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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Phenomenological model of the third-harmonic magnetic response due to superconducting fluctuations: Application to Sr 2 RuO 4

Here, we employ the phenomenological Lawrence-Doniach model to compute the contributions of the superconducting fluctuations to the third-harmonic magnetic response, denoted here by $\bar{M_3}$, which can be measured in a precise way using ac magnetic fields and lock-in techniques. We show that, in an intermediate temperature regime, this quantity behaves as the third-order nonlinear susceptibility, which shows a power-law dependence with the reduced temperature ε = $\frac{T-T_c}{T_c}$ as ε -5/2 . Very close to T c , however, $\bar{M_3}$ saturates due to the nonzero amplitude of the ac field. We compare our theoretical results with experimental data for three conventional superconductors—lead, niobium, and vanadium—and for the unconventional superconductor Sr 2 RuO 4 (SRO). We find good agreement between theory and experiment for the elemental superconductors, although the theoretical values for the critical field systematically deviate from the experimental ones. In the case of SRO, however, the phenomenological model completely fails to describe the data, as the third-harmonic response remains sizable over a much wider reduced temperature range compared to Pb, Nb, and V. We show that an inhomogeneous distribution of T c across the sample can partially account for this discrepancy, since regions with a locally higher T c contribute to the fluctuation $\bar{M_3}$ significantly more than regions with the “nominal” T c of the clean system. However, the exponential temperature dependence of $\bar{M_3}$ first reported by Pelc et al. [Nat. Commun. 10, 2729 (2019)] is not captured by the model with inhomogeneity. We conclude that, while inhomogeneity is an important ingredient to understand the superconducting fluctuations of SRO and other perovskite superconductors, additional effects may be at play, such as non-Gaussian fluctuations or rare-region effects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Comparative Analysis via CFD Simulation on the Impact of Graphite Anode Morphologies on the Discharge of a Lithium-Ion Battery

The morphology of electrode materials plays a crucial role in determining the performance of lithium-ion batteries. Traditional computational models often simplify graphite flakes as uniformly sized spheres, which limits their predictive accuracy. In this study, we present a computational workflow that overcomes these limitations by incorporating a more realistic representation of graphite morphologies. This workflow is designed to be flexible and reproducible, enabling efficient evaluation of electrochemical performance across diverse material structures. By exploring different graphite morphologies, our approach accelerates the optimization of material preparation techniques and processing conditions. Our findings reveal that incorporating greater morphological complexity leads to significant deviations from classical model predictions. Instead, our refined model offers a more accurate representation of battery discharge behavior, closely aligning with experimental data. This improvement underscores the importance of detailed morphological descriptions in advancing battery design and performance assessments. To promote accessibility and reproducibility, we provide the developed code for seamless integration with the COMSOL API, allowing researchers to implement and adapt it easily. This computational framework serves as a valuable tool for investigating the impact of graphite morphology on battery performance, bridging the gap between theoretical modeling and experimental validation to enhance lithium-ion battery technology.

25 ENERGY STORAGE↗
DOE OSTI2023

Kernel-based global sensitivity analysis obtained from a single data set

Results from global sensitivity analysis (GSA) often guide the understanding of complicated input–output systems. Kernel-based GSA methods have recently been proposed for their capability of treating a broad scope of complex systems. In this paper, we develop a new set of kernel GSA tools when only a single set of input–output data is available. Three key advances are made: (1) A new numerical estimator is proposed that demonstrates an empirical improvement over previous procedures. (2) A computational method for generating inner statistical functions from a single data set is presented. (3) A theoretical extension is made to define conditional sensitivity indices, which reveal the degree that the inputs carry shared information about the output when inherent input–input correlations are present. Utilizing these conditional sensitivity indices, a decomposition is derived for the output uncertainty based on what is called the optimal learning sequence of the input variables, which remains consistent when correlations exist between the input variables. Further, while these advances cover a range of GSA subjects, a common single data set numerical solution is provided by a technique known as the conditional mean embedding of distributions. The new methodology is implemented on benchmark systems to demonstrate the provided insights.

42 ENGINEERING↗
DOE OSTI2022

Coupled Electronic and Nuclear Motions during Azobenzene Photoisomerization Monitored by Ultrafast Electron Diffraction

Ultrafast electron diffraction is a powerful technique that can resolve molecular structures with femtosecond and angstrom resolutions. We demonstrate theoretically how it can be used to monitor conical intersection dynamics in molecules. Specific contributions to the signal are identified which vanish in the absence of vibronic coherence and offer a direct window into conical intersection paths. A special focus is on hybrid scattering from nuclei and electrons, a process that is unique to electron (rather than X-ray) diffraction and monitors the strongly coupled nuclear and electronic motions in the vicinity of conical intersections. Furthermore, an application is made to the cis to trans isomerization of azobenzene, computed with exact quantum dynamics wavepacket propagation in a reactive two-dimensional nuclear space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Come for predictions, stay for complexity: synthesis and experimental probing of ionic conductivity in Li 9 B 19 S 33

Lithium thioborates, despite their potential cost-effectiveness and low density, have received considerably less attention as solid electrolytes compared to their thiophosphate counterparts. A primary obstacle to their widespread investigation has been the inherent challenge in synthesizing single-phase materials. Computational studies have predicted several lithium thioborate phases exhibiting high ionic conductivity, with Li 9 B 19 S 33 notably predicted to reach 80 mS cm −1 . However, experimental validation of these theoretical predictions remains absent. This work addresses this gap by detailing a successful synthesis of the previously elusive Li 9 B 19 S 33 phase, facilitated by in situ temperature dependent powder X-ray diffraction. Our findings reveal the peritectic nature of phase formation, necessitating an excess of boron sulfide in the reaction mixture. We further present a comprehensive structural characterization of Li 9 B 19 S 33 utilizing spectroscopic techniques like NMR, FT-IR, and diffuse reflectance and report on its ionic conductivity. Solid-state 6 Li NMR line narrowing experiments revealed an ion mobility activation energy of 0.26 eV whereas activation energies derived from impedance spectroscopy measurements were significantly higher, resulting in lower than theoretically predicted ionic conductivity.

Oppong, Richeal A. [Iowa State Univ., Ames, IA (Un↗
DOE OSTI2025

Resolving Lonsdaleite's decade-long controversy: Atomistic insights into a metastable diamond polymorph

Lonsdaleite, a theoretically proposed hexagonal diamond polymorph, has remained at the center of a five-decade scientific controversy since its 1967 identification. While some studies claim it exhibits superior hardness through compression-induced structural changes, others contend it is merely a stacking-faulted cubic diamond. Meteoritic samples and synthetic preparations have yielded conflicting evidence, with even advanced characterisation techniques like XRD and TEM failing to provide definitive proof. In this work, we employ first-principles density functional theory (DFT) and molecular dynamics (MD) simulations to generate unambiguous theoretical fingerprints through XRD, Raman, and SAED patterns that distinguish true Lonsdaleite from cubic diamond and its defective variants. Our atomistic approach quantifies the thermodynamic metastability of Lonsdaleite under realistic pressure-temperature conditions, reveals distinct spectral signatures through simulated Raman and resolves the structural ambiguity through generalised stacking fault energy analysis. By establishing clear criteria for definitive identification, this study provides long-awaited clarity to the Lonsdaleite debate while offering a robust computational framework for characterising metastable carbon phases in meteoritic, synthetic and industrial materials.

DFT↗
DOE OSTI2020

Retrieval of energy spectra for all flavours of neutrinos from core-collapse supernova with multiple detectors

ABSTRACT We present a new method by which to retrieve energy spectrum for all falvours of neutrinos from core-collapse supernova (CCSN). In the retrieval process, we do not assume any analytic formulas to express the energy spectrum of neutrinos but rather take a direct way of spectrum reconstruction from the observed data; the singular value decomposition algorithm with a newly developed adaptive energy-gridding technique is adopted. We employ three independent reaction channels having different flavour sensitivity to neutrinos. Two reaction channels, inverse beta decay on proton and elastic scattering on electrons, from a water Cherenkov detector such as Super-Kamiokande (SK) and Hyper-Kamiokande (HK), and a charged current reaction channel with Argon from the Deep Underground Neutrino Experiment (DUNE) are adopted. Given neutrino oscillation models, we iteratively search the neutrino energy spectra at the CCSN source until they provide the consistent event counts in the three reaction channels. We test the capability of our method by demonstrating the spectrum retrieval to a theoretical neutrino data computed by our recent three-dimensional CCSN simulation. Although the energy spectrum with either electron-type or electron-type antineutrinos at the CCSN source has relatively large error compared to that of other species, the joint analysis with HK + DUNE or SK + DUNE will provide precise energy spectrum of all flavours of neutrinos at the source. Finally, we discuss perspectives for improvements of our method by using neutrino data of other detectors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Thermal transport in defective and disordered materials

With significant recent advancements in thermal sciences—such as the development of new theoretical and experimental techniques, and the discovery of new transport mechanisms—it is helpful to revisit the fundamentals of vibrational heat conduction to formulate an updated and informed physical understanding. The increasing maturity of simulation and modeling methods sparks the desire to leverage these techniques to rapidly improve and develop technology through digital engineering and multi-scale, electro-thermal models. With that vision in mind, this review attempts to build a holistic understanding of thermal transport by focusing on the often unaddressed relationships between subfields, which can be critical for multi-scale modeling approaches. For example, we outline the relationship between mode-specific (computational) and spectral (analytical) models. We relate thermal boundary resistance models based on perturbation approaches and classic transmissivity based models. We discuss the relationship between lattice dynamics and molecular dynamics approaches along with two-channel transport frameworks that have emerged recently and that connect crystal-like and amorphous-like heat conduction. Throughout, we discuss best practices for modeling experimental data and outline how these models can guide material-level and system-level design.

42 ENGINEERING↗
DOE OSTI2025

Mechanical sensors for ultraheavy dark matter searches via long-range forces

Dark matter candidates with masses around the Planck scale are theoretically well-motivated, and it has been suggested that it might be possible to search for dark matter solely via gravitational interactions in this mass range. In this work, we explore the pathway towards searching for dark matter candidates with masses around the Planck scale using mechanical sensors while considering realistic experimental constraints, and develop analysis techniques needed to conduct such searches. These dark matter particles are expected to leave tracks as their signature in mechanical sensor arrays, and we show that we can effectively search for such tracks using statistical approaches to track-finding. We analyze a range of possible experimental setups and compute sensitivity projections for searches for ultraheavy dark matter coupling to the Standard Model via long-range forces. We find that while a search for Planck-scale dark matter purely via gravitational couplings would be exceedingly difficult, requiring ∼80 dB of quantum noise reduction with a 100 3 array of devices, there is a wide range of currently unexplored dark matter candidates which can be searched for with already existing or near-term experimental platforms.

Qin, Juehang [Rice Univ., Houston, TX (United Stat↗
DOE OSTI2021

Analysis of the SBP-SAT Stabilization for Finite Element Methods Part II: Entropy Stability

In the hyperbolic research community, there exists the strong belief that a continuous Galerkin scheme is notoriously unstable and additional stabilization terms have to be added to guarantee stability. In the first part of the series, the application of simultaneous approximation terms for linear problems is investigated where the boundary conditions are imposed weakly. By applying this technique, the authors demonstrate that a pure continuous Galerkin scheme is indeed linearly stable if the boundary conditions are imposed in the correct way. In this work, we extend this investigation to the nonlinear case and focus on entropy conservation. Here, by switching to entropy variables, we provide an estimation of the boundary operators also for nonlinear problems, that guarantee conservation. In numerical simulations, we verify our theoretical analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Randomized Algorithms for Symmetric Nonnegative Matrix Factorization

Symmetric Nonnegative Matrix Factorization (SymNMF) is a technique in data analysis and machine learning that approximates a matrix with a product of a nonnegative, low-rank matrix and it transpose. To design faster and more scalable algorithms for SymNMF we develop two randomized algorithms for its computation. The first method uses randomized matrix sketching to compute an initial low-rank approximation to the input matrix and proceeds to uses this as a low-rank input to rapidly compute a SymNMF. The second methods uses randomized leverage score sampling to approximately solve constrained least squares problems. Many successful methods for SymNMF rely on (approximately) solving sequences of constrained least squares problems. Here, we prove theoretically that leverage score sampling can approximately solve constrained least squares problems to e-accuracy. Finally we demonstrate both methods work in practice by applying them to graph clustering tasks on large real world data sets. These experiments show that our methods approximately maintain solution quality and achieve significant speed ups for both large dense and large sparse problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Three-Photon Optical Pumping for Trapped Ion Quantum Computing

In this report we describe the testing of a novel scheme for state preparation of trapped ions in a quantum computing setup. This technique optimally would allow for similar precision and speed of state preparation while allowing for individual addressability of single ions in a chain using technology already available in a trapped ion experiment. As quantum computing experiments become more complicated, mid-experiment measurements will become necessary to achieve algorithms such as quantum error correction. Any mid-experiment measurement then requires the measured qubit to be re-prepared to a known quantum state. Currently this involves the protected qubits to be moved a sizeable distance away from the qubit being re-prepared which can be costly in terms of experiment length as well as introducing errors. Theoretical calculations predict that a three-photon process would allow for state preparation without qubit movement with similar efficiencies to current state preparation methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Penalized ensemble Kalman filters for high dimensional non-linear systems

The ensemble Kalman filter (EnKF) is a data assimilation technique that uses an ensemble of models, updated with data, to track the time evolution of a usually non-linear system. It does so by using an empirical approximation to the well-known Kalman filter. However, its performance can suffer when the ensemble size is smaller than the state space, as is often necessary for computationally burdensome models. This scenario means that the empirical estimate of the state covariance is not full rank and possibly quite noisy. To solve this problem in this high dimensional regime, we propose a computationally fast and easy to implement algorithm called the penalized ensemble Kalman filter (PEnKF). Under certain conditions, it can be theoretically proven that the PEnKF will be accurate (the estimation error will converge to zero) despite having fewer ensemble members than state dimensions. Further, as contrasted to localization methods, the proposed approach learns the covariance structure associated with the dynamical system. These theoretical results are supported with simulations of several non-linear and high dimensional systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

The Schwarz Alternating Method for the Seamless Coupling of Nonlinear Reduced Order Models and Full Order Models

Projection-based model order reduction allows for the parsimonious representation of full order models (FOMs), typically obtained through the discretization of a set of partial differential equations (PDEs) using conventional techniques (e.g., finite element, finite volume, finite difference methods) where the discretization may contain a very large number of degrees of freedom. As a result of this more compact representation, the resulting projection-based reduced order models (ROMs) can achieve considerable computational speedups, which are especially useful in real-time or multi-query analyses. One known deficiency of projection-based ROMs is that they can suffer from a lack of robustness, stability and accuracy, especially in the predictive regime, which ultimately limits their useful application. Another research gap that has prevented the widespread adoption of ROMs within the modeling and simulation community is the lack of theoretical and algorithmic foundations necessary for the “plug-and-play” integration of these models into existing multi-scale and multi-physics frameworks. This paper describes a new methodology that has the potential to address both of the aforementioned deficiencies by coupling projection-based ROMs with each other as well as with conventional FOMs by means of the Schwarz alternating method [41]. Leveraging recent work that adapted the Schwarz alternating method to enable consistent and concurrent multiscale coupling of finite element FOMs in solid mechanics [35, 36], we present a new extension of the Schwarz framework that enables FOM-ROM and ROM-ROM coupling, following a domain decomposition of the physical geometry on which a PDE is posed. In order to maintain efficiency and achieve computation speed-ups, we employ hyper-reduction via the Energy-Conserving Sampling and Weighting (ECSW) approach [13]. We evaluate the proposed coupling approach in the reproductive as well as in the predictive regime on a canonical test case that involves the dynamic propagation of a traveling wave in a nonlinear hyper-elastic material.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Exploring Nontrivial Topological Superconductivity in 2M-WS2 for Topological Quantum Computation

This project has two main research goals: (1) growing the high-quality two-dimensional (2D) 2M-pahse WS 2 (2M-WS 2 ) single crystals and identifying clear signatures of the unconventional superconductivity in the 2M-WS 2 ; and (2) establishing the layer-dependence of the Majorana zero mode in the 2M-WS 2 down to the monoatomic layer limit. These goals were planned to be achieved by growing high-quality and large-scale 2M-WS 2 single crystals and transferring their thin layers onto different substrates for the proposed measurements. The layer-dependent unconventional superconductivity in 2M-WS 2 were systematically studied by different techniques, including transport measurements (charge, thermal and spin), scanning tunneling microscopy and spectroscopy (STM/S), angle-resolved photoemission spectroscopy (ARPES), and theoretical calculations. The research team is comprised of researchers from University of Wyoming (UW) and three DOE National Laboratories (DOE NLs), including Argonne National Laboratory (ANL), Lawrence Berkeley National Laboratory (LBNL) and Sandia National Laboratories (SNL), with complete and complementary expertise: PI Tian: Handling 2D materials, nanofabrication, nanodevices, and quantum transport; Co-Is: Ackerman and Leonard: van der Waals material crystal growth and handling; Chien: Nanoimaging with STM/S; and Tang: Magnetic measurements and charge and thermal transport; National lab collaborators (NLs): Guisinger (ANL): STM/S and nanoimaging; Mo and Rotenberg (LBNL): ARPES and nano ARPES (nARPES); Lu (SNL): Quantum information science, quantum transport, and nanofabrication; and Baczewski (SNL): Theoretical modeling and calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ptychography at all wavelengths

Ptychography is a computational imaging technique that operates across multiple wavelength regimes, from electron (picometres) to X-ray (~0.1 nm), extreme ultraviolet (~10 nm) and visible light (micrometres). By reconstructing both amplitude and phase from diffraction patterns, ptychography enables high-resolution, quantitative imaging without conventional limitations imposed by lens-based optics. Ptychography has enabled advances across a range of scales: achieving deep-sub-angstrom resolution with electron microscopy, becoming an indispensable tool at X-ray synchrotron facilities worldwide and overcoming the trade-offs between resolution and field-of-view in optical imaging. This Primer provides a unified treatment of ptychography across these wavelength regimes. First, we discuss theoretical foundations, reconstruction algorithms, experimental considerations and wavelength-specific challenges. We then give examples of raw and processed data from various configurations and wavelengths. Next, we highlight key applications of ptychography in life sciences, materials science and industry. We also discuss data standards, open-source software implementations and best practices for ensuring reproducibility across different wavelength regimes. Finally, we consider limitations and future opportunities for ptychography. Together with accompanying datasets and code implementations, this Primer aims to serve newcomers and experienced practitioners in the field, facilitating broader adoption of ptychography across different disciplines.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Real-time insight into the multistage mechanism of nanoparticle exsolution from a perovskite host surface

In exsolution, nanoparticles form by emerging from oxide hosts by application of redox driving forces, leading to transformative advances in stability, activity, and efficiency over deposition techniques, and resulting in a wide range of new opportunities for catalytic, energy and net-zero-related technologies. However, the mechanism of exsolved nanoparticle nucleation and perovskite structural evolution, has, to date, remained unclear. Herein, we shed light on this elusive process by following in real time Ir nanoparticle emergence from a SrTiO 3 host oxide lattice, using in situ high-resolution electron microscopy in combination with computational simulations and machine learning analytics. We show that nucleation occurs via atom clustering, in tandem with host evolution, revealing the participation of surface defects and host lattice restructuring in trapping Ir atoms to initiate nanoparticle formation and growth. These insights provide a theoretical platform and practical recommendations to further the development of highly functional and broadly applicable exsolvable materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗