Combinatorial magnetron sputtering was used to generate a library of devices with a wide range of Hf:Zr ratios. The electrical properties of these devices were systematically investigated and correlated to the crystal structure. XRD, XRR, XPS, and EDS were used to measure film structure, thickness, bonding, and chemistry across the sample, respectively. Time-resolved PUND measurements were used to confirm ferroelectric behavior in devices ranging from 49 to 22 atomic % Hf. The switching current and polarization were extracted from the PUND measurements. Ferroelectricity is shown to increase from 49 to 38% Hf. Further decreasing Hf decreases the switching polarization, while also generating peak splitting behavior. Device endurance properties were measured and showed consistent switching up to 104 cycles, while enduring cycling out to 105 cycles. Increased cycling of bimodal switching unified the peak splitting, while increasing the overall switching polarization. XRD structural measurements showed high orthorhombic/tetragonal peaks in the regions exhibiting ferroelectricity, suggesting that the primary phase driving the ferroelectric behavior is orthorhombic.

Combinatorial magnetron sputtering was used to generate a library of devices with a wide range of Hf:Zr ratios. The electrical properties of these devices were systematically investigated and correlated to the crystal structure. XRD, XRR, XPS, and EDS were used to measure film structure, thickness, bonding, and chemistry across the sample, respectively. Time-resolved PUND measurements were used to confirm ferroelectric behavior in devices ranging from 49 to 22 atomic % Hf. The switching current and polarization were extracted from the PUND measurements. Ferroelectricity is shown to increase from 49 to 38% Hf. Further decreasing Hf decreases the switching polarization, while also generating peak splitting behavior. Device endurance properties were measured and showed consistent switching up to 104 cycles, while enduring cycling out to 105 cycles. Increased cycling of bimodal switching unified the peak splitting, while increasing the overall switching polarization. XRD structural measurements showed high orthorhombic/tetragonal peaks in the regions exhibiting ferroelectricity, suggesting that the primary phase driving the ferroelectric behavior is orthorhombic.

In this work, we demonstrated direct-write editing of niobium superconducting thin film devices by focused electron beam induced etching (FEBIE) with the XeF2 precursor. Niobium films of 200 and 50 nm thickness were deposited onto SiO2 coated silicon wafers by magnetron sputtering and fabricated into four-point probe patterns. Directly written FEBIE current crowding Dayem bridge devices were synthesized and characterized. Furthermore, Josephson junctions were fabricated via FEBIE line etches to form a trench across the ≈3.8 µm superconducting channel width. Detailed superconducting transport properties of the devices were characterized.

ABSTRACT A comprehensive investigation of composition‐driven symmetry modulation and the structure–property correlation in the lead‐free ferroelectric (1− x )K 0 . 5 Na 0 . 5 NbO 3 – x BiScO 3 (KNN‐ x BS) (0 ≤ x ≤ 0.05) system is discussed. Structural analysis using Rietveld refinement combined with Raman spectroscopy reveals a systematic evolution of the room‐temperature crystal symmetry from orthorhombic ( Amm 2, x = 0) to the coexistence of orthorhombic + monoclinic (0.005 ≤ x ≤ 0.010), to single‐phase monoclinic ( Pm , 0.015 ≤ x ≤ 0.020), and finally to coexistence of monoclinic + cubic ( x ≥ 0.030). Microstructural analysis demonstrates the systematic decrease of grain size with BiScO 3 substitution. Temperature‐dependent dielectric measurements demonstrated a progressive downward shift and broadening of the orthorhombic‐tetragonal ( T O – T ) and tetragonal‐cubic ( T T – C ) transitions with increasing x , with the T O – T transition shifting below room temperature for x ≥ 0.015. The composition x = 0.015 exhibits optimal functional response, with d 33 = 154 pC/N, ε r = 921 at T C , and 2 P r = 68 µC/cm 2 , within the monoclinic stability region. These findings elucidate the role of BiScO 3 ‐induced structural heterogeneity and phase coexistence in governing property enhancement in the modified KNN system. The study also provides insights into phase‐boundary engineering strategies in lead‐free perovskite ferroelectrics.

Refractory compositionally complex alloys (RCCAs) are considered the next generation high-temperature materials. However, their high-dimensional composition spaces are too large to explore by traditional density functional theory or experimental means, making new RCCA discovery slow and cumbersome. This work has addressed these challenges with an integrated composition design framework that can efficiently and exhaustively explore the relationship between the compositions and two fundamental aspects: 1) the phase stability, including the target body-centered cubic (BCC) phase and its competing phases (hexagonal closed-pack (HCP) structures, Laves and B2 intermetallic phases), and 2) the mechanical properties. This framework is demonstrated with RCCAs within nine refractory metals (Ti, V, Cr, Zr, Nb, Mo, Hf, Ta, and W). Theory-guided machine learning (ML) models were employed to find the composition-mechanical property relationship of RCCAs, where the established theory is used to supplement the yield strength data at ultra-high temperature, and a forward sequential feature selection (SFS) is used to determine feature selection. The resulting ML model for temperature-dependent yield strength was found to have an R_squared value of 0.98 over the entire temperature range (from 0 to 2000 K). The impact of each constituent element on the six key properties is evaluated. The addition of Nb tends to stabilize the BCC phase and the addition of Ti improves the ductility of RCCAs. Combined with all methods involved in this framework, the on-demand designer allows the alloy designers to have all properties for any RCCA compositions and narrow down the composition space by applying custom screening criteria. The output from the predictor and screener provides valuable guidance for our experimental study of RCCAs and accelerates the pace of materials discovery.

Oxygen-focused ion beam induced deposition (O-FIBID) enables the direct-write fabrication of Pt nanostructures while simultaneously enhancing purity concurrently through reactive oxygen–deposit interactions. By systematically varying the dwell time, accelerating voltage, and precursor pressure, the Pt content and conductivity can be controlled. Under optimum conditions, the Pt content reached 63 at.%. Across the dwell-time range used for resistivity measurements, the Pt content increased from 20 to 33 at.%, while the resistivity decreased from 2.9 × 104 μΩ·cm to 1.2 × 103 μΩ·cm, which is consistent with enhanced percolation through Pt grains and the lower intrinsic resistivity of the purer Pt deposit. The simulation results support a purification mechanism driven by the beam-induced activation of implanted oxygen balanced against the preferential sputtering of Pt. These results demonstrate O-FIBID as a viable method for the nanoscale direct write of conductive Pt without post-processing, and some deviations from conventional FIBID wisdom are observed. These results serve as a foundation for exploring nascent, reactive focused ion beam-induced deposition processes.

High-throughput synthesis and characterization of novel ceramic materials with improved thermomechanical properties and phase stability are needed to accelerate the discovery of next-generation thermal barrier materials. A combinatorial thin film material library of (GdDyHoEr)2Zr2O7 were created via combinatorial magnetron reactive sputtering with rare-earth/zirconium alloy targets. Structural, chemical, and thermal property characterization mapping across the four component composition space was performed and correlated with thermal transport measurements. Steady state thermoreflectance mapping identifies a pronounced minimum in thermal conductivity within the Dy/Gd-rich quadrant. This minimum does not coincide with either the equiatomic composition or the region predicted to exhibit maximum cation size disorder. Instead, it corresponds to the largest experimentally observed lattice parameter, despite deviating from Vegard-like chemical averaging, and is independent of grain size and whole-pattern microstrain. These observations suggest that the way the fluorite lattice accommodates compositional complexity, rather than cation size disorder alone, provides a more informative descriptor of thermal transport. Overall, this work establishes a high-throughput workflow for combinatorial thin-film synthesis and multimodal characterization, enabling the rapid identification of previously inaccessible structure-property relationships in compositionally complex ceramics.

Accelerating the discovery of mechanical properties in combinatorial materials requires autonomous experimentation that accounts for both instrument behavior and experimental cost. Here, an automated nanoindentation (AE-NI) framework is developed and validated for adaptive mechanical mapping of combinatorial thin-film libraries. The method integrates heteroskedastic Gaussian-process modeling with cost-aware Bayesian optimization to dynamically select indentation locations and hold times, minimizing total testing time while preserving measurement accuracy. A detailed emulator and cost model capture the intrinsic penalties associated with lateral motion, drift stabilization, and reconfiguration-factors often neglected in conventional active-learning approaches. To prevent kernel-length-scale collapse caused by disparate time scales, a hierarchical meta-testing workflow combining local grid and global exploration is introduced. Implementation of the workflow is shown on an experimental Ta-Ti-Hf-Zr thin-film library. The proposed framework achieves nearly a thirty-fold improvement in property-mapping efficiency relative to grid-based indentation, demonstrating that incorporating cost and drift models into probabilistic planning substantially improves performance. This study establishes a generalizable strategy for optimizing experimental workflows in autonomous materials characterization and can be extended to other high-precision, drift-limited instruments.

For over three decades, scanning probe microscopy (SPM) has been a key method for exploring material structures and functionalities at nanometer and often atomic scales in ambient, liquid, and vacuum environments. Historically, SPM applications have predominantly been downstream, with images and spectra serving as a qualitative source of data on the microstructure and properties of materials, and in rare cases of fundamental physical knowledge. However, the fast-growing developments in accelerated material synthesis via self-driving labs and established applications such as combinatorial spread libraries are poised to change this paradigm. Rapid synthesis demands matching capabilities to probe structure and functionalities of materials on small scales and with high throughput. SPM inherently meets these criteria, offering a rich and diverse array of data from a single measurement. Here, we overview SPM methods applicable to these emerging applications and emphasize their quantitativeness, focusing on piezoresponse force microscopy, electrochemical strain microscopy, conductive, and surface photovoltage measurements. We discuss the challenges and opportunities ahead, asserting that SPM will play a crucial role in closing the loop from material prediction and synthesis to characterization.

Accelerating the discovery of mechanical properties in combinatorial materials requires autonomous experimentation that accounts for both instrument behavior and experimental cost. Here, an automated nanoindentation (AE-NI) framework is developed and validated for adaptive mechanical mapping of combinatorial thin-film libraries. The method integrates heteroskedastic Gaussian-process modeling with cost-aware Bayesian optimization to dynamically select indentation locations and hold times, minimizing total testing time while preserving measurement accuracy. A detailed emulator and cost model capture the intrinsic penalties associated with lateral motion, drift stabilization, and reconfiguration-factors often neglected in conventional active-learning approaches. To prevent kernel-length-scale collapse caused by disparate time scales, a hierarchical meta-testing workflow combining local grid and global exploration is introduced. Implementation of the workflow is shown on a experimental Ta-Ti-Hf-Zr thin-film library. The proposed framework achieves nearly a thirty-fold improvement in property-mapping efficiency relative to grid-based indentation, demonstrating that incorporating cost and drift models into probabilistic planning substantially improves performance. This study establishes a generalizable strategy for optimizing experimental workflows in autonomous materials characterization and can be extended to other high-precision, drift-limited instruments.

In this study, we investigate the thermal stability and high-temperature evolution of He bubbles within the structure of the WTaCrV refractory concentrated solid solution alloy (RCSA), which is dedicated to nuclear fusion applications. The material was first irradiated with He+ ions to form nanometric He bubbles within its structure. Subsequently, their high-temperature evolution was studied using an in-situ heating method in a transmission electron microscope over a temperature range of 700 degrees C to 1000 degrees C. We found that the bubbles are stable in size up to a temperature of 700 degrees C and show no agglomeration up to 800 degrees C. At higher temperatures, the coarsening of the bubbles occurs through the migration and coalescence mechanism; however, even at 1000 degrees C, the size of the bubbles only slightly exceeds 1 nm. For a more in-depth understanding of the phenomena occurring during high-temperature annealing, molecular dynamics simulations were applied. We demonstrate that the low diffusivity of VmHen clusters in the investigated WTaCrV alloy is responsible for the low tendency for high- temperature coarsening of the bubbles. The results of this study highlight the potential of the WTaCrV RCSA as a refractory, irradiation-resistant material for crucial components in future fusion reactors.

Focused electron beam induced etching (FEBIE) with XeF 2 (xenon difluoride) precursor is conducted on multi‐layer exfoliated WS 2 (tungsten disulfide) and monolayer WS 2 grown by chemical vapor deposition (CVD). The films are characterized by atomic force microscopy (AFM) and Raman and photoluminescence (PL) spectroscopy post‐etching. The etch rates/efficiencies are reported as a function of electron beam energy, current, dwell time, and XeF 2 pressure. Bulk film Raman spectra are unchanged post‐FEBIE, indicating minimal subsurface damage. Monolayer WS 2 shows a decrease in Raman and PL intensity post‐FEBIE, with a dose‐to‐clear of ≈2 nC µm −2 . The study reveals regimes affected by the various mass transport contributions such as refresh time and the ratio of electrons/XeF 2 . Spontaneous etching was discovered during FEBIE of large patterned areas due to the long frame/refresh times. Density functional theory and ab initio molecular dynamics simulations compares desorption of SF x and WF x molecules from pristine WS 2 basal planes and pore edges, revealing the spontaneous etching is consistent with etching of partially etched monolayers during each frame. Single‐line etching width of 21 nm, and patterning flakes into 100 nm wide channels are demonstrated. This work demonstrates the possibility of editing WS 2 flakes into electronic devices of arbitrary dimensions for semiconductor applications.

The rapidly developing technology of fusion reactors requires the development of new materials with enhanced radiation resistance. In this study, two new equimolar concentrated solid solution alloys (CSAs), MoTaTiV and MoTaTiVZr, designed for nuclear applications, were synthesized using magnetron sputtering. The MoTaTiV alloy exhibited a columnar microstructure, characteristic for materials produced via magnetron sputtering, and a body-centered cubic (BCC) structure. The addition of Zr to form the quinary MoTaTiVZr alloy led to the formation of an amorphous phase due to increased lattice distortion caused by the introduction of Zr into the crystal structure of the base alloy. To evaluate the irradiation resistance of the synthesized alloys, the materials were irradiated with 200 keV He+ ions. In the case of MoTaTiV, helium bubble formation was initially observed primarily at column boundaries, which serve as preferential nucleation sites. However, at a fluence of 5 x 1016 cm-2, bubbles were also detected within the matrix. For the MoTaTiVZr alloy, due to its amorphous structure, bubbles were uniformly distributed throughout the matrix. Despite these differences in He accumulation behavior, the bubbles in the MoTaTiVZr CSA were slightly smaller, indicating that increasing lattice distortion inhibits bubble growth. Furthermore, the absence of radiation-induced hardening in both materials suggests a high intrinsic resistance to radiation damage.

Lead-free (K0.48Na0.48Li0.04)(Nb1-xTax)O3 (KNLNT-x) ceramics were synthesized to study the effects of Li and Ta substitution on phase transition behavior, microstructure, and ferroelectric, dielectric, and piezoelectric properties. X-ray diffraction and Raman spectroscopy show that compositions with x < 0.10 exhibit a single orthorhombic (Amm2) phase, while 0.10 <= x <= 0.20 show coexistence of orthorhombic and tetragonal (Amm2 + P4mm) phases. For x > 0.20, a single tetragonal (P4mm) phase is obtained. Microstructural analysis shows a dense ceramic with decreasing grain size as Ta concentration increases. Temperature-dependent dielectric studies reveal two transitions: orthorhombic-tetragonal (TO-T) and tetragonal-cubic (TC). Both transition temperatures decrease systematically with increasing Ta, and TO-T shifts below room temperature for x > 0.15. The composition KNLNT-0.20 exhibits the highest dielectric constant (Er = 556) and piezoelectric coefficient (d33 = 159 pC/N). The enhanced piezoelectric response is attributed to a morphotropic phase boundary rather than a shift of the polymorphic phase boundary temperature. A composition-temperature phase diagram was constructed based on XRD, Raman, and dielectric data.

Physical imaging is a foundational characterization method in areas from condensed matter physics and chemistry to astronomy and spans length scales from atomic to universe. Images encapsulate crucial data regarding atomic bonding, materials microstructures, and dynamic phenomena such as microstructural evolution and turbulence, among other phenomena. The challenge lies in effectively extracting and interpreting this information. Variational Autoencoders (VAEs) have emerged as powerful tools for identifying the underlying factors of variation in image data, providing a systematic approach to distilling meaningful patterns from complex data sets. However, a significant hurdle in their application is the definition and selection of appropriate descriptors reflecting local structures. Here, we introduce the scale-invariant VAE approach (SI-VAE) based on the progressive training of the VAE with the descriptors sampled at different length scales. The SI-VAE allows the discovery of the length scale-dependent factors of variation in the system. Here, we illustrate this approach using the ferroelectric domain images and generalize it to the movies of the electron-beam induced phenomena in graphene and topography evolution across combinatorial libraries. This approach can further be used to initialize the decision making in automated experiments including structure–property discovery and can be applied across a broad range of imaging methods. This approach is universal and can be applied to any spatially resolved data including both experimental imaging studies and simulations, and can be particularly useful for exploration of phenomena such as turbulence and scale-invariant transformation fronts.

Combinatorial magnetron sputtering and electrical characterization were used to systematically study the impact of compositional changes in the resistive switching of transition metal oxides, specifically the ZrxTa1-xOy system. Current-voltage behavior across a range of temperatures provided insights into the mechanisms that contribute to differences in the electrical conductivity of the pristine Ta2O5 and ZrO2, and mixed ZrxTa1-xOy devices. The underlying conductive mechanism was found to be a mixture of charge trapping and ionic motion, where charge trapping/emission dictated the short-term cycling behavior while ion motion contributed to changes in the conduction with increased cycling number. ToF-SIMS was used to identify the origin of the "wake-up" behavior of the devices, revealing an ionic motion contribution. This understanding of how cation concentration affects conduction in mixed valence systems helps provide a foundation for a new approach toward manipulating resistive switching in these active layer materials.

Many applications from advanced nuclear reactors to aerospace and automotive industries require materials to operate in extreme environments. In search of new materials that can operate in these extremes, the present work explores this space whereby: (1) guided by atomistic and thermodynamic calculations we utilize thin film combinatorial synthesis to rapidly explore mechanical and thermal properties in a broad range of refractory compositionally complex alloys, and (2) observe transformation induced plasticity via oscillations in the thin film nanoindentation load depth curves that are attributed to, (3) a stress-induced HCP-to-BCC phase transformation in the resulting nanogranular microstructure, which to our knowledge has not been observed before in this alloy system; and finally (4) scale to bulk materials to compare the thin film results.

Since the dawn of scanning probe microscopy (SPM), tapping or intermittent contact mode has been one of the most widely used imaging modes. Manual optimization of tapping mode not only takes a lot of instrument and operator time but also often leads to frequent probe and sample damage, poor image quality, and reproducibility issues for new types of samples or inexperienced users. Despite wide use, optimization of tapping mode imaging is an extremely difficult problem, being ill-suited to both classical control methods and machine learning techniques. Here, we describe a reward-driven workflow to automate the optimization of the SPM in tapping mode. The reward function is defined based on multiple channels with physical and empirical knowledge of good scans encoded, representing a sample-agnostic measure of image quality and imitating the decision-making logic employed by human operators. The workflow determines scanning parameters that produce consistent, high-quality images in attractive modes across various probes and samples. These results demonstrate improved efficiency and reliability in tapping mode SPM operation.