Artem Maksov

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.

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.

Direct atomic fabrication and dopant positioning in Si using electron beams with active real-time image-based feedback

Semiconductor fabrication is a mainstay of modern civilization, enabling the myriad applications and technologies that underpin everyday life. However, while sub-10 nanometer devices are already entering the mainstream, the end of the Moores law roadmap still lacks tools capable of bulk semiconductor fabrication on sub-nanometer and atomic levels, with probe-based manipulation being explored as the only known pathway. Here we demonstrate that the atomic-sized focused beam of a scanning transmission electron microscope can be used to manipulate semiconductors such as Si on the atomic level, inducing growth of crystalline Si from the amorphous phase, reentrant amorphization, milling, and dopant front motion. These phenomena are visualized in real-time with atomic resolution. We further implement active feedback control based on real-time image analytics to automatically control the e-beam motion, enabling shape control and providing a pathway for atom-by-atom correction of fabricated structures in the near future. These observations open a new epoch for atom-by-atom manufacturing in bulk, the long-held dream of nanotechnology.

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.

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.

Knowledge Extraction from Atomically Resolved Images

Tremendous strides in experimental capabilities of scanning transmission electron microscopy and scanning tunneling microscopy (STM) over the past 30 years made atomically resolved imaging routine. However, consistent integration and use of atomically resolved data with generative models is unavailable, so information on local thermodynamics and other microscopic driving forces encoded in the observed atomic configurations remains hidden. Here, we present a framework based on statistical distance minimization to consistently utilize the information available from atomic configurations obtained from an atomically resolved image and extract meaningful physical interaction parameters. We illustrate the applicability of the framework on an STM image of a FeSexTe1-x superconductor, with the segregation of the chalcogen atoms investigated using a nonideal interacting solid solution model. This universal method makes full use of the microscopic degrees of freedom sampled in an atomically resolved image and can be extended via Bayesian inference toward unbiased model selection with uncertainty quantification.

Deep Data Analytics in Structural and Functional Imaging of Nanoscale Materials

Recent advances in scanning probe microscopy and scanning transmission electron microscopy have opened unprecedented opportunities in probing the materials structural parameters and electronic properties in real space on a picometre-scale. At the same time, the ability of modern day microscopes to quickly produce large, high-resolution datasets has created a challenge for rapid physics-guided analysis of data that typically contain several hundreds to several thousand atomic or molecular units per image. Here it is demonstrated how the advanced statistical analysis and machine learning techniques can be used for extracting relevant physical and chemical information from microscope data on multiple functional materials. Specifically, the following three case studies are discussed (i) application of a combination of convolutional neural network and Markov model for analyzing positional and orientational order in molecular self-assembly; (ii) a combination of sliding window fast Fourier transform, Pearson correlation matrix and canonical correlation analysis methods to study the relationships between lattice distortions and electron scattering patterns in graphene; (iii) application of a non-negative matrix factorization with physics-based constraints and Moran’s analysis of spatial associations to extracting electronic responses linked to different types of structural domains from multi-modal imaging datasets on iron-based superconductors. The approaches demonstrated here are universal in nature and can be applied to a variety of microscopic measurements on different materials.

A Framework to Learn Physics from Atomically Resolved Images

1. Joint Institute for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge TN 2. Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge TN 3. Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge TN 4. Bredesen Center for Interdisciplinary Research, University of Tennessee, Knoxville, TN 5. School of Physics, Huazhong University of Science and Technology, Wuhan, China

Deep Data Mining in a Real Space: Application to Scanning Probe Microscopy Studies on a “Parent” State of a High Temperature Superconductor

Nanoscale inhomogeneity of structural and electronic orders in a crystalline matter can have a powerful and non-random effect on the macroscopic properties of technologically relevant materials. Scanning tunneling microscopy and spectroscopy (STM/S), which probes topographic and electronic properties of the surfaces with a nanometer-scale resolution, constitutes an important experimental tool for exploring local inhomogeneity in these materials. The STS mode allows to acquire 3D G(X,Y,U) datasets, where G=dI/dU corresponds to a value of a differential conductance proportional to LDOS at the specific energy E=eU at each (X,Y) point. Unfortunately, in many strongly correlated materials, such as hightemperature superconductors (HTSC), there is usually no direct and simple connection between behavior of STS curves and the surface topographic (“structural”) features. As the ever-increasing amount of STM/S data on strongly correlated systems makes the individual inspection of datasets highly impractical and, in many cases, nearly impossible, there is an urgent need for developing deep data mining tools for a reliable identification and spatial mapping of statistically significant electronic behaviors in an automated fashion of a full information extraction [1]. Here we present an approach based on k-means clustering, principle component analysis (PCA) and Bayesian linear unmixing (BLU) used to uncover “buried” electronic phases from the STS datasets of a parent magnetic state of ironpnictide HTSC.