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Hexagonal $ABC$ intermetallics are predicted to have tunable ferroelectric, topological, and magnetic properties as a function of the polar buckling of $BC$ atomic planes. We report the impact of isovalent lanthanide substitution on the buckling, structural phase transitions, and electronic and magnetic properties of Gd$_x$La$_{1-x}$PtSb films grown by molecular beam epitaxy (MBE) on c-plane sapphire substrates. The Gd$_x$La$_{1-x}$PtSb films form a solid solution from x = 0 to 1 and retain the polar hexagonal structure ($P6_3 mc$) out to $x \leq 0.95$. With increasing $x$, the PtSb buckling increases and the out of plane lattice constant $c$ decreases due to the lanthanide contraction. While hexagonal LaPtSb is a highly conductive polar metal, the carrier density decreases with $x$ until an abrupt phase transition to a zero band overlap semimetal is found for cubic GdPtSb at $x=1$. The magnetic susceptibility peaks at small but finite $x$, which we attribute to Ruderman Kittel Kasuya Yosida (RKKY) coupling between localized $4f$ moments, whose concentration increases with $x$, and free carriers that decrease with $x$. Samples with $x\geq 0.3$ show antiferromagnetic Curie-Weiss behavior and a Neel temperature that increases with $x$. The Gd$_x$La$_{1-x}$PtSb system provides opportunities to dramatically alter the polar buckling and concentration of local $4f$ moments, for tuning chiral spin textures and topological phases.

One compelling vision of the future of materials discovery and design involves the use of machine learning (ML) models to predict materials properties and then rapidly find materials tailored for specific applications. However, realizing this vision requires both providing detailed uncertainty quantification (model prediction errors and domain of applicability) and making models readily usable. At present, it is common practice in the community to assess ML model performance only in terms of prediction accuracy (e.g., mean absolute error), while neglecting detailed uncertainty quantification and robust model accessibility and usability. Here, we demonstrate a practical method for realizing both uncertainty and accessibility features with a large set of models. We develop random forest ML models for 33 materials properties spanning an array of data sources (computational and experimental) and property types (electrical, mechanical, thermodynamic, etc.). All models have calibrated ensemble error bars to quantify prediction uncertainty and domain of applicability guidance enabled by kernel-density-estimate-based feature distance measures. All data and models are publicly hosted on the Garden-AI infrastructure, which provides an easy-to-use, persistent interface for model dissemination that permits models to be invoked with only a few lines of Python code. We demonstrate the power of this approach by using our models to conduct a fully ML-based materials discovery exercise to search for new stable, highly active perovskite oxide catalyst materials.

Tantalum films incorporated into superconducting circuits have exhibited low surface losses, resulting in long-lived qubit states. Remaining loss pathways originate in microscopic defects which manifest as two level systems (TLS) at low temperature. These defects limit performance, so careful attention to tantalum film structures is critical for optimal use in quantum devices. In this work, we investigate the growth of tantalum using magnetron sputtering on sapphire, Si, and photoresist substrates. In the case of sapphire, we present procedures for growth of fully-oriented films with alpha-Ta [1 1 1] // Al2O3 [0 0 1] and alpha-Ta [1 -1 0] // Al2O3 [1 0 0] orientational relationships, and having residual resistivity ratios (RRR) ~ 60 for 220 nm thick films. On Si, we find a complex grain texturing with Ta [1 1 0] normal to the substrate and RRR ~ 30. We further demonstrate airbridge fabrication using Nb to nucleate alpha-Ta on photoresist surfaces. Superconducting resonators patterned from films on sapphire show TLS-limited quality factors of 1.4 +/- 0.3 x 10^6 at 10 mK. Structural characterization using scanning electron microscopy, X-ray diffraction, low temperature transport, secondary ion mass spectrometry, and transmission electron microscopy reveal the dependence of residual impurities and screw dislocation density on processing conditions. The results provide practical insights for fabrication of high-performing technological devices including qubit arrays, and guide future work on crystallographically deterministic qubit fabrication.

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 300 K. In-situ low-temperature scanning tunneling microscopy/spectroscopy and density-functional theory calculations further reveal that a 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.

We use a random forest model to predict the critical cooling rate (RC) for glass formation of various alloys from features of their constituent elements. The random forest model was trained on a database that integrates multiple sources of direct and indirect RC data for metallic glasses to expand the directly measured RC database of less than 100 values to a training set of over 2,000 values. The model error on 5-fold cross validation is 0.66 orders of magnitude in K/s. The error on leave out one group cross validation on alloy system groups is 0.59 log units in K/s when the target alloy constituents appear more than 500 times in training data. Using this model, we make predictions for the set of compositions with melt-spun glasses in the database, and for the full set of quaternary alloys that have constituents which appear more than 500 times in training data. These predictions identify a number of potential new bulk metallic glass (BMG) systems for future study, but the model is most useful for identification of alloy systems likely to contain good glass formers, rather than detailed discovery of bulk glass composition regions within known glassy systems.

We examine the effects of Pt vacancies on the magnetotransport properties of Weyl semimetal candidate GdPtSb films, grown by molecular beam epitaxy on c-plane sapphire. Rutherford backscattering spectrometry (RBS) and x-ray diffraction measurements suggest that phase pure GdPt$_{x}$Sb films can accommodate up to $15\%$ Pt vacancies ($x=0.85$), which act as acceptors as measured by Hall effect. Two classes of electrical transport behavior are observed. Pt-deficient films display a metallic temperature dependent resistivity (d$\rho$/dT$>$0). The longitudinal magnetoresistance (LMR, magnetic field $\mathbf{B}$ parallel to electric field $\mathbf{E}$) is more negative than transverse magnetoresistance (TMR, $\mathbf{B} \perp \mathbf{E}$), consistent with the expected chiral anomaly for a Weyl semimetal. The combination of Pt-vacancy disorder and doping away from the expected Weyl nodes; however, suggests conductivity fluctuations may explain the negative LMR rather than chiral anomaly. Samples closer to stoichiometry display the opposite behavior: semiconductor-like resistivity (d$\rho$/dT$<$0) and more negative transverse magnetoresistance than longitudinal magnetoresistance. Hysteresis and other nonlinearities in the low field Hall effect and magnetoresistance suggest that spin disorder scattering, and possible topological Hall effect, may dominate the near stoichiometric samples. Our findings highlight the complications of transport-based identification of Weyl nodes, but point to possible topological spin textures in GdPtSb.

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 mesoscale structure in a molecular glass system which may be relevant to organic electronics.

Short-timescale atomic rearrangements are fundamental to the kinetics of glasses and frequently dominated by one atom moving significantly (a rearrangement), while others relax only modestly. The rates and directions of such rearrangements (or hops) are dominated by the distributions of activation barriers (Eact) for rearrangement for a single atom and how those distributions vary across the atoms in the system. We have used molecular dynamics simulations of Cu50Zr50 metallic glass below Tg in an isoconfigurational ensemble to catalog the ensemble of rearrangements from thousands of sites. The majority of atoms are strongly caged by their neighbors, but a tiny fraction has a very high propensity for rearrangement, which leads to a power-law variation in the cage-breaking probability for the atoms in the model. In addition, atoms generally have multiple accessible rearrangement vectors, each with its own Eact. However, atoms with lower Eact (or higher rearrangement rates) generally explored fewer possible rearrangement vectors, as the low Eact path is explored far more than others. We discuss how our results influence future modeling efforts to predict the rearrangement vector of a hopping atom.

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 amongst which is the column position. Neural networks (NNs) are a high performance, computationally efficient method 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.

The bulk and surface dynamics of Cu50Zr50 metallic glass were studied using classical molecular dynamics (MD) simulations. As the alloy undergoes cooling, it passes through liquid, supercooled, and glassy states. While bulk dynamics showed a marked slowing down prior to glass formation, with increasing activation energy, the slowdown in surface dynamics was relatively subtle. The surface exhibited a lower glass transition temperature than the bulk, and the dynamics preceding the transition were accurately described by a temperature-independent activation energy. Surface dynamics were much faster than bulk at a given temperature in the supercooled state, but surface and bulk dynamics were found to be very similar when compared at their respective glass transition temperatures. The manifestation of dynamical heterogeneity, as characterized by the non-Gaussian parameter and breakdown of the Stokes-Einstein equation, was also similar between bulk and surface for temperatures scaled by their respective glass transition temperatures. Individual atom motion was dominated by a cage and jump mechanism in the glassy state for both the bulk and surface. We utilize this cage and jump mechanisms to separate the activation energy for diffusion into two parts: (i) cage-breaking barrier (Q1), associated with the rearrangement of neighboring atoms to free up space and (ii) the subsequent jump barrier (Q2). It was observed that Q1 dominates Q2 for both bulk and surface diffusion, and the difference in activation energies for bulk and surface diffusion mainly arose from the differences in cage-breaking barrier Q1., Comment: Manuscript and Supplementary Information all as a single PDF

We have developed models of metallic alloy glass forming ability based on newly computationally accessible features obtained from molecular dynamics simulations. In this work we showed that it is possible to increase the predictive value of GFA models by using input features obtained from molecular dynamics simulations. Such features require only relatively straightforward and scalable simulations, making them significantly easier and less expensive to obtain than experimental measurements. We generated a database of molecular dynamics critical cooling rates along with associated candidate features that are inspired from previous research on GFA. Out of the list of 9 proposed GFA features, we identify two as being the most important to performance through a LASSO model. Enthalpy of crystallization and icosahedral-like fraction at 100 K showed promise because they enable a significant improvement to model performance and because they are accessible to flexible ab initio quantum mechanical methods readily applicable to almost all systems. This advancement in computationally accessible features for machine learning predictions GFA will enable future models to more accurately predict new glass forming alloys.

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 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- 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 and DS over 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 activation energy (Q) and compare 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., Comment: Manuscript and Supplementary Information as a single PDF

Various combinations of characteristic temperatures, such as the glass transition temperature, liquidus temperature, and crystallization temperature, have been proposed as predictions of the glass forming ability of metal alloys. We have used statistical approaches from machine learning to systematically explore a wide range of possible characteristic temperature functions for predicting glass forming ability in the form of critical casting diameter, $D_{max}$. Both linear and non-linear models were used to learn on the largest database of $D_{max}$ values to date consisting of 747 compositions. We find that no combination of temperatures for features offers a better prediction of $D_{max}$ in a machine learning model than the temperatures themselves, and that regression models suffer from poor performance on standard machine learning metrics like root mean square error (minimum value of $3.3 \pm 0.1$ $mm$ for data with a standard deviation of 4.8 $mm$). Examination of the errors vs. database size suggest that a larger database may improve results, although a database significantly larger than that used here would likely be required. Shifting a focus from regression to categorization models learning from characteristic temperatures can be used to weakly distinguish glasses likely to be above vs. below our database's median $D_{max}$ value of 4.0 $mm$, with a mean F1 score of $0.77 \pm 0.02$ for this categorization. The overall weak results on predicting $D_{max}$ suggests that critical cooling rate might be a better target for machine learning model prediction.

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 Al$_2$O$_3$ 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 (piezomagnetixm and magnetostriction) and strain gradients (flexomagnetism).

The electronic, magnetic, thermoelectric, and topological properties of Heusler compounds (composition $XYZ$ or $X_2 YZ$) are highly sensitive to stoichiometry and defects. Here we establish the existence and experimentally map the bounds of a \textit{semi} adsorption-controlled growth window for semiconducting half Heusler FeVSb films, grown by molecular beam epitaxy (MBE). We show that due to the high volatility of Sb, the Sb stoichiometry is self-limiting for a finite range of growth temperatures and Sb fluxes, similar to the growth of III-V semiconductors such as GaSb and GaAs. Films grown within this window are nearly structurally indistinguishable by X-ray diffraction (XRD) and reflection high energy electron diffraction (RHEED). The highest electron mobility and lowest background carrier density are obtained towards the Sb-rich bound of the window, suggesting that Sb-vacancies may be a common defect. Similar \textit{semi} adsorption-controlled bounds are expected for other ternary intermetallics that contain a volatile species $Z=$\{Sb, As, Bi\}, e.g., CoTiSb, LuPtSb, GdPtBi, and NiMnSb. However, outstanding challenges remain in controlling the remaining Fe/V ($X/Y$) transition metal stoichiometry.

A major challenge for ferroelectric devices is the depolarization field, which competes with and often destroys long-range polar order in the limit of ultrathin films. Recent theoretical predictions suggest a new class of materials, termed hyperferroelectics, that should be robust against the depolarization field and enable ferroelectricity down to the monolayer limit. Here we demonstrate the epitaxial growth of hexagonal LiZnSb, one of the hyperferroelectric candidate materials, by molecular-beam epitaxy on GaSb (111)B substrates. Due to the high volatility of all three atomic species, we find that LiZnSb can be grown in an adsorption-controlled window, using an excess zinc flux. Within this window, the desired polar hexagonal phase is stabilized with respect to a competing cubic polymorph, as revealed by X-ray diffraction and transmission electron microscopy measurements. First-principles calculations show that for moderate amounts of epitaxial strain and moderate concentrations of Li vacancies, the cubic LiZnSb phase is lower in formation energy than the hexagonal phase, but only by a few meV per formula unit. Therefore we suggest that kinetics plays a role in stabilizing the desired hexagonal phase at low temperatures. Our results provide a path towards experimentally demonstrating ferroelectricity and hyperferroelectricity in a new class of ternary intermetallic compounds., Comment: The following article has been submitted to the Journal of Vacuum Science and Technology. After it is published, it will be found at https://avs.scitation.org/journal/jvb

We report the molecular beam epitaxial growth, structure, and electronic measurements of single-crystalline LaAuSb films on Al$_2$O$_3$ (0001) substrates. LaAuSb belongs to a broad family of hexagonal $ABC$ intermetallics in which the magnitude and sign of layer buckling have strong effects on properties, e.g., predicted hyperferroelecticity, polar metallicity, and Weyl and Dirac states. Scanning transmission electron microscopy reveals highly buckled planes of Au-Sb atoms, with strong interlayer Au-Au interactions and a doubling of the unit cell. This buckling is four times larger than the buckling observed in other $ABC$s with similar composition, e.g. LaAuGe and LaPtSb. Photoemission spectroscopy measurements and comparison with theory suggest an electronic driving force for the Au-Au dimerization, since LaAuSb, with a 19-electron count, has one more valence electron per formula unit than most stable $ABC$s. Our results suggest that the electron count, in addition to conventional parameters such as epitaxial strain and chemical pressure, provides a powerful means for tuning the layer buckling in ferroic $ABC$s., Comment: in press

We use a structure analysis technique that quantifies cluster geometries using machine learning to identify a bicapped square antiprism (BSAP) as the nearest-neighbor cluster involved in the glass transition in Pd82Si18, a prototype metal-metalloid metallic glass. BSAPs have slow dynamics and grow significantly in concentration during cooling, like icosahedra in other systems. These results are evidence that some preferred structures universally contribute to the glass transition and show that analysis of interatomic geometry can find important structures not revealed by topology.

The structural motifs of a Zr$_{50}$Cu$_{45}$Al$_{5}$ metallic glass were learned from atomistic models using a new structure analysis method called motif extraction that employs point-pattern matching and machine learning clustering techniques. The motifs are the nearest-neighbor building blocks of the glass and reveal a well-defined hierarchy of structures as a function of coordination number. Some of the motifs are icosahedral or quasi-icosahedral in structure, while others take on the structure of the most close-packed geometries for each coordination number. These results set the stage for developing clearer structure-property connections in metallic glasses. Motif extraction can be applied to any disordered material to identify its structural motifs without the need for human input.

StructOpt, an open-source structure optimization suite, applies genetic algorithm and particle swarm methods to obtain atomic structures that minimize an objective function. The objective function typically consists of the energy and the error between simulated and experimental data, which is typically applied to determine structures that minimize energy to the extent possible while also being fully consistent with available experimental data. We present example use cases including the structure of a metastable Pt nanoparticle determined from energetic and scanning transmission electron microscopy data, and the structure of an amorphous-nanocrystal composite determined from energetic and fluctuation electron microscopy data. StructOpt is modular in its construction and therefore is naturally extensible to include new materials simulation modules or new optimization methods, either written by the user or existing in other code packages. It uses the Message Passing Interface's (MPI) dynamic process management functionality to allocate resources to computationally expensive codes on the fly, enabling StructOpt to take full advantage of the parallelization tools available in many scientific packages.

The local arrangement of atoms is one of the most important predictors of mechanical and functional properties of materials. However, algorithms for identifying the geometrical arrangements of atoms in complex materials systems are lacking. To address this challenge, we present a point-pattern matching algorithm that can detect instances of a `template' structure in a given set of atom coordinates. To our knowledge this is the first geometrical comparison technique for atomistic configurations with very different number of atoms, and when the optimal rotations and translations required to align the configurations are unknown. The pattern matching algorithm can be combined with an appropriate set of metrics to quantify the similarity or dissimilarity between configurations of atoms. We demonstrate the unique capabilities of the point-pattern matching algorithm using two examples where the automated analysis of local atomistic structure can help advance the understanding of structure-property relationships in material science: (a) identifying local three-dimensional polyhedral units along interfacial defects, and (b) the analysis of quasi-icosahedral topologies in the atomistic structure of metallic glasses. We anticipate that the pattern matching algorithm will be applicable in the analysis of atomistic structures in broad areas of condensed matter systems, including biological molecules, polymers and disordered metallic systems. An online implementation of the algorithm is provided via the open source code hosted on GitHub (https://github.com/spatala/ppm3d).

Magnetic materials that possess large bulk perpendicular magnetic anisotropy (PMA) are essential for the development of magnetic tunnel junctions (MTJs) used in future spintronic memory and logic devices. The addition of an antiferromagnetic layer to these MTJs was recently predicted to facilitate ultra-fast magnetization switching. Here, we report a demonstration of a bulk perpendicular synthetic antiferromagnetic (P-SAFM) structure comprised of a (001) textured FePd/Ru/FePd trilayer with a face-centered-cubic (fcc) phase Ru spacer. The L10 FePd P-SAFM structure shows a large bulk PMA (~10.2 Merg/cc) and strong antiferromagnetic coupling (~2.60 erg/cm2). Full perpendicular magnetic tunnel junctions (P-MTJs) with a L10 FePd P-SAFM layer are then fabricated. Tunneling magnetoresistance ratios of up to ~25% (~60%) are observed at room temperature (5K) after post-annealing at 350 C. Exhibiting high thermal stabilities and large Ku, the bulk P-MTJs with an L10 FePd P-SAFM layer could pave a way for next-generation ultrahigh-density and ultra-low-energy spintronic applications., Comment: 8 pages, 4 figures

We report an approach for three-dimensional imaging of single vacancies using high precision quantitative high-angle annular dark-field Z-contrast scanning transmission electron microscopy (STEM). Vacancies are identified by both the reduction in scattered intensity created by the missing atom and the distortion of the surrounding atom positions. Vacancy positions are determined laterally to a unique lattice site in the image and in depth to within one of two lattice sites by dynamical diffraction effects. 35 single La vacancies are identified in images of a LaMnO3 thin film sample. The vacancies are randomly distributed in depth and correspond to a La vacancy concentration of 0.79%, which is consistent with the level of control of cation stoichiometry within our synthesis process (~1%) and with the equilibrium concentration of La vacancies under the film growth conditions. This work demonstrates an approach to characterizing low concentrations of vacancies with high spatial resolution.

Supercooled liquids exhibit spatial heterogeneity in the dynamics of their fluctuating atomic arrangements. The length and time scales of the heterogeneous dynamics are central to the glass transition and influence nucleation and growth of crystals from the liquid. We report direct experimental visualization of the spatially heterogeneous dynamics as a function of temperature in the supercooled liquid state of a Pt-based metallic glass, using electron correlation microscopy with sub-nanometer resolution. An experimental four point space-time intensity correlation function demonstrates a growing dynamic correlation length, $\xi$, upon cooling of the liquid toward the glass transition temperature. $\xi$ as a function of the relaxation time $\tau$ data are in the good agreement with the Adam-Gibbs, inhomogeneous mode coupling theory and random first order transition theory of the glass transition. The same experiments demonstrate the existence of a nanometer thickness near surface layer with order of magnitude shorter relaxation time than inside the bulk., Comment: updated references updated method to extract correlation length from g4(\Delta r, \Delta t); conclusion favoring RFOT model is weakened, but other results are unchanged

Voltage control effects provide an energy-efficient means of tailoring material properties, especially in highly integrated nanoscale devices. However, only insulating and semiconducting systems can be controlled so far. In metallic systems, there is no electric field due to electron screening effects and thus no such control effect exists. Here we demonstrate that metallic systems can also be controlled electrically through ionic not electronic effects. In a Pt/Co structure, the control of the metallic Pt/Co interface can lead to unprecedented control effects on the magnetic properties of the entire structure. Consequently, the magnetization and perpendicular magnetic anisotropy of the Co layer can be independently manipulated to any desired state, the efficient spin toques can be enhanced about 3.5 times, and the switching current can be reduced about one order of magnitude. This ability to control a metallic system may be extended to control other physical phenomena., Comment: 20 pages, 7 figures, Accepted by Physical Review Applied (2017)

Understanding the structure of ZnO surface reconstructions and their resultant properties is crucial to the rational design of ZnO-containing devices ranging from optoelectronics to catalysts. Here, we are motivated by recent experimental work which showed a new surface reconstruction containing Zn vacancies ordered in a Zn(3x3) pattern in the subsurface of (0001)-O terminated ZnO. A reconstruction with Zn vacancies on (0001)-O is surprising and counterintuitive because Zn vacancies enhance the surface dipole rather than reduce it. In this work, we show using Density Functional Theory (DFT) that subsurface Zn vacancies can form on (0001)-O when coupled with adsorption of surface H and are in fact stable under a wide range of common conditions. We also show these vacancies have a significant ordering tendency and that Sb-doping created subsurface inversion domain boundaries (IDBs) enhances the driving force of Zn vacancy alignment into large domains of the Zn(3x3) reconstruction.

Although defect clusters are detrimental to electronic and mechanical properties of semiconductor materials, annihilation of such clusters is limited by their lack of thermal mobility due to high migration barriers. Here, we find that small clusters in bulk SiC (a covalent material of importance for both electronic and nuclear applications) can become mobile at room temperature under the influence of electron radiation. So far, direct observation of radiation-induced diffusion of defect clusters in bulk materials has not been demonstrated yet. This finding was made possible by low angle annular dark field (LAADF) scanning transmission electron microscopy (STEM) combined with non-rigid registration technique to remove sample instability, which enables atomic resolution imaging of small migrating defect clusters. We show that the underlying mechanism of this athermal diffusion is ballistic collision between incoming electrons and cluster atoms. Our findings suggest that defect clusters may be mobile under certain irradiation conditions, changing current understanding of cluster annealing process in irradiated covalent materials.

FEMSIM, a Fortran code, has been developed to simulate the fluctuation electron microscopy signal, the variance, V(k), from a model atomic structure. FEMSIM has been incorporated into a hybrid-reverse Monte Carlo code that combines an embedded atom or Finnis-Sinclair potential with the deviation between simulated and experimental V(k) data to refine an atomic model with structure constrained by both the potential and experimental data. The resulting models have experimentally-derived medium-range order., Comment: 14 pages, 3 figures, preprint

Perpendicular magnetic anisotropy [Co/Pd]15 and L10-FePt nanowire arrays of period 63 nm with linewidths 38 nm and 27 nm and film thickness 27 nm and 20 nm respectively were fabricated using a self-assembled PS-b-PDMS diblock copolymer film as a lithographic mask. The wires are predicted to support Neel walls in the Co/Pd and Bloch walls in the FePt. Magnetostatic interactions from nearest neighbor nanowires promote a ground state configuration consisting of alternating up and down magnetization in adjacent wires. This was observed over ~75% of the Co/Pd wires after ac-demagnetization but was less prevalent in the FePt because the ratio of interaction field to switching field was much smaller. Interactions also led to correlations in the domain wall positions in adjacent Co/Pd nanowires. The reversal process was characterized by nucleation of reverse domains, followed at higher fields by propagation of the domains along the nanowires. These narrow wires provide model system for exploring domain wall structure and dynamics in perpendicular anisotropy systems.

Quantitative density measurements from electron scattering show that shear bands in deformed Al_{88}Y_{7}Fe_{5} metallic glass exhibit alternating high and low density regions, ranging from -9 % to +6 % relative to the un-deformed matrix. Small deections of the shear band from the main propagation direction coincide with switches in density from higher to lower than the matrix and vice versa, indicating that faster and slower motion (stick-slip) occurs during the propagation. Nano-beam diffraction analyses provides clear evidence that the density changes are accompanied by structural changes suggesting that shear alters the packing of tightly-bound short- or medium-range atomic clusters. This bears a striking resemblance to the packing behavior in granular shear bands formed upon deformation of granular media.