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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Theoretical description of proton-deuteron interactions using exact two-body dynamics of the femtoscopic correlation method

Modeling proton-deuteron interactions is particularly challenging. Due the deuteron's large size, the interaction can extend over several femtometers. The degree to which it can be modeled as a two-body problem might also be questioned. One way to study these interactions is through femtoscopic correlation measurements of particle pairs, extracting information using available theoretical models. In this work, we examine two approaches for describing proton-deuteron correlations: the Lednický-Lyuboshits formalism and full numerical solutions of the Schrödinger equation. Here, our results show that the differences between these methods are significant. Furthermore, we demonstrate that incorporating higher-order partial waves—particularly the p wave—is essential for accurately capturing the dynamics of proton–deuteron interactions and the full potential of the strong force.

Nucleon induced nuclear reactions↗
DOE OSTI2021

QCD critical point from the Nambu–Jona-Lasino model with a scalar-vector interaction

We study the critical point in the QCD phase diagram in the Nambu–Jona-Lasino (NJL) model by including a scalar-vector coupled interaction. We find that varying the strength of this interaction, which has no effect on the vacuum properties of QCD, can significantly affect the location of the critical point in the QCD phase diagram, particularly the value of the critical temperature. This provides a convenient way to use the NJL-based transport or hydrodynamic model to extract information about the QCD phase diagram from relativistic heavy-ion collisions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Study of the agreement between binary neutron star ejecta models derived from numerical relativity simulations

Neutron star mergers have recently become a tool to study extreme gravity, nucleosynthesis, and the chemical composition of the Universe. To date, there has been one joint gravitational and electromagnetic observation of a binary neutron star merger, GW170817, as well as a solely gravitational observation, GW190425. In order to accurately identify and interpret electromagnetic signals of neutron star mergers, better models of the matter outflows generated by these mergers are required. We compare a series of ejecta models to see where they provide strong constraints on the amount of ejected mass expected from a system, and where systematic uncertainties in current models prevent us from reliably extracting information from observed events. We also examine 2396 neutron star equations of state compatible with GW170817 to see whether a given ejecta mass could be reasonably produced with a neutron star of said equation of state, and whether different ejecta models provide consistent predictions. We find that the difference between models is often comparable to or larger than the error generally assumed for these models, implying better constraints on the models are needed. In conclusion, we also note that the extrapolation of outflow models outside of their calibration window, while commonly needed to analyze gravitational wave events, is extremely unreliable and occasionally leads to completely unphysical results.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Experimental Realization of the 1D Random Field Ising Model

Here we have measured magnetic-field-induced avalanches in a square artificial spin ice array of interacting nanomagnets. Starting from the ground state ordered configuration, we imaged the individual nanomagnet moments after each successive application of an incrementally increasing field. The statistics of the evolution of the moment configuration show good agreement with the canonical one-dimensional random field Ising model. We extract information about the microscopic structure of the arrays from our macroscopic measurements of their collective behavior, demonstrating a process that could be applied to other systems exhibiting avalanches.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Final Measurement of the 235 U Antineutrino Energy Spectrum with the PROSPECT-I Detector at HFIR

This Letter reports one of the most precise measurements to date of the antineutrino spectrum from a purely 235 U-fueled reactor, made with the final dataset from the PROSPECT-I detector at the High Flux Isotope Reactor. By extracting information from previously unused detector segments, this analysis effectively doubles the statistics of the previous PROSPECT measurement. Further, the reconstructed energy spectrum is unfolded into antineutrino energy and compared with both the Huber-Mueller model and a spectrum from a commercial reactor burning multiple fuel isotopes. A local excess over the model is observed in the 5–7 MeV energy region. Comparison of the PROSPECT results with those from commercial reactors provides new constraints on the origin of this excess, disfavoring at 2.0 and 3.7 standard deviations the hypotheses that antineutrinos from 235 U are solely responsible and noncontributors to the excess observed at commercial reactors, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Importance of nuclear quantum effects on the hydration of chloride ion

Path-integral ab initio molecular dynamics (PI-AIMD) calculations have been employed to probe the nature of chloride ion solvation in aqueous solution. Nuclear quantum effects (NQEs) are shown to weaken hydrogen bonding between the chloride anion and the solvation shell of water molecules. As a consequence, the disruptive effect of the anion on the solvent water structure is significantly reduced compared to what is found in the absence of NQEs. Additionally, the chloride hydration structure obtained from PI-AIMD agrees well with information extracted from neutron scattering data. In particular, the observed satellite peak in the hydrogen-chloride-hydrogen triple angular distribution serves as a clear signature of NQEs. The present results suggest that NQEs are likely to play a crucial role in determining the structure of saline solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Mapping hidden space-charge distributions across crystalline metal oxide/group IV semiconductor interfaces

The electronic structures of semiconducting heterojunctions are critically dependent on composition including the presence and concentrations of dopants, both intended and unintended. Dopant profiles in the interfacial region can have major effects on band energies which in turn drive transport properties. Here, in this study, we use core-level photoelectron line shapes excited with hard x rays to extract information about electric fields resulting from internal charge transfer in epitaxial La 0.03 Sr 0.97 Zr x Ti 1–x O 3 /Ge(001) (0.1≤x≤0.7) heterostructures. Experiments were carried out for heterojunctions involving both n- and p-type Ge substrates. These heterojunctions were not amenable to electronic characterization of all regions by transport measurements because the doped substrates act as electrical shunts, precluding probing the more resistive films and masking interface conductivity. However, the core-level line shapes were found to be a rich source of information on built-in potentials that exist throughout the heterostructure, and yielded valuable insight into the impact of band bending on band alignment at the buried interfaces. The electronic effects expected for Ge with uniform n- and p-type doping are eclipsed by those of unintended oxygen dopants in the Ge near the interface. This study illustrates the power of hard x-ray photoemission spectroscopy and related modeling to determine electronic structure in material systems for which insight from traditional transport measurements is limited.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Entanglement enabled intensity interferometry in ultrarelativistic ultraperipheral nuclear collisions

An important tool in studying the subfemtoscale spacetime structure of matter in ultrarelativistic heavy-ion collisions is Hanbury Brown–Twiss (HBT) intensity interferometry of identical particles in the final state of the collisions. We propose that a variant of the entanglement enabled intensity interferometry (𝐸 2 ⁢𝐼 2 ) framework introduced by Cotler and Wilczek can provide a powerful alternative to HBT interferometry in extracting fundamental nonperturbative features of quantum chromodynamics at high energies. We apply this framework to demonstrate that the spatial distributions of color singlet (pomeron) configurations in nuclei are sensitive to measurements of exclusive resonant decays of 𝜌 mesons into 𝜋 ± pairs in ultrarelativistic ultraperipheral nuclear collisions (UPCs) at the Relativistic Heavy Ion Collider and the Large Hadron Collider. A preliminary analysis suggests that the model-independent extraction of pomeron distributions will require careful treatment of the interplay of 𝐸 2 ⁢𝐼 2 in the vector meson exclusive decay with the incoherent cross section for exclusive vector meson production. The 𝐸 2 ⁢𝐼 2 framework developed here is quite general. It can also be employed as a tool to extract information on the spin structure of pomeron couplings as well as enhance the discovery potential for rare odderon configurations from exclusive vector meson decays into few-particle final states both in UPCs and at the Electron-Ion Collider.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network

Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials, such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here, we present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecular level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in the virtual screening of the combinatorial chemical space. Published by the American Physical Society 2024

Zhang, Hengrui (ORCID:0000000231831654)↗
DOE OSTI2026

Pion and kaon PDFs from lattice QCD via large momentum effective theory and short-distance factorization

In this paper, we present a first-principles lattice-QCD calculation of the unpolarized quark PDF for the pion and the kaon. The lattice data rely on matrix elements calculated for boosted mesons coupled to non-local operators containing a Wilson line. The calculations on this lattice ensemble correspond to two degenerate light, a strange, and a charm quark (𝑁 𝑓 = 2 + 1 + 1), using maximally twisted mass fermions with a clover term. The lattice volume is 32 3 × 64, with a lattice spacing of 0.0934 fm, and a pion mass of 260 MeV. Matrix elements are calculated for hadron boosts of |𝑃 3 | = 0, 0.41, 0.83, 1.25, 1.66, and 2.07 GeV. To match lattice QCD results to their light-cone counterparts, we employ two complementary frameworks: the large-momentum effective theory (LaMET) and the short-distance factorization (SDF). Using these approaches in parallel, we also test the lattice data to identify methodology-driven systematics. Results are presented for the standard quark PDFs, as well as the valence sector. Beyond obtaining the PDFs, we also explore the possibility of extracting information on SU(3) flavor-symmetry-breaking effects. For LaMET, we also parametrize the momentum dependence to obtain the infinite-momentum PDFs. Since the present calculation is performed on a single ensemble at a pion mass of 260 MeV and fixed lattice spacing, the uncertainties reported are statistical only, and systematic uncertainties remain to be addressed in future multi-ensemble studies.

Miller, Joshua [Temple University, Philadelphia, P↗
DOE OSTI2022

A simple technique to classify diffraction data from dynamic proteins according to individual polymorphs

One often observes small but measurable differences in the diffraction data measured from different crystals of a single protein. These differences might reflect structural differences in the protein and may reveal the natural dynamism of the molecule in solution. Partitioning these mixed-state data into single-state clusters is a critical step that could extract information about the dynamic behavior of proteins from hundreds or thousands of single-crystal data sets. Mixed-state data can be obtained deliberately (through intentional perturbation) or inadvertently (while attempting to measure highly redundant single-crystal data). To the extent that different states adopt different molecular structures, one expects to observe differences in the crystals; each of the polystates will create a polymorph of the crystals. After mixed-state diffraction data have been measured, deliberately or inadvertently, the challenge is to sort the data into clusters that may represent relevant biological polystates. Here, this problem is addressed using a simple multi-factor clustering approach that classifies each data set using independent observables, thereby assigning each data set to the correct location in conformational space. This procedure is illustrated using two independent observables, unit-cell parameters and intensities, to cluster mixed-state data from chymotrypsinogen (ChTg) crystals. It is observed that the data populate an arc of the reaction trajectory as ChTg is converted into chymotrypsin.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Running Ensemble Workflows at Extreme Scale: Lessons Learned and Path Forward

The ever-increasing volumes of scientific data combined with sophisticated techniques for extracting information from them have led to the increasing popularity of ensemble workflows which are a collection of runs of individual workflows. A traditional approach followed by scientists to run ensembles is to rely on simple scripts to execute different runs and manage resources. This approach is not scalable and is error-prone, thereby motivating the development of workflow management systems that specialize in executing ensembles on HPC clusters. However, when the size of both the ensemble and the target system reach extreme scales, existing workflow management systems face new challenges that hamper their efficient execution. In this paper, we describe our experience scaling an ensemble workflow from the computational biology domain from the early design stages to the execution at extreme scale on Summit, a leadership class supercomputer at the Oak Ridge National Laboratory. We discuss challenges that arise when scaling ensembles to several million runs on thousands of HPC nodes. We identify challenges with composition of the ensemble itself, its execution at large scale, post-processing of the generated data, and scalability of the file system. Based on the experience acquired, we develop a generic vision of the capabilities and abstractions to add to existing workflow management systems to enable the execution of ensemble workflows at extreme scales. We believe that the understanding of these fundamental challenges will help application teams along with workflow system developers with designing the next generation of infrastructure for composing and executing extreme-scale ensemble workflows.

Mehta, Kshitij↗
DOE OSTI2024

Visualizing an Exascale Data Center Digital Twin: Considerations, Challenges and Opportunities

Digital twins are an excellent tool to model, visualize, and simulate complex systems, to understand and optimize their operation. In this work, we present the technical challenges of real-time visualization of a digital twin of the Frontier supercomputer.We show the initial prototype and current state of the twin and highlight technical design challenges of visualizing such a large High Performance Computing (HPC) system. The goal is to understand the use of augmented reality as a primary way to extract information and collaborate on digital twins of complex systems. This leverages the spatio-temporal aspect of a 3D representation of a digital twin, with the ability to view historical and real-time telemetry, triggering simulations of a system state and viewing the results, which can be augmented via dashboards for details. Finally, we discuss considerations and opportunities for augmented reality of digital twins of large-scale, parallel computers.

Maiterth, Matthias↗
DOE OSTI2022

The Latent Dirichlet Allocation model applied to airborne LiDAR data: A case study on mapping forest degradation associated with fragmentation and fire in the Amazon region

1. LiDAR data are being increasingly used to provide a detailed characterization of the vertical profile of forests. This characterization enables the generation of new insights on the influence of environmental drivers and anthropogenic disturbances on forest structure as well as on how forest structure influences important ecosystem functions and services. Unfortunately, extracting information from LiDAR data in a way that enables the spatial visualization of forest structure, as well as its temporal changes, is challenging due to the high dimensionality of these data. 2. In this study, we show how the Latent Dirichlet Allocation model applied to LiDAR data (LidarLDA) can be used to identify forest structural types and how the relative abundance of these forest types changes throughout the landscape. The code to fit this model is made available through the open-source r package LidarLDA in github. We illustrate the use of LidarLDA both with simulated data and data from a large-scale fire experiment in the Brazilian Amazon region. 3. Using simulated data, we demonstrate that LidarLDA accurately identifies the number of forest types as well as their spatial distribution and absorptance probabilities. For the empirical data, we found that LidarLDA detects both landscape-level patterns in forest structure as well as the strong interacting effect of fire and forest fragmentation on forest structure based on the experimental fire plots. More specifically, LidarLDA reveals that proximity to forest edge exacerbates the impact of fires, and that burned forests remain structurally different from unburned areas for at least 7 years, even when burned only once. Importantly, LidarLDA generates insights on the 3D structure of forest that cannot be obtained using more standard approaches that just focus on top-of-the-canopy information (e.g. canopy height models based on LiDAR data). 4. By enabling the mapping of forest structure and its temporal changes, we believe that LidarLDA will be of broad utility to the ecological research community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Revisiting the assignment of atomic charges in metal oxides based on core-level x-ray photoelectron spectra: The case of Ti in SrTiO3(001)

We demonstrate that assigning formal charges to transition metal (TM) cations based on core-level (CL) x-ray photoemission binding energies in oxides leads to physically inconsistent pictures of electronic structure. O 2p–TM 3d hybridization is well known to result in significant covalency in TM–O bonds, thereby reducing TM cation charges from their fully ionic values. However, the ionic bonding model remains the working paradigm for assigning TM CL features, and the resulting cation charges are often taken to be representative of the material under study. Here, we show that a more physically meaningful way to assign charges is to extract information about charge distributions utilizing Dirac–Hartree–Fock theory to calculate CL spectra from first principles and then use the resulting wave functions to determine charges based on orbital occupancies. TM cation charges can also be determined using density functional theory and Bader population analysis. We illustrate these two methods using the Ti 2p spectrum for SrTiO3(001) and show that the agreement between them is excellent. Significantly, the resulting Ti charge is considerably lower than the formal charge. The high degree of similarity between the Ti 2p spectrum for SrTiO3 and those for the rutile and anatase polymorphs of TiO2 suggests that the charge densities surrounding Ti in the latter materials are similar to that in SrTiO3. Taking a broader perspective, oxides containing other first-row transition metals also exhibit covalent character, leading to TM cation charges lower than the analogous fully ionic values in these materials as well.

Chambers, Scott A. (ORCID:000000025415043X)↗
DOE OSTI2020

Three-dimensional super line-localization in low signal-to-noise microscope images via prior-apprised unsupervised learning (PAUL)

Biological processes such as processive enzyme turnover and intracellular cargo tracking involve the dynamic motion of a small "article" along a curvilinear biopolymer track. To understand these processes that occur across multiple length and time scales, one must acquire both the trajectory of the particle and the position of the track along which it moves, possibly by combining high-resolution single-particle tracking with conventional microscopy. Yet, usually there is a significant resolution mismatch between these modalities: while the tracked particle is localized with a precision of 10 nm, the image of the surroundings is limited by optical difraction, with 200 nm lateral and 500 nm axial resolutions. Compared to the particle's trajectory, the surrounding curvilinear structure appears as a blurred and noisy image. This disparity in the spatial resolutions of the particle trajectory and the surrounding curvilinear structure image makes data reconstruction, as well as interpretation, particularly challenging. Analysis is further complicated when the curvilinear structures are oriented arbitrarily in 3D space. Here, we present a prior-apprised unsupervised learning (PAUL) approach to extract information from 3D images where the underlying features resemble a curved line such as a filament or microtubule. This three-stage framework starts with a Hessian-based feature enhancement, which is followed by feature registration, where local line segments are detected on repetitively sampled subimage tiles. In the final stage, statistical learning, segments are clustered based on their geometric relationships. Principal curves are then approximated from each segment group via statistical tools including principal component analysis, bootstrap and kernel transformation. This procedure is characterized on simulated images, where sub-voxel medium deviations from true curves have been achieved. The 3D PAUL approach has also been implemented for successful line localization in experimental 3D images of gold nanowires obtained using a multifocal microscope. Lastly, this work not only bridges the resolution gap between two microscopy modalities, but also allows us to conduct 3D super line-localization imaging experiments, without using super-resolution techniques.

42 ENGINEERING↗
DOE OSTI2023

Digital quantum simulation of NMR experiments

Simulations of nuclear magnetic resonance (NMR) experiments can be an important tool for extracting information about molecular structure and optimizing experimental protocols but are often intractable on classical computers for large molecules such as proteins and for protocols such as zero-field NMR. We demonstrate the first quantum simulation of an NMR spectrum, computing the zero-field spectrum of the methyl group of acetonitrile using four qubits of a trapped-ion quantum computer. We reduce the sampling cost of the quantum simulation by an order of magnitude using compressed sensing techniques. We show how the intrinsic decoherence of NMR systems may enable the zero-field simulation of classically hard molecules on relatively near-term quantum hardware and discuss how the experimentally demonstrated quantum algorithm can be used to efficiently simulate scientifically and technologically relevant solid-state NMR experiments on more mature devices. Our work opens a practical application for quantum computation.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Integrase-On-Demand-Pipeline Data Set

Files needed to run the Integrase-On-Demand-Pipeline, a program designed to provide users with a list of putative attachment site and integrase pairs for a prokaryotic genome of interest. isles.pkl: Serialized python-object file, containing a dictionary of attachment site sequences and reference genomic island information extracted from the Genomic island database ints.gff: Gene format file containing annotations for all integrases referenced in isles.pkl. The source genome, gene coordinates, integrase name, protein IDs and amino acid sequence included. reps.msh: Binary file containing 1000 128-bit MurmurHash3 hashes for >80,000 genomes

McClain, Hannah Marie [Sandia National Laboratorie↗