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Medium-range structure at a length scale between ~0.5 and 3 nm strongly influences the properties and phase transformations of metallic glass alloys. Electron diffraction methods based on four-dimensional scanning transmission electron microscopy are well suited for characterizing such structure using nanodiffraction with beams matched in size to the length scale of interest. Structural characterization using techniques including fluctuation electron microscopy, Ångström-beam electron diffraction, and angular correlation mapping have revealed widespread competition between crystalline and noncrystalline order, structural motifs responsible for crystallization and glass formation, and length scales controlling plasticity. Time-resolved, in situ electron correlation microscopy has demonstrated spatially heterogeneous dynamics in supercooled liquids and glasses and has hinted at connections between structure and atomic motions. Continued advances in techniques, sources, and detectors offer prospects for more discoveries to come. 

We describe a method for identifying and clustering diffraction vectors in four-dimensional (4-D) scanning transmission electron microscopy data to determine characteristic diffraction patterns from overlapping structures in projection. First, the data is convolved with a 4-D kernel, then diffraction vectors are identified and clustered using both density-based clustering and a metric that emphasizes rotational symmetries. The method works well for both crystalline and amorphous samples and in high- and low-dose experiments. A simulated dataset of overlapping aluminum nanocrystals provides performance metrics as a function of Poisson noise and the number of overlapping structures. Experimental data from an aluminum nanocrystal sample shows similar performance. For an amorphous Pd77.5Cu6Si16.5 thin film, experiments measuring glassy structure show strong evidence of 4- and 6-fold symmetry structures. A significant background arises from the diffraction of overlapping structures. Quantifying this background helps to separate contributions from single, rotationally symmetric structures vs. apparent symmetries arising from overlapping structures in projection. 

Metallic glass thin films (MGTFs) are a recently developed class of alloy coatings with potential applications ranging from biomedical devices to electrical components. Their tribological performance in service conditions is dictated by MGTF bulk composition but can be limited by the native oxide surface that inevitably forms upon exposure to atmosphere. Surface oxidation, thickness, and composition of ZrCuNiAl MGTFs were characterized using a combination of X-ray photoelectron microscopy (XPS) and electron probe microanalysis (EPMA). MGTF samples with nominal thicknesses of 50, 500, and 1500 nm were sputtered onto Si and SiN wafer substrates within a high vacuum deposition chamber and their amorphicity was confirmed by X-ray diffraction. XPS depth profiling identified the thin film composition and showed that the surface oxide was dominated by a mixed layer of mostly ZrO2, a little oxidized Al, and some metallic Zr. EPMA X-ray intensities were acquired as a function of beam energy to excite characteristic X-rays from different depths of the MGTFs and reconstructed using open-source thin film analysis software BadgerFilm, to determine the composition and thickness of sample layers. EPMA results constrain the composition to be Zr54Cu29Al10Ni7 within 0.7 at. % variation and total thicknesses to be 49, 470, and 1546 nm. Using the oxide composition identified from XPS depth profiling as an input for BadgerFilm analysis, EPMA results indicate the surface oxidation layer on each of the thin film samples was 6.5 ± 1.1 nm thick and uniform across a 0.25 mm region of the film. 

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. 

Abstract In recent years,T_dtransition metal dichalcogenides have been heavily explored for their type‐II Weyl topology, gate‐tunable superconductivity, and nontrivial edge states in the monolayer limit. Here, the Fermi surface characteristics and fundamental transport properties of similarly structured 2M‐WSe₂bulk single crystals are investigated. The measurements of the angular dependent Shubnikov–de Haas oscillations, with support from first‐principles calculations, reveal multiple three‐ and two‐dimensional Fermi pockets, one of which exhibits a nontrivial Berry's phase. In addition, it is shown that the electronic properties of 2M‐WSe₂are similar to those of orthorhombic MoTe₂and WTe₂, having a single dominant carrier type at high temperatures that evolves into coexisting electron and hole pockets with near compensation at temperatures below 100 K, suggesting the existence of a Lifshitz transition. Altogether, the observations provide evidence towards the topologically nontrivial electronic properties of 2M‐WSe₂and motivate further investigation on the topological properties of 2Mtransition metal dichalcogenides in the atomically thin limit.