling microscopies on a novel layered material displaying Kitaev physics, α-RuCl3. Our local crystallography analysis reveals considerable variations in the geometry of the ligand sublattice in thin films of α-RuCl3 that opens a way to realization of a spatially inhomogeneous magnetic ground state at the nanometre length scale. Using scanning tunnelling techniques, we observe the electronic energy gap of ≈0.25 eV and intra-unit cell symmetry breaking of charge distribution in individual α-RuCl3 surface layer. The corresponding charge-ordered pattern has a fine structure associated with two different types of charge disproportionation at Cl-terminated surface.

Phases and Interfaces from Real Space Atomically Resolved Data: Physics-Based Deep Data Image Analysis.

Advances in electron and scanning probe microscopies have led to a wealth of atomically resolved structural and electronic data, often with ∼1-10 pm precision. However, knowledge generation from such data requires the development of a physics-based robust framework to link the observed structures to macroscopic chemical and physical descriptors, including single phase regions, order parameter fields, interfaces, and structural and topological defects. Here, we develop an approach based on a synergy of sliding window Fourier transform to capture the local analog of traditional structure factors combined with blind linear unmixing of the resultant 4D data set. This deep data analysis is ideally matched to the underlying physics of the problem and allows reconstruction of the a priori unknown structure factors of individual components and their spatial localization. We demonstrate the principles of this approach using a synthetic data set and further apply it for extracting chemical and physically relevant information from electron and scanning tunneling microscopy data. This method promises to dramatically speed up crystallographic analysis in atomically resolved data, paving the road toward automatic local structure-property determinations in crystalline and quasi-ordered systems, as well as systems with competing structural and electronic order parameters.

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 modern 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 structure-property relationships of materials at the single-impurity and atomic-configuration levels. However, there are no statistics-based approaches for cross-correlation of structure and property variables obtained from the different information channels of 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 methods to study the 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 the 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 used to analyze correlations in various multi-modal imaging techniques where the information of interest is spatially distributed and generally has a complex multi-dimensional nature.

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-T c 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 and advanced statistical learning for an automatic separation and extraction of statistically significant electronic behaviors in the spin density wave 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.

Learning surface molecular structures via machine vision

Recent advances in high resolution scanning transmission electron and scanning probe microscopies have allowed researchers to perform measurements of materials structural parameters and functional properties in real space with a picometre precision. In many technologically relevant atomic and/or molecular systems, however, the information of interest is distributed spatially in a non-uniform manner and may have a complex multi-dimensional nature. One of the critical issues, therefore, lies in being able to accurately identify (‘read out’) all the individual building blocks in different atomic/molecular architectures, as well as more complex patterns that these blocks may form, on a scale of hundreds and thousands of individual atomic/molecular units. Here we employ machine vision to read and recognize complex molecular assemblies on surfaces. Specifically, we combine Markov random field model and convolutional neural networks to classify structural and rotational states of all individual building blocks in molecular assembly on the metallic surface visualized in high-resolution scanning tunneling microscopy measurements. We show how the obtained full decoding of the system allows us to directly construct a pair density function—a centerpiece in analysis of disorder-property relationship paradigm—as well as to analyze spatial correlations between multiple order parameters at the nanoscale, and elucidate reaction pathway involving molecular conformation changes. The method represents a significant shift in our way of analyzing atomic and/or molecular resolved microscopic images and can be applied to variety of other microscopic measurements of structural, electronic, and magnetic orders in different condensed matter systems.Machine learning: Computers automatically decode complex molecular patternsComplex patterns formed by thousands of molecules on a surface can now be automatically recognized and classified by a computer. Sergei Kalinin, Maxim Ziatdinov and Artem Maksov at Oak Ridge National Lab have developed a ‘machine vision’ approach, adapting techniques already used in cancer detection and satellite imaging, to analyze up to 1000 s of individual molecular configurations from microscopic images of so-called ‘buckybowl’ molecules on gold surfaces. These bowl-shaped molecules may rest face up or down, and can adopt different rotational orientations, resulting in a rich tapestry of molecular patterns which are too complicated to sort manually or with existing computational methods. However, this machine vision approach revealed details of how buckybowls interact with their neighbors to build complex arrays, which could help in the design of molecular memory devices.

Identifying local structural states in atomic imaging by computer vision

The availability of atomically resolved imaging modalities enables an unprecedented view into the local structural states of materials, which manifest themselves by deviations from the fundamental assumptions of periodicity and symmetry. Consequently, approaches that aim to extract these local structural states from atomic imaging data with minimal assumptions regarding the average crystallographic configuration of a material are indispensable to advances in structural and chemical investigations of materials. Here, we present an approach to identify and classify local structural states that is rooted in computer vision. This approach introduces a definition of a structural state that is composed of both local and nonlocal information extracted from atomically resolved images, and is wholly untethered from the familiar concepts of symmetry and periodicity. Instead, this approach relies on computer vision techniques such as feature detection, and concepts such as scale invariance. We present the fundamental aspects of local structural state extraction and classification by application to simulated scanning transmission electron microscopy images, and analyze the robustness of this approach in the presence of common instrumental factors such as noise, limited spatial resolution, and weak contrast. Finally, we apply this computer vision-based approach for the unsupervised detection and classification of local structural states in an experimental electron micrograph of a complex oxides interface, and a scanning tunneling micrograph of a defect-engineered multilayer graphene surface.

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 this https URL). 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.

Deep data analysis via physically constrained linear unmixing: universal framework, domain examples, and a community-wide platform

Many spectral responses in materials science, physics, and chemistry experiments can be characterized as resulting from the superposition of a number of more basic individual spectra. In this context, unmixing is defined as the problem of determining the individual spectra, given measurements of multiple spectra that are spatially resolved across samples, as well as the determination of the corresponding abundance maps indicating the local weighting of each individual spectrum. Matrix factorization is a popular linear unmixing technique that considers that the mixture model between the individual spectra and the spatial maps is linear. Here, we present a tutorial paper targeted at domain scientists to introduce linear unmixing techniques, to facilitate greater understanding of spectroscopic imaging data. We detail a matrix factorization framework that can incorporate different domain information through various parameters of the matrix factorization method. We demonstrate many domain-specific examples to explain the expressivity of the matrix factorization framework and show how the appropriate use of domain-specific constraints such as non-negativity and sum-to-one abundance result in physically meaningful spectral decompositions that are more readily interpretable. Our aim is not only to explain the off-the-shelf available tools, but to add additional constraints when ready-made algorithms are unavailable for the task. All examples use the scalable open source implementation from https://github.com/ramkikannan/nmflibrary that can run from small laptops to supercomputers, creating a user-wide platform for rapid dissemination and adoption across scientific disciplines.