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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Low Repetition-Rate, High-Resolution Femtosecond Transmission Electron Microscopy

Spatial and energy resolutions of state-of-the-art transmission electron microscopes (TEMs) have surpassed 50 pm and 5 meV. However, with respect to the time domain, even the fastest detectors combined with the brightest sources may only be able to reach the microsecond timescale. Thus, conventional methods are incapable of resolving myriad fundamental ultrafast (i.e., attosecond to picosecond) atomic-scale dynamics. The successful demonstration of femtosecond (fs) laser-based (LB) ultrafast transmission electron microscopy (UEM) nearly 20 years ago provided a means to span this nearly 10-order-of-magnitude temporal gap. While nanometer-picosecond UEM studies of dynamics are now well established, ultrafast Å-scale imaging has gone largely unrealized. Further, while instrument development has rightly been an emphasis, and while new modalities and uses of pulsed-beam TEM continue to emerge, the overall chemical and materials application space has been only modestly explored to date. In this Perspectives article, we argue that these apparent shortfalls can be attributed to a simple lack of data and detail. We speculate that present work and continued growth of the field will ultimately lead to the realization that Å-scale fs dynamics can indeed be imaged with minimally modified UEM instrumentation and with repetition rates (f rep ) below - and perhaps even well below - 1 MHz. We further argue that use of low f rep , whether for LB UEM or for chopped/bunched beams, significantly expands the accessible application space. Furthermore, this calls for systematically establishing modality-specific limits so that especially promising technologies can be pursued, thus ultimately facilitating broader adoption as individual instrument capabilities expand.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

PRIMED for the Future: Purposing Raw Intake for Machine Learning-Enabled Detection (Final Report)

The COVID-19 pandemic demonstrated how a novel, elusive, and diffuse biological threat can engender uncertainty and misinformation, and it underscored the need for flexible analytical modalities agnostic to the identity of biological material. Yet even before the pandemic, recognition of the limitations of the current, list-based approach, which focuses on known pathogens and biotoxins, and of the importance of agent-agnostic biodetection was growing within the biosecurity community. In a 2018 report on “Biodefense in the Age of Synthetic Biology,” for example, the National Academy of Sciences stated that “an overreliance on the Select Agent List is a systematic weakness affecting many aspects of the United States’ current biodefense mitigation capability”. More recently, a group of biodefense researchers proposed the identification and adoption of “bioagent-agnostic signatures (BASs)” as a way of detecting and characterizing not only existing agents but also novel ones, an approach they believe will “enable a more flexible and resilient biodefense posture”. Indeed, the future of biodetection requires us to begin developing novel analytics that can identify anomalies and/or characteristics that indicate a potential threat, whether known or unknown, without looking for a specific signature that has been identified previously. To assess potential threats more rapidly, it is critical to develop agnostic artificial intelligence (AI)/machine learning (ML) systems that can be employed for real-time assessment of the nature and source of a perturbation. Such systems should be multi scale and multi-dimensional, integrating sensor data from a range of biological, chemical, and physical application spaces. Emerging deep learning (DL) models demonstrate exceptional promise for identification of discriminatory features within multi-dimensional datasets. DL models have the capacity to recognize and encode highly complex patterns in a wide range of input data modalities, including images, text, and biological/chemical/physical spectra. As such, they can execute a wide range of assessments and determinations that have traditionally required a human operator. The promise of advances in DL is apparent in the realm of human health and medicine. DL models have been validated for evaluating a variety of clinical threats to human health in a range of contexts, including infection and cancer, and they demonstrated improved performance in predicting stroke relative to human neurologists in some categories of data. Continuously evolving advances in AI/ML are expected to support more efficient evaluation of raw sequence, spectroscopy, and spectrometry data. For instance, recent advances and deployment of large language models (LLM) such as Generative Pre training Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) have already motivated application of these models for biological function prediction. As frameworks such as LLMs become larger and more complex in their representations, their capacity to serve as pre-trained models that can be fine-tuned for biological/biodetection purposes will similarly be amplified. While existing and emerging AI/ML have found broad applicability and use cases in the clinical sciences, development for environmental evaluation and biodetection has been limited. Functionalizing such capabilities for this purpose requires an understanding of the existing technical landscape and how the respective tools and algorithms are currently being employed. This landscape awareness then allows an assessment of the current practical capabilities of existing models and the anticipated requirements and development efforts that will be needed to adapt available algorithms for biodetection applications relevant to DHS. Leveraging expertise in biodetection, ML, and operational biodetection, the effort described in this report is comprised of a systematic landscape assessment (Subtask 2.1), comparative evaluation (Subtask 2.2), and formulation of a value proposition (Subtask 2.3) for the prospect of ML-enabled, agnostic biodetection from raw, or minimally-processed, datasets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

PRIMED for the Future: Purposing Raw Intake for Machine Learning-Enabled Detection

The COVID-19 pandemic demonstrated how a novel, elusive, and diffuse biological threat can engender uncertainty and misinformation, and it underscored the need for flexible analytical modalities agnostic to the identity of biological material. Yet even before the pandemic recognition of the limitations of the current, list-based approach, which focuses on known pathogens and biotoxins, and of the importance of agent-agnostic biodetection, was growing within the biosecurity community. In a 2018 report on “Biodefense in the Age of Synthetic Biology,” for example, the National Academy of Sciences stated that “an overreliance on the Select Agent List is a systematic weakness affecting many aspects of the United States’ current biodefense mitigation capability." More recently, a group of biodefense researchers proposed the identification and adoption of “bioagent-agnostic signatures (BASs)” as a way of detecting and characterizing not only existing agents but also novel ones, an approach they believe will “enable a more flexible and resilient biodefense posture." Indeed, the future of biodetection requires us to begin developing novel analytics that can identify anomalies and/or characteristics that indicate a potential threat, whether known or unknown, without looking for a specific signature that has been identified previously. To assess potential threats more rapidly, it is critical to develop agnostic artificial intelligence (AI)/machine learning (ML) systems that can be employed for real-time assessment of the nature and source of a perturbation. Such systems should be multiscale and multi-dimensional, integrating sensor data from a range of biological, chemical, and physical application spaces. Emerging deep learning (DL) models demonstrate exceptional promise for identification of discriminatory features within multi-dimensional datasets. DL models have the capacity to recognize and encode highly complex patterns in a wide range of input data modalities, including images, text, and biological/chemical/physical spectra. As such, they can execute a wide range of assessments and determinations that have traditionally required a human operator.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications (Full Report)

The Modular Autonomous Research System (MARS) was developed to address the pressing need for faster, more reliable, and more adaptable scientific discovery. Traditional experimentation is limited by manual labor, long cycle times, and fragmented data streams, which constrain the ability to explore complex chemical and materials design spaces. To overcome these limitations, we created an integrated, modular platform that combines laboratory robotics, diverse measurement instruments, and a central data infrastructure with artificial intelligence–driven decision-making. The system links liquid handling robots, robotic arms, and optical plate readers into a closed loop where experiments are executed automatically, data is analyzed in real time, and subsequent experimental conditions are adaptively chosen to maximize information gain. Over the course of the project, MARS was validated on two primary test cases—spectroscopic metal–ligand binding assays and peptide-directed mineralization—which highlighted the system’s ability to handle uncertainty and variability in experimental measurements. To further demonstrate modularity and extensibility, we also established additional testbeds in electrochemistry for catalyst discovery and electrolyte formulation for advanced batteries. The results show that MARS can reliably conduct autonomous campaigns with minimal human intervention, adapt to distinct scientific domains, and provide a scalable model for future self-driving laboratories. This work establishes new capabilities for modular, uncertainty-aware automation and directly supports the need for advanced, data-driven research platforms capable of accelerating discovery across a wide range of scientific and national security missions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Electrochemical mechanisms of leakage‐current in photovoltaic modules

Abstract This paper analyzes the mechanisms and pathways for leakage current flow observed in Si photovoltaic modules subjected to high temperature and humidity and a large voltage bias with respect to ground. The current inside of the frame is in the form of electron motion, but in the glass and polymer, it is in at least a large part attributable to the movement of ions. When the mode of current flow changes from electronic to ionic conduction, electrochemical reactions will take place at the interface. This can include reactions that produce volatile chemical species like H 2 , CO x , and O 2 , along with ionic species such as OH − and H 3 O + . Here, we see evidence of the importance of different charge carriers with different diffusion rates and the influence of electrochemical processes involved. The application of negative voltage to the cell circuit affects the resistivity of glass producing surfaces with poor conductivity but with some increases in the electrochemical potential producing complicated interactions that are important when the voltage is changed. In the polymer, there is the development of a space charge region and a chemical gradient providing two oppositional forces to current flow, which when released create a complicated discharge process. Here, we give a basic understanding of the charge/discharge of PV cells highlighting how the specific mechanisms are important in understanding some of the degradation processes in PV modules. We find that there is evidence of multiple significant charge carrier species with different diffusion time scales. The glass/polymer interface forms a depleted region of higher resistance after prolonged exposure to current. Charge also builds up at the polymer to cell antireflective coating interface and mostly flows to the gridlines to experience electrochemical reactions. These complexities result in non‐linear behavior where the apparent resistivities of the different layers change during charge/discharging processes, making the modeling of the current flow extremely difficult.

14 SOLAR ENERGY↗
DOE OSTI2024

How Well Can Quantum Embedding Method Predict the Reaction Profiles for Hydrogenation of Small Li Clusters?

Quantum computing leverages the principles of quantum mechanics in novel ways to tackle complex chemistry problems that cannot be accurately addressed using traditional quantum chemistry methods. However, the high computational cost and available number of physical qubits with high fidelity limit its application to small chemical systems. This work employed a quantum-classical framework which features a quantum active space-embedding approach to perform simulations of chemical reactions that require up to 14 qubits. This framework was applied to prototypical example metal hydrogenation reactions: the coupling between hydrogen and Li 2 , Li 3 , and Li 4 clusters. Particular attention was paid to the computation of barriers and reaction energies. The predicted reaction profiles compare well with advanced classical quantum chemistry methods, demonstrating the potential of the quantum embedding algorithm to map out reaction profiles of realistic gas-phase chemical reactions to ascertain qualitative energetic trends. Additionally, the predicted potential energy curves provide a benchmark to compare against both current and future quantum embedding approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Optimal 3D chemical imaging with multimodal electron tomography

Accurate mapping of nanoscale chemistry in three dimensions (3D) has been a longstanding challenge. Modern electron microscopy provides chemical images by electron energy loss spectroscopy (EELS) and energy dispersive x-ray spectrometry (EDX) but requires high fluences that damage specimens. In 3D, the requirements are worse; electron tomography demands many high-fluence chemical maps for reconstruction, creating a tradeoff between resolution, accuracy, and sample survival. Fused multimodal electron tomography (MM-ET) alleviates this requirement by leveraging lower-fluence high-angle annular dark-field (HAADF) images alongside a few chemical maps to dramatically improve chemical resolution. Here, experimental and computational parameter space is systematically explored to determine when MM-ET performs best. Ideal imaging conditions balance sample survival with resolution and chemical specificity; we recommend a tilt range of at least ± 70°, acquiring 40 equally spaced HAADF projections (signal-to-noise > 10), and 7 EELS/EDX maps of each chemistry (signal-to-noise > 4).

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dynamic development of geochemical reaction fronts during hydraulic stimulation of shale

Injection of acidic hydraulic fracture fluid (HFF) into shale formations for unconventional oil/gas production results in chemical reactions in the shale matrix that can alter fluid transport. Here, we report the results of set of experiments designed to evaluate the impact of calcite dissolution as a function of carbonate mineral content on matrix chemical reactivity and pore-space modification concomitant with imbibition. In this study, we tracked acidic HFF transport in four samples of Wolfcamp shale with calcite contents varying from 4% to 59% by monitoring the rate and spatial extent of bromide tracer transport using synchrotron-based X-ray fluorescence microprobe (XFM) imaging. Concurrently, we also carried out XFM imaging of the spatial distribution of Ca in the Wolfcamp shale cores (as a proxy of calcite distribution). Our approach thus yields a direct record of time-resolved selective ion transport resulting from the penetration of acidic HFF and the associated mineral transformations in the shale cores. We show that the variability in calcite content of Wolfcamp shale samples can directly affect the rate and spatial extent of imbibition. Although reaction of the acidic HFF with carbonates in shales enhances calcite dissolution and increases porosity, the spatial extent of calcite dissolution in the shale matrix is limited due to a rapid neutralization of pH. The relative abundance and spatial distribution of calcite control the chemical saturation state of the HFF progressing into the matrix. As a result, calcite has a major impact on the spatial extent and rate of matrix alteration and thus on HFF transport during subsurface reservoir stimulation. Consequently, increased calcite content in the shale matrix inhibits the spatial extent of the pore-volume increase and, by extension, the spatial extent and rate of imbibition. Our results thus show that the overall rates of calcite dissolution approach the rates of acidic HFF transport (i.e., Damköhler number ~1), which could contribute to the efficiency of subsurface reservoir stimulation. A better understanding of HFF-calcite reaction rates is crucial for improving the prediction and optimization of fluid transport across HFF-shale interfaces during hydraulic fracturing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Localized Active Space Pair-Density Functional Theory

Accurate quantum chemical methods for the prediction of spin-state energy gaps for strongly correlated systems are computationally expensive and scale poorly with the size of the system. This makes calculations for many experimentally interesting molecules impractical even with abundant computational resources. Previous work has shown that the localized active space (LAS) self-consistent field (SCF) method can be an efficient way to obtain multiconfiguration SCF wave functions of comparable quality to the corresponding complete active space (CAS) ones. To obtain quantitative results, a post-SCF method is needed to estimate the complete correlation energy. One such method is multiconfiguration pair-density functional theory (PDFT), which calculates the energy based on the density and on-top pair density obtained from a multiconfiguration wave function. In this work, we introduce localized-active-space PDFT, which uses a LAS wave function for subsequent PDFT calculations. The method is tested by computing spin-state energies and gaps in conjugated organic molecules and a bimetallic compound and comparing to the corresponding CAS-PDFT values.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Corrosion Engineering of Part‐Per‐Million Single Atom Pt 1 /Ni(OH) 2 Electrocatalyst for PET Upcycling at Ampere‐Level Current Density

The plastic waste issue has posed a series of formidable challenges for the ecological environment and human health. While conventional recycling strategies often lead to plastic down-cycling, the electrochemical strategy of recovering valuable monomers enables an ideal, circular plastic economy. Here a corrosion synthesized single atom Pt 1 /Ni(OH) 2 electrocatalyst with part-per-million noble Pt loading for highly efficient and selective upcycling of polyethylene terephthalate (PET) into valuable chemicals (potassium diformate and terephthalic acid) and green hydrogen is reported. Electro-oxidation of PET hydrolysate, ethylene glycol (EG), to formate is processed with high Faraday efficiency (FE) and selectivity (>90%) at the current density close to 1000 mA cm −2 (1.444 V vs RHE). The in situ spectroscopy and density functional theory calculations provide insights into the mechanism and the understanding of the high efficiency. Remarkably, the electro-oxidation of EG at the ampere-level current density is also successfully illustrated by using a membrane-electrode assembly with high FEs to formate integrated with hydrogen production for 500 h of continuous operation. In conclusion, this process allows valuable chemical production at high space-time yield and is highly profitable (588–700 $\$$ ton −1 PET), showing an industrial perspective on single-atom catalysis of electrochemical plastic upcycling.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Leveraging computational thermodynamics to guide SiC-ZrC chemical vapor deposition process development

Here using the CALPHAD approach to understand zirconium carbide deposition, a series of phase equilibria were calculated from a custom thermodynamic database based on a literature source, and the equilibria were used to explore the potential chemical vapor deposition (CVD) processing space in the ZrCl 4 -CH 3 SiCl 3 -CH 4 -H 2 system as a function of pressure, temperature, and gas composition. Several gas ratios were considered. At a given ZrCl 4 :CH 3 SiCl 3 ratio within the range studied, the most important factor was found to be the ratios of CH 4 :ZrCl 4 , wherein the nature of the composition – carbide vs. silicide – could be controlled. A pure binary composition of ZrC and SiC is expected to form by increasing the initial amount of methane and decreasing the amount of hydrogen from values predicted purely based on thermodynamic equilibrium. Rietveld analysis of the x-ray diffractograms from corresponding experimental depositions confirmed that increasing the CH 4 :ZrCl 4 ratio increased the fraction of carbon-containing species (SiC, ZrC) and decreased the fraction of non-carbides (ZrSi, ZrSi 2 , etc.), as predicted from the CALPHAD results.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Operational and Mission Highlights A Monthly Summary of Top Achievements

In September 2020, the Laboratory’s Chemical Microscopy Facility tripled in size, expanding to 1,000 square feet. Located at TA-46, Building158, this facility houses a unique Laboratory resource. Operated by the Physical Chemistry and Applied Spectroscopy (C-PCS) group, this facility was established to renew the Laboratory’s rich history in radiobiology. The facility combines biological research capabilities with isotope production, as well as radioanalytical and chemical infrastructure. This facility provides space for Biosafety Level 2 (BSL-2) research, with alpha-emitting and other radionuclides of interest associated with biological systems. Equipped with a Class ll/A2 biological safety cabinet, a radiological fume hood and a host of other analytical equipment, this facility serves an effective resource to evaluate the efficacy of targeted alpha therapeutics, as well as the deleterious effects of radiological materials on living organisms. The facility receives funding through LDRD- 20180005DR, “Establishing a Radiotherapeutic Capability to Counter Biothreats,” and the BES Heavy Element Chemistry Program at the Laboratory.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2026

Ultrafast x-ray imaging of coherently controlled molecular dynamics in real space and time

Coherent control aims to manipulate chemical processes on the latent length and timescales of atoms and bonds, scales that are intrinsic to molecular dynamics but not directly resolved by most experimental probes. As a result, existing coherent-control experiments, which overwhelmingly rely on spectroscopic observables, leave a crucial blind spot: the direct, real-space recovery of all atomic and molecular rearrangements in response to coherently controlled excitations. Here, we overcome this limitation by integrating a Tannor-Kosloff-Rice pump-control-probe scheme with ultrafast X-ray scattering to capture snapshots of the wavepacket motion in a benchmark molecular system. We demonstrate this by photoexciting diatomic iodine vapor with a visible pump pulse, selectively steering the wavepacket toward ground-state recombination or dissociative pathways with a time-delayed near-IR control pulse, and recording the dynamics with angstrom and femtosecond precision with an ultrashort hard X-ray probe pulse. By comparing these structural observations with numerical solutions of the time-dependent Schrödinger equation, we reveal how coherent control actively reshapes the molecular charge density distribution. Our results pave the way for leveraging structural feedback as a control handle and provide a fundamental microscopic visualization of quantum decoherence and energy redistribution at the atomic level.

Hopper, Thomas R. [SLAC National Accelerator Labor↗
DOE OSTI2024

Jacobian-scaled K-means clustering for physics-informed segmentation of reacting flows

This work introduces Jacobian-scaled K-means (JSK-means) clustering, which is a physicsinformed clustering strategy centered on the K-means framework. The method allows for the injection of underlying physical knowledge into the clustering procedure through a distance function modification: instead of leveraging conventional Euclidean distance vectors, the JSKmeans procedure operates on distance vectors scaled by matrices obtained from dynamical system Jacobians evaluated at the cluster centroids. The goal of this work is to show how the JSKmeans algorithm - without modifying the input dataset - produces clusters that capture regions of dynamical similarity, in that the clusters are redistributed towards high-sensitivity regions in phase space and are described by similarity in the source terms of samples instead of the samples themselves. The algorithm is demonstrated on a complex reacting flow simulation dataset (a channel detonation configuration), where the dynamics in the thermochemical composition space are known through the highly nonlinear and stiff Arrhenius-based chemical source terms. Interpretations of cluster partitions in both physical space and composition space reveal how JSK-means shifts clusters produced by standard K-means towards regions of high chemical sensitivity (e.g., towards regions of peak heat release rate near the detonation reaction zone). Furthermore, the findings presented here illustrate the benefits of utilizing Jacobian-scaled distances in clustering techniques, and the JSK-means method in particular displays promising potential for improving former partition-based modeling strategies in reacting flow (and other multi-physics) applications.

Clustering↗
DOE OSTI2023

Unsupervised machine learning discovery of structural units and transformation pathways from imaging data

We show that unsupervised machine learning can be used to learn chemical transformation pathways from observational Scanning Transmission Electron Microscopy (STEM) data. To enable this analysis, we assumed the existence of atoms, a discreteness of atomic classes, and the presence of an explicit relationship between the observed STEM contrast and the presence of atomic units. With only these postulates, we developed a machine learning method leveraging a rotationally invariant variational autoencoder (VAE) that can identify the existing molecular fragments observed within a material. The approach encodes the information contained in STEM image sequences using a small number of latent variables, allowing the exploration of chemical transformation pathways by tracing the evolution of atoms in the latent space of the system. The results suggest that atomically resolved STEM data can be used to derive fundamental physical and chemical mechanisms involved, by providing encodings of the observed structures that act as bottom-up equivalents of structural order parameters. The approach also demonstrates the potential of variational (i.e., Bayesian) methods in the physical sciences and will stimulate the development of more sophisticated ways to encode physical constraints in the encoder–decoder architectures and generative physical laws and causal relationships in the latent space of VAEs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Nonadiabatic Dynamics in a Continuous Circularly Polarized Laser Field with Floquet Phase-Space Surface Hopping

Non-adiabatic chemical reactions involving continuous circularly polarized light (cw CPL) have not attracted as much attention as dynamics in unpolarized/linearly polarized light. However, including circularly (in contrast to linearly) polarized light allows one to effectively introduce a complex-valued time-dependent Hamiltonian, which offers a new path for control or exploration through the introduction of Berry forces. Here, we investigate several inexpensive semiclassical approaches for modeling such nonadiabatic dynamics in the presence of a time-dependent complex-valued Hamiltonian, beginning with a straightforward instantaneous adiabatic fewest-switches surface hopping (IA-FSSH) approach (where the electronic states depend on position and time), continuing to a standard Floquet fewest switches surface hopping (F-FSSH) approach (where the electronic states depend on position and frequency), and ending with an exotic Floquet phase-space surface hopping (F-PSSH) approach (wherethe electronic states depend on position, frequency, and momentum). Using a set of model systems with time-dependent complex-valued Hamiltonians, we show that the Floquet phase- space adiabats are the optimal choice of basis as far as accounting for Berry phase effects and delivering accuracy. Furthermore, the F-PSSH algorithm sets the stage for modeling nonadiabatic dynamics under strong externally pumped circular polarization in the future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Martensitic Transformation in Fe x Mn 80-x Co 10 Cr 10 High-Entropy Alloy

High-entropy alloys and even medium-entropy alloys are an intriguing class of materials in that structure and property relations can be controlled via alloying and chemical disorder over wide ranges in the composition space. Employing density-functional theory combined with the coherent-potential approximation to average over all chemical configurations, we tune free energies between face-centered-cubic and hexagonal-close-packed phases in Fe x Mn 80 - x Co 10 Cr 10 systems. Within Fe-Mn-based alloys, we show that the martensitic transformation and chemical short-range order directly correlate with the face-centered-cubic and hexagonal-close-packed energy difference and stacking-fault energies, which are in quantitative agreement with recent observation of two phase region (face-centered cubic and hexagonal closed pack) in a polycrystalline high-entropy alloy sample at x = 40 at.%. Our predictions are further confirmed by single-crystal measurements on a x = 40 at.% using transmission-electron microscopy, selective-area diffraction, and electron-backscattered-diffraction mapping. The results herein offer an understanding of transformation-induced or twinning-induced plasticity in this class of high-entropy alloys and a design guide for controlling the physics at the electronic level.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗