Room-Temperature Ferromagnetism in Epitaxial Bilayer FeSb/SrTiO3(001) Terminated with a Kagome Lattice.

Two-dimensional (2D) magnets exhibit unique physical properties for potential applications in spintronics. To date, most 2D ferromagnets are obtained by mechanical exfoliation of bulk materials with van der Waals interlayer interactions, and the synthesis of single- or few-layer 2D ferromagnets with strong interlayer coupling remains experimentally challenging. Here, we report the epitaxial growth of 2D non-van der Waals ferromagnetic bilayer FeSb on SrTiO3(001) substrates stabilized by strong coupling to the substrate, which exhibits in-plane magnetic anisotropy and a Curie temperature above 390 K. In situ low-temperature scanning tunneling microscopy/spectroscopy and density-functional theory calculations further reveal that an Fe Kagome layer terminates the bilayer FeSb. Our results open a new avenue for further exploring emergent quantum phenomena from the interplay of ferromagnetism and topology for application in spintronics.

Using 4D STEM to Probe Mesoscale Order in Molecular Glass Films Prepared by Physical Vapor Deposition.

Physical vapor deposition can be used to prepare highly stable organic glass systems where the molecules show orientational and translational ordering at the nanoscale. We have used low-dose four-dimensional scanning transmission electron microscopy (4D STEM), enabled by a fast direct electron detector, to map columnar order in glassy samples of a discotic mesogen using a 2 nm probe. Both vapor-deposited and liquid-cooled glassy films show domains of similar orientation, but their size varies from tens to hundreds of nanometers, depending on processing. Domain sizes are consistent with surface-diffusion-mediated ordering during film deposition. These results demonstrate the ability of low-dose 4D STEM to characterize a mesoscale structure in a molecular glass system which may be relevant to organic electronics.

Vapor-to-glass preparation of biaxially aligned organic semiconductors.

Physical vapor deposition (PVD) provides a route to prepare highly stable and anisotropic organic glasses that are utilized in multi-layer structures such as organic light-emitting devices. While previous work has demonstrated that anisotropic glasses with uniaxial symmetry can be prepared by PVD, here, we prepare biaxially aligned glasses in which molecular orientation has a preferred in-plane direction. With the collective effect of the surface equilibration mechanism and template growth on an aligned substrate, macroscopic biaxial alignment is achieved in depositions as much as 180 K below the clearing point TLC-iso (and 50 K below the glass transition temperature Tg) with single-component disk-like (phenanthroperylene ester) and rod-like (itraconazole) mesogens. The preparation of biaxially aligned organic semiconductors adds a new dimension of structural control for vapor-deposited glasses and may enable polarized emission and in-plane control of charge mobility.

Nucleation kinetics model for primary crystallization in Al-Y-Fe metallic glass.

The high density of aluminum nanocrystals (>1021 m-3) that develop during the primary crystallization in Al-based metallic glasses indicates a high nucleation rate (∼1018 m-3 s-1). Several studies have been advanced to account for the primary crystallization behavior, but none have been developed to completely describe the reaction kinetics. Recently, structural analysis by fluctuation electron microscopy has demonstrated the presence of the Al-like medium range order (MRO) regions as a spatial heterogeneity in as-spun Al88Y7Fe5 metallic glass that is representative for the class of Al-based amorphous alloys that develop Al nanocrystals during primary crystallization. From the structural characterization, an MRO seeded nucleation configuration is established, whereby the Al nanocrystals are catalyzed by the MRO core to decrease the nucleation barrier. The MRO seeded nucleation model and the kinetic data from the delay time (τ) measurement provide a full accounting of the evolution of the Al nanocrystal density (Nv) during the primary crystallization under isothermal annealing treatments. Moreover, the calculated values of the steady state nucleation rates (Jss) predicted by the nucleation model agree with the experimental results. Moreover, the model satisfies constraints on the structural, thermodynamic, and kinetic parameters, such as the critical nucleus size, the interface energy, and the volume-free energy driving force that are essential for a fully self-consistent nucleation kinetics analysis. The nucleation kinetics model can be applied more broadly to materials that are characterized by the presence of spatial heterogeneities.

Benchmark Tests of Atom Segmentation Deep Learning Models with a Consistent Dataset.

The information content of atomic-resolution scanning transmission electron microscopy (STEM) images can often be reduced to a handful of parameters describing each atomic column, chief among which is the column position. Neural networks (NNs) are high performance, computationally efficient methods to automatically locate atomic columns in images, which has led to a profusion of NN models and associated training datasets. We have developed a benchmark dataset of simulated and experimental STEM images and used it to evaluate the performance of two sets of recent NN models for atom location in STEM images. Both models exhibit high performance for images of varying quality from several different crystal lattices. However, there are important differences in performance as a function of image quality, and both models perform poorly for images outside the training data, such as interfaces with large difference in background intensity. Both the benchmark dataset and the models are available using the Foundry service for dissemination, discovery, and reuse of machine learning models.

Deep Learning Approach for High-accuracy Electron Counting of Monolithic Active Pixel Sensor-type Direct Electron Detectors at Increased Electron Dose.

Electron counting can be performed algorithmically for monolithic active pixel sensor direct electron detectors to eliminate readout noise and Landau noise arising from the variability in the amount of deposited energy for each electron. Errors in existing counting algorithms include mistakenly counting a multielectron strike as a single electron event, and inaccurately locating the incident position of the electron due to lateral spread of deposited energy and dark noise. Here, we report a supervised deep learning (DL) approach based on Faster region-based convolutional neural network (R-CNN) to recognize single electron events at varying electron doses and voltages. The DL approach shows high accuracy according to the near-ideal modulation transfer function (MTF) and detector quantum efficiency for sparse images. It predicts, on average, 0.47 pixel deviation from the incident positions for 200 kV electrons versus 0.59 pixel using the conventional counting method. The DL approach also shows better robustness against coincidence loss as the electron dose increases, maintaining the MTF at half Nyquist frequency above 0.83 as the electron density increases to 0.06 e-/pixel. Thus, the DL model extends the advantages of counting analysis to higher dose rates than conventional methods.

Surface Diffusion Is Controlled by Bulk Fragility across All Glass Types.

Surface diffusion is vastly faster than bulk diffusion in some glasses, but only moderately enhanced in others. We show that this variation is closely linked to bulk fragility, a common measure of how quickly dynamics is excited when a glass is heated to become a liquid. In fragile molecular glasses, surface diffusion can be a factor of 10^{8} faster than bulk diffusion at the glass transition temperature, while in the strong system SiO_{2}, the enhancement is a factor of 10. Between these two extremes lie systems of intermediate fragility, including metallic glasses and amorphous selenium and silicon. This indicates that stronger liquids have greater resistance to dynamic excitation from bulk to surface and enables prediction of surface diffusion, surface crystallization, and formation of stable glasses by vapor deposition.

Radiation-induced segregation in a ceramic.

Radiation-induced segregation is well known in metals, but has been rarely studied in ceramics. We discover that radiation can induce notable segregation of one of the constituent elements to grain boundaries in a ceramic, despite the fact that the ceramic forms a line compound and therefore has a strong thermodynamic driving force to resist off-stoichiometry. Specifically, irradiation of silicon carbide at 300 °C leads to carbon enrichment near grain boundaries, whereas the enrichment diminishes for irradiation at 600 °C. The temperature dependence of this radiation-induced segregation is different from that shown in metallic systems. Using an ab initio informed rate theory model, we demonstrate that this difference is introduced by the unique defect energy landscapes present in the covalent system. Additionally, we discover that grain boundaries in unirradiated silicon carbide grown by chemical vapour deposition are intrinsically carbon-depleted. The inherent grain boundary chemistry and its evolution under radiation are both critical for understanding the many properties of ceramics associated with grain boundaries.

Factors correlating to enhanced surface diffusion in metallic glasses.

The enhancement of surface diffusion (DS) over the bulk (DV) in metallic glasses (MGs) is well documented and likely to strongly influence the properties of glasses grown by vapor deposition. Here, we use classical molecular dynamics (MD) simulations to identify different factors influencing the enhancement of surface diffusion in MGs. MGs have a simple atomic structure and belong to the category of moderately fragile glasses that undergo pronounced slowdown of bulk dynamics with cooling close to the glass transition temperature (Tg). We observe that DS exhibits a much more moderate slowdown compared to DV when approaching Tg, and DS/DV at Tg varies by two orders of magnitude among the MGs investigated. We demonstrate that both the surface energy and the fraction of missing bonds for surface atoms show good correlation to DS/DV, implying that the loss of nearest neighbors at the surface directly translates into higher mobility, unlike the behavior of network-bonded and hydrogen-bonded organic glasses. Fragility, a measure of the slowdown of bulk dynamics close to Tg, also correlates to DS/DV, with more fragile systems having larger surface enhancement of mobility. The deviations observed in the fragility-DS/DV relationship are shown to be correlated to the extent of segregation or depletion of the mobile element at the surface. Finally, we explore the relationship between the diffusion pre-exponential factor (D0) and the activation energy (Q) and compare it to a ln(D0)-Q correlation previously established for bulk glasses, demonstrating similar correlations from MD as in the experiments and that the surface and bulk have very similar ln(D0)-Q correlations.

Graph network based deep learning of bandgaps.

Recent machine learning models for bandgap prediction that explicitly encode the structure information to the model feature set significantly improve the model accuracy compared to both traditional machine learning and non-graph-based deep learning methods. The ongoing rapid growth of open-access bandgap databases can benefit such model construction not only by expanding their domain of applicability but also by requiring constant updating of the model. Here, we build a new state-of-the-art multi-fidelity graph network model for bandgap prediction of crystalline compounds from a large bandgap database of experimental and density functional theory (DFT) computed bandgaps with over 806 600 entries (1500 experimental, 775 700 low-fidelity DFT, and 29 400 high-fidelity DFT). The model predicts bandgaps with a 0.23 eV mean absolute error in cross validation for high-fidelity data, and including the mixed data from all different fidelities improves the prediction of the high-fidelity data. The prediction error is smaller for high-symmetry crystals than for low symmetry crystals. Our data are published through a new cloud-based computing environment, called the "Foundry," which supports easy creation and revision of standardized data structures and will enable cloud accessible containerized models, allowing for continuous model development and data accumulation in the future.

Optimizing Nonrigid Registration for Scanning Transmission Electron Microscopy Image Series.

Achieving sub-picometer precision measurements of atomic column positions in high-resolution scanning transmission electron microscope images using nonrigid registration (NRR) and averaging of image series requires careful optimization of experimental conditions and the parameters of the registration algorithm. On experimental data from SrTiO3 [100], sub-pm precision requires alignment of the sample to the zone axis to within 1 mrad tilt and sample drift of less than 1 nm/min. At fixed total electron dose for the series, precision in the fast scan direction improves with shorter pixel dwell time to the limit of our microscope hardware, but the best precision along the slow scan direction occurs at 6 µs/px dwell time. Within the NRR algorithm, the "smoothness factor" that penalizes large estimated shifts is the most important parameter for sub-pm precision, but in general, the precision of NRR images is robust over a wide range of parameters.

Metastable Intermediates in Amorphous Titanium Oxide: A Hidden Role Leading to Ultra-Stable Photoanode Protection.

Metastable intermediates represent a non-equilibrium state of matter that may impose profound impacts to materials properties beyond our understandings of monolithic and equilibrium systems. Here, we report a discovery of hidden metastable intermediates in amorphous TiO2 thin films and their critical role in electrochemical damage. These intermediates have a non-bulk crystal-like structure and exhibit significantly higher electrical conductivity than both the amorphous and the crystalline phases. When these TiO2 films are applied to protect Si photoelectrochemical (PEC) photoanodes, the intermediates can induce localized high electrical currents that largely accelerate the etching of the TiO2 film and the Si electrode underneath. The intermediates can be effectively suppressed by raising their nucleation barrier via reducing the film thickness from 24 to 2.5 nm. The homogeneous amorphous TiO2-film-coated Si photoanodes achieved more than 500 h of PEC water oxidation at a steady photocurrent density of over 30 mA·cm-2.

Paths to Stabilizing Electronically Aberrant Compounds: A Defect-Stabilized Polymorph and Constrained Atomic Motion in PtGa2.

The structures and properties of intermetallic phases are intimately connected to electron count; unfavorable electron counts can result in structural rearrangements or new electrical or magnetic behavior when no such transformation is available. The compound PtGa2 appears to teeter on the border between these two scenarios with its two polymorphs: a cubic fluorite type form (c-PtGa2) and a complex tetragonal superstructure (t-PtGa2) whose Pt-Pt connectivity aligns with the 18- n electron counting rule. Here, we investigate the factors underlying this polymorphism. Electronic structure calculations show that the transition to t-PtGa2 opens a pseudogap at the Fermi energy that can be traced to Pt-Pt isolobal bond formation, in line with the 18- n bonding scheme. Conversely, DFT-chemical pressure (CP) analysis reveals a network of positive local pressures along Pt-Ga contacts, requiring that the c-PtGa2 to t-PtGa2 transition follows tightly concerted atomic motions. Experimentally, a series of samples with varying Pt:Ga ratios were synthesized to examine the stability ranges of the polymorphs. Ga-poor samples yield exclusively the cubic polymorph over the full range of temperatures studied, which can be correlated to the enhanced incorporation of interstitial Pt atoms (at points of negative pressure in the CP scheme). At more Ga-rich compositions, however, t-PtGa2 emerges as a low-temperature form. In these samples, the t-PtGa2 to c-PtGa2 transition is found to be reversible, but with a large hysteresis that in single crystals can exceed 100 °C. Together, the theoretical and experimental results indicate that the c-PtGa2 phase is buttressed at its unfavorable electron count by the interstitial atoms and networks of positive CPs that restrict atomic motion, suggesting more general strategies for achieving exotic electronic structures in intermetallic materials.

Atomic Layer Epitaxy of h-BN(0001) Multilayers on Co(0001) and Molecular Beam Epitaxy Growth of Graphene on h-BN(0001)/Co(0001).

The direct growth of hexagonal boron nitride (h-BN) by industrially scalable methods is of broad interest for spintronic and nanoelectronic device applications. Such applications often require atomically precise control of film thickness and azimuthal registry between layers and substrate. We report the formation, by atomic layer epitaxy (ALE), of multilayer h-BN(0001) films (up to 7 monolayers) on Co(0001). The ALE process employs BCl3/NH3 cycles at 600 K substrate temperature. X-ray photoelectron spectroscopy (XPS) and low energy electron diffraction (LEED) data show that this process yields an increase in h-BN average film thickness linearly proportional to the number of BCl3/NH3 cycles, with BN layers in azimuthal registry with each other and with the Co(0001) substrate. LEED diffraction spot profile data indicate an average BN domain size of at least 1900 Å. Optical microscopy data indicate the presence of some domains as large as ∼20 µm. Transmission electron microscopy (TEM) and ambient exposure studies demonstrate macroscopic and microscopic continuity of the h-BN film, with the h-BN film highly conformal to the Co substrate. Photoemission data show that the h-BN(0001) film is p-type, with band bending near the Co/h-BN interface. Growth of graphene by molecular beam epitaxy (MBE) is observed on the surface of multilayer h-BN(0001) at temperatures of 800 K. LEED data indicate azimuthal graphene alignment with the h-BN and Co(0001) lattices, with domain size similar to BN. The evidence of multilayer BN and graphene azimuthal alignment with the lattice of the Co(0001) substrate demonstrates that this procedure is suitable for scalable production of heterojunctions for spintronic applications.

Non-rigid registration and non-local principle component analysis to improve electron microscopy spectrum images.

Image registration and non-local Poisson principal component analysis (PCA) denoising improve the quality of characteristic x-ray (EDS) spectrum imaging of Ca-stabilized Nd2/3TiO3 acquired at atomic resolution in a scanning transmission electron microscope. Image registration based on the simultaneously acquired high angle annular dark field image significantly outperforms acquisition with a long pixel dwell time or drift correction using a reference image. Non-local Poisson PCA denoising reduces noise more strongly than conventional weighted PCA while preserving atomic structure more faithfully. The reliability of and optimal internal parameters for non-local Poisson PCA denoising of EDS spectrum images is assessed using tests on phantom data.

Patterning of sub-50 nm perpendicular CoFeB/MgO-based magnetic tunnel junctions.

Perpendicular magnetic tunnel junctions (p-MTJs) were patterned into nanopillars using electron-beam lithography to study their scaling and switching behaviour. Magnetoresistance measurements of annealed and unannealed p-MTJ films using scanning probe microscopy showed good agreement with Monte Carlo modeling. p-MTJ pillars demonstrated clear parallel magnetic states, both 'up' or both 'down' following AC-demagnetization. Significant variability in the resistance of p-MTJ pillars was observed and attributed to edge features generated during patterning or local inhomogeneity in the MgO layer.