Direct probe of interplay between local structure and superconductivity in FeTe₀.₅₅Se₀.₄₅

Hidden structures: a driving factor to achieve low thermal conductivity and high thermoelectric performance

The long-range periodic atomic arrangement or the lack thereof in solids typically dictates the magnitude and temperature dependence of their lattice thermal conductivity (κlat). Compared to crystalline materials, glasses exhibit a much-suppressed κlat across all temperatures as the phonon mean free path reaches parity with the interatomic distances therein. While the occurrence of such glass-like thermal transport in crystalline solids captivates the scientific community with its fundamental inquiry, it also holds the potential for profoundly impacting the field of thermoelectric energy conversion. Therefore, efficient manipulation of thermal transport and comprehension of the microscopic mechanisms dictating phonon scattering in crystalline solids are paramount. As quantized lattice vibrations (i.e., phonons) drive κlat, atomistic insights into the chemical bonding characteristics are crucial to have informed knowledge about their origins. Recently, it has been observed that within the highly symmetric 'averaged' crystal structures, often there are hidden locally asymmetric atomic motifs (within a few Å), which exert far-reaching influence on phonon transport. Phenomena such as local atomic off-centering, atomic rattling or tunneling, liquid-like atomic motion, site splitting, local ordering, etc., which arise within a few Å scales, are generally found to drastically disrupt the passage of heat carrying phonons. Despite their profound implication(s) for phonon dynamics, they are often overlooked by traditional crystallographic techniques. In this review, we provide a brief overview of the fundamental aspects of heat transport and explore the status quo of innately low thermally conductive crystalline solids, wherein the phonon dynamics is majorly governed by local structural phenomena. We also discuss advanced techniques capable of characterizing the crystal structure at the sub-atomic level. Subsequently, we delve into the emergent new ideas with examples linked to local crystal structure and lattice dynamics. While discussing the implications of the local structure for thermal conductivity, we provide the state-of-the-art examples of high-performance thermoelectric materials. Finally, we offer our viewpoint on the experimental and theoretical challenges, potential new paths, and the integration of novel strategies with material synthesis to achieve low κlat and realize high thermoelectric performance in crystalline solids via local structure designing.

Discrete energy levels of Caroli-de Gennes-Matricon states in quantum limit in FeTe0.55Se0.45

Caroli-de Gennes-Matricon (CdGM) states were predicted in 1964 as low-energy excitations within vortex cores of type-II superconductors. In the quantum limit, the energy levels of these states were predicted to be discrete with the basic levels at ±μΔ2/EF (μ = 1/2, 3/2, 5/2, …) with Δ the superconducting energy gap and EF the Fermi energy. However, due to the small ratio of Δ/EF in most type-II superconductors, it is very difficult to observe the discrete CdGM states, but rather a symmetric peak which appears at zero bias at the vortex center. Here we report the clear observation of these discrete energy levels of CdGM states in FeTe0.55Se0.45. The rather stable energies of these bound state peaks vs. space clearly validate our conclusion. Analysis based on the energies of these CdGM states indicates that the Fermi energy in the present system is very small.

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 FeSe_xTe_1-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 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.

Big-deep-smart data in imaging for guiding materials design

Harnessing big data, deep data, and smart data from state-of-the-art imaging might accelerate the design and realization of advanced functional materials. Here we discuss new opportunities in materials design enabled by the availability of big data in imaging and data analytics approaches, including their limitations, in material systems of practical interest. We specifically focus on how these tools might help realize new discoveries in a timely manner. Such methodologies are particularly appropriate to explore in light of continued improvements in atomistic imaging, modelling and data analytics methods.

An automatic method for atom identification in scanning tunnelling microscopy images of Fe-chalcogenide superconductors

We describe a computational approach for the automatic recognition and classification of atomic species in scanning tunnelling microscopy images. The approach is based on a pipeline of image processing methods in which the classification step is performed by means of a Fuzzy Clustering algorithm. As a representative example, we use the computational tool to characterize the nanoscale phase separation in thin films of the Fe-chalcogenide superconductor FeSex Te1-x , starting from synthetic data sets and experimental topographies. We quantify the stoichiometry fluctuations on length scales from tens to a few nanometres.

The nanoscale structure and unoccupied valence electronic states in FeSe1-xTex chalcogenides probed by X-ray absorption measurements

We have studied the nanoscale structure and unoccupied electronic states in FeSe1-xTex by a combined analysis of Se K, Te L1 and Fe K-edges X-ray absorption measurements. Extended X-ray absorption fine structure (EXAFS) results show that iron-chalcogen (Fe-Se and Fe-Te) distances in ternary FeSe1-xTex are similar to those measured for binary FeSe and FeTe. The local Fe-Se/Te distances determined by different absorption edges fit well in the characteristic Z-plot of random alloys, providing unambiguous support to the inhomogeneous nanoscale structure of the ternary FeSe1-xTex system. X-ray absorption near-edge structure (XANES) spectra reveal a gradual evolution of the unoccupied valence electronic states as a function of Te-substitution in FeSe1-xTex. The Fe 3d-Se 4p/Te 5p hybridization is found to decrease with Te-substitution, accompanied by an increase in unoccupied Se 4p states and a decrease in unoccupied Te 5p states. The results are discussed in the frame of local inhomogeneity in the FeSe1-xTex system driven by random alloying of Se/Te atoms.

Local crystallography analysis for atomically resolved scanning tunneling microscopy images

Scanning probe microscopy has emerged as a powerful and flexible tool for atomically resolved imaging of surface structures. However, due to the amount of information extracted, in many cases the interpretation of such data is limited to being qualitative and semi-quantitative in nature. At the same time, much can be learned from local atom parameters, such as distances and angles, that can be analyzed and interpreted as variations of local chemical bonding, or order parameter fields. Here, we demonstrate an iterative algorithm for indexing and determining atomic positions that allows the analysis of inhomogeneous surfaces. This approach is further illustrated by local crystallographic analysis of several real surfaces, including highly ordered pyrolytic graphite and an Fe-based superconductor FeTe0.55Se0.45. This study provides a new pathway to extract and quantify local properties for scanning probe microscopy images.