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.

Momentum transfer resolved electron correlation microscopy.

Electron correlation microscopy (ECM) characterizes local structural relaxation dynamics in fluctuating systems like supercooled liquids with nanometer spatial resolution. We have developed a new type of ECM technique that provides moderate resolution in momentum transfer or k space using five-dimensional scanning transmission electron microscopy. k-resolved ECM on a Pt57.5Cu14.7Ni5.3P22.5 metallic supercooled liquids measures rich spatial and momentum structure in the relaxation time data τ(r,k). Relaxation time maps τ(r) at each azimuthal k are independent samples of the material's underlying relaxation time distribution, and τ of radial k shows more complex behavior than the de Gennes narrowing observed in analogous X-ray experiments. We have determined the requirements for electron counts per k-pixel, number of k-pixels per speckle, and time sampling to obtain reliable k-resolved ECM data.

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.

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.

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.

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.

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.

Large Area, High Resolution Mapping of Approximate Rotational Symmetries in a Pd77.5Cu6Si16.5 Metallic Glass Thin Film.

Densely spaced four-dimensional scanning transmission electron microscopy (4D STEM) analyzed using correlation symmetry coefficients enables large area mapping of approximate rotational symmetries in amorphous materials. Here, we report the effects of Poisson noise, limited electron counts, probe coherence, reciprocal space sampling, and the probe-sample interaction volume on 4D STEM symmetry mapping experiments. These results lead to an experiment parameter envelope for high quality, high confidence 4D STEM symmetry mapping. We also establish a direct link between the symmetry coefficients and approximate rotational symmetries of nearest-neighbor atomic clusters using electron diffraction simulations from atomic models of a metallic glass. Experiments on a Pd77.5Cu6Si16.5 metallic glass thin film demonstrate the ability to image the types, sizes, volume fractions, and spatial correlations amongst local rotationally symmetry regions in the glass.

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.

Correlation symmetry analysis of electron nanodiffraction from amorphous materials.

Angular symmetry in diffraction reflects rotational symmetry in the sample. We introduce the angular symmetry coefficient as a method to extract local symmetry information from electron nanodiffraction patterns of amorphous materials. Symmetry coefficients are the average of the angular autocorrelation function at the characteristic angles of a particular rotational symmetry. The symmetry coefficients avoid non-structural features arising from Fourier transformation and Friedel symmetry breakdown that affect the angular power spectrum approach to determining angular symmetries in amorphous nanodiffraction. Both methods require thin samples to avoid overlapping diffraction from clusters of atoms separated in the thickness of the sample, but symmetry coefficients are more forgiving. Electron nanodiffraction experiments on a Pd-based metallic glass sample demonstrate both potentially misleading information in angular power spectrum and the utility of symmetry coefficients.

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.

Fast Surface Dynamics on a Metallic Glass Nanowire.

The dynamics near the surface of glasses can be much faster than in the bulk. We studied the surface dynamics of a Pt-based metallic glass using electron correlation microscopy with sub-nanometer resolution. Our studies show an ∼20 K suppression of the glass transition temperature at the surface. The enhancement in surface dynamics is suppressed by coating the metallic glass with a thin layer of amorphous carbon. Parallel molecular dynamics simulations on Ni80P20 show a similar temperature suppression of the surface glass transition temperature and that the enhanced surface dynamics are arrested by a capping layer that chemically binds to the glass surface. Mobility in the near-surface region occurs via atomic caging and hopping, with a strong correlation between slow dynamics and high cage-breaking barriers and stringlike cooperative motion. Surface and bulk dynamics collapse together as a function of temperature rescaled by their respective glass transition temperatures.

Epitaxy, exfoliation, and strain-induced magnetism in rippled Heusler membranes.

Single-crystalline membranes of functional materials enable the tuning of properties via extreme strain states; however, conventional routes for producing membranes require the use of sacrificial layers and chemical etchants, which can both damage the membrane and limit the ability to make them ultrathin. Here we demonstrate the epitaxial growth of the cubic Heusler compound GdPtSb on graphene-terminated Al2O3 substrates. Despite the presence of the graphene interlayer, the Heusler films have epitaxial registry to the underlying sapphire, as revealed by x-ray diffraction, reflection high energy electron diffraction, and transmission electron microscopy. The weak Van der Waals interactions of graphene enable mechanical exfoliation to yield free-standing GdPtSb membranes, which form ripples when transferred to a flexible polymer handle. Whereas unstrained GdPtSb is antiferromagnetic, measurements on rippled membranes show a spontaneous magnetic moment at room temperature, with a saturation magnetization of 5.2 bohr magneton per Gd. First-principles calculations show that the coupling to homogeneous strain is too small to induce ferromagnetism, suggesting a dominant role for strain gradients. Our membranes provide a novel platform for tuning the magnetic properties of intermetallic compounds via strain (piezomagnetism and magnetostriction) and strain gradients (flexomagnetism).

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.

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.