A Mg-Y-Al-Zn-Zr alloy (MRL250TM) was fabricated by laser powder bed fusion (LPBF). The processing window was identified using a dimensionless number (Pi 2) that accounts for volumetric energy density, laser dwell time, and thermophysical properties. Within the tested range, Pi 2 values of 141 and 143 produced the highest tensile performance, achieving an as-built yield strength of 261 MPa, an ultimate tensile strength of 376 MPa, and 7% ductility with a specific strength of 204 kNm/kg. Fractography showed a rough, dimpled morphology consistent with ductile failure, and the as-built microstructure contained well-dispersed nanoscale Al2Y particles. These results demonstrate that Pi 2, a reduced order energy balance dimensionless number, identifies a viable LPBF processing window for Mg alloys, and that within this window the Mg-Y-Al-Zn-Zr alloy exhibits a previously unreported combination of uniaxial tensile strength and ductility.

We have used ion beam assisted deposition to modify the properties of a thin film Pd77.5Cu6Si16.5 metallic glass without altering the composition. By irradiating the film surface during deposition, the mobility of surface atoms is enhanced, leading to the development of atomically smooth films with increased hardness and kinetic stability. Further, increasing the ion beam energy changes the crystallization pathways in the metallic glass films, pointing to changes in as-deposited structure. This approach to tune the properties of metallic glass films may unlock access to previously unobserved structural states.

We have developed a machine learning model for critical cooling rates for metallic glasses based on computational properties, supporting in-silico screening for desired Rc values and significantly reducing reliance on time-consuming laboratory work. We compare results for features derived from easy-to-compute functions of elemental properties to more complex physically motivated properties using ab initio, machine-learning potentials, and empirical potential molecular dynamics methods. The established approach enables property acquisition across a diverse range of alloys. Analysis of various features for 34 alloys from 20 chemical systems shows that the best model for critical cooling rates was learned from one elemental property-based feature and three simulated features. The elemental property based feature is an ideal entropy value based on alloy stoichiometry. The simulated features were acquired from estimates of energies above the convex hull, changes in heat capacity, and the fraction of icosahedra-like Voronoi polyhedra. Models were assessed through a demanding cross validation test based on repeatedly leaving out full chemical systems as test sets and had an R2 of 0.78 and a mean average error of 0.76 in units of lg(K/s). We demonstrate with Shapley additive explanation analysis that the most impactful features have physically reasonable influence on model predictions. The established methodology can be applied to other high-throughput studies of material properties of diverse compositions.

N-heterocyclic aromatic molecules (N-LHCs) can store hydrogen (H2) in their chemical bonds through reversible (de)hydrogenation over supported metal catalysts, but catalyst deactivation mechanisms during H2 storage reactions remain poorly understood. Here, we investigated the reactivity and stability of supported Pd and Ni catalysts for the liquid-phase hydrogenation of N-LHCs with varying methyl group positions. We combine continuous flow reactor studies to probe time-on-stream stability and regenerability, with post-reaction catalyst characterization (CO chemisorption, microscopy, elemental analysis) to assess catalyst deactivation routes, including coking, sintering, and leaching. While decreases in rates with time-on-stream due to coking are observed with both metals (Pd, Ni) across all studied carriers, irreversible deactivation due to sintering strongly depends on the structure of N-LHC and the catalyst surface. Over Pd/SiO2 and Pd/Al2O3, while only reversible deactivation via coking occurred during the hydrogenation of indole (performing >5900 catalytic turnovers), indoles methylated at the N- and 2-positions instead caused irreversible sintering. In contrast, Ni/SiO2 catalysts were resistant to sintering for indole and N-methylindole hydrogenations (performing up to 150,000 turnovers) but sintered during the hydrogenation of 2-methylindole. To gain further insights into reactant-dependent sintering, we investigated additional N-LHCs with different basicities and steric properties. The lack of a monotonic correlation between catalyst stability and basicity of N-LHCs suggests that a combination of steric and electronic properties of N-LHC molecules impacts their propensity to induce sintering. This work provides new insights into reactant-dependent catalyst stability that guide the selection of catalysts and carriers for H2 storage in chemical bonds.

In alignment with the Materials Genome Initiative and as the product of a workshop sponsored by the US National Science Foundation, we define a vision for materials laboratories of the future in alloys, amorphous materials, and composite materials; chart a roadmap for realizing this vision; identify technical bottlenecks and barriers to access; and propose pathways to equitable and democratic access to integrated toolsets in a manner that addresses urgent societal needs, accelerates technological innovation, and enhances manufacturing competitiveness. Spanning three important materials classes, this article summarizes the areas of alignment and unifying themes, distinctive needs of different materials research communities, key science drivers that cannot be accomplished within the capabilities of current materials laboratories, and open questions that need further community input. Here, we provide a broader context for the workshop, synopsize the salient findings, outline a shared vision for democratizing access and accelerating materials discovery, highlight some case studies across the three different materials classes, and identify significant issues that need further discussion.

Tantalum films incorporated into superconducting circuits have exhibited low surface losses, resulting in long-lived qubit states. The remaining loss pathways originate in microscopic defects that manifest as two level systems (TLSs) at low temperatures. 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 the growth of fully-oriented films with α-Ta [1 1 1]//Al2O3 [0 0 0 1] and α-Ta [1 −1 0]//Al2O3 [1 0 −1 0] orientational relationships and having residual resistivity ratio (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 α-Ta on photoresist surfaces. For the films on sapphire, resonators show TLS-limited quality factors of 1.3 ± 0.3 × 106 at 10 mK (for a waveguide gap and conductor width of 3 and 6 μm, respectively). 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 into the fabrication of advanced superconducting devices including qubit arrays and guide future works on crystallographically deterministic qubit fabrication.

In recent years, T d transition 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 2 M ‐WSe 2 bulk 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 2 M ‐WSe 2 are similar to those of orthorhombic MoTe 2 and WTe 2 , 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 2 M ‐WSe 2 and motivate further investigation on the topological properties of 2 M transition metal dichalcogenides in the atomically thin limit.

Highly ordered liquid crystalline (LC) phases have important potential for organic electronics. We studied the molecular alignment and domain structure in a columnar LC thin film with nanometer resolution during in situ heating using four-dimensional scanning transmission electron microscopy (4D STEM). The initial disordered vapor-deposited LC glass thin film rapidly ordered at its glass transition temperature into a hexagonal columnar phase with small (<10 nm), well-aligned, planar domains (columns oriented parallel to the surface). Upon further heating, the domains coarsen via bulk diffusion, then the film crystallizes, then finally transforms back to an LC phase at an even higher temperature. The LC phase at high temperature shows straight columns of molecules, which we attribute to structure inherited from the intermediate crystalline phase. Nanoscale 4D STEM offers direct insight into the mechanisms of domain reorganization, and intermediate crystallization is a potential approach to manipulate orientational order and texture at the nano- to mesoscale in LC thin films.

This project supported research advancing the state of the art in scanning transmission electron microscopy (STEM) imaging and characterization of materials, especially electronic materials. The project began with studies of point defects in materials using high-resolution Z-contrast STEM, then evolved to including methods in four-dimensional (4D) STEM and machine learning. Highlights include: (1) the discovery of stable, p-type ZnO via Sb doping, (2) demonstration that the high performance of InGaN LEDs is not due to In composition fluctuations as previously proposed, (3) the demonstration of the first sub-pm precision high-resolution STEM images using non-rigid registration of a series of STEM images, (4) the extension of high-precision STEM to low dose imaging and spectrum imaging, (5) discovery of a synthesis method for twisted spiral growth of transition-metal dichalcogenide materials via chemical vapor deposition, and (6) development of machine-learning methods for analysis and denoising of enormous 4D STEM data sets.

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.

Remote and van der Waals epitaxy are promising approaches for synthesizing single crystalline membranes for flexible electronics and discovery of new properties via extreme strain; however, a fundamental challenge is that most materials do not wet the graphene surface. We develop a cold seed approach for synthesizing smooth intermetallic films on graphene that can be exfoliated to form few nanometer thick single crystalline membranes. Our seeded GdAuGe films have narrow X-ray rocking curve widths of 9-24 arc seconds, which is 2 orders of magnitude lower than their counterparts grown by typical high temperature methods, and have atomically sharp interfaces observed by transmission electron microscopy. Upon exfoliation and rippling, strain gradients in GdAuGe membranes induce an antiferromagnetic to ferri/ferromagnetic transition. Our smooth, ultrathin membranes provide a clean platform for discovering new flexomagnetic effects in quantum materials.