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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

COMPILE: a GWAS computational pipeline for gene discovery in complex genomes

Abstract Background Genome-Wide Association Studies (GWAS) are used to identify genes and alleles that contribute to quantitative traits in large and genetically diverse populations. However, traits with complex genetic architectures create an enormous computational load for discovery of candidate genes with acceptable statistical certainty. We developed a streamlined computational pipeline for GWAS (COMPILE) to accelerate identification and annotation of candidate maize genes associated with a quantitative trait, and then matches maize genes to their closest rice and Arabidopsis homologs by sequence similarity. Results COMPILE executed GWAS using a Mixed Linear Model that incorporated, without compression, recent advancements in population structure control, then linked significant Quantitative Trait Loci (QTL) to candidate genes and RNA regulatory elements contained in any genome. COMPILE was validated using published data to identify QTL associated with the traits of α-tocopherol biosynthesis and flowering time, and identified published candidate genes as well as additional genes and non-coding RNAs. We then applied COMPILE to 274 genotypes of the maize Goodman Association Panel to identify candidate loci contributing to resistance of maize stems to penetration by larvae of the European Corn Borer ( Ostrinia nubilalis ). Candidate genes included those that encode a gene of unknown function, WRKY and MYB-like transcriptional factors, receptor-kinase signaling, riboflavin synthesis, nucleotide-sugar interconversion, and prolyl hydroxylation. Expression of the gene of unknown function has been associated with pathogen stress in maize and in rice homologs closest in sequence identity. Conclusions The relative speed of data analysis using COMPILE allowed comparison of population size and compression. Limitations in population size and diversity are major constraints for a trait and are not overcome by increasing marker density. COMPILE is customizable and is readily adaptable for application to species with robust genomic and proteome databases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Data Cards for Standardized Metadata Across DOE-Aligned Data Initiatives: Toward Transparent, Interoperable, and Governed Dataset Documentation

As data-intensive research, advanced computing, and artificial intelligence become increasingly central to scientific and operational workflows, the need for consistent, transparent, and machine-actionable documentation has grown correspondingly. Multiple DOE-aligned communities—including Office of Science, Genesis Mission, American Science Cloud (AmSC), National Nuclear Security Administration (NNSA) stewardship and governance, and related cross-laboratory collaborations—have independently developed metadata practices to support discovery, access, reuse, repository deposit, and compliance.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS2003

Supernovae and the Accelerating Universe

Orbiting high above the turbulence of the earth's atmosphere, the Hubble Space Telescope (HST) has provided breathtaking views of astronomical objects never before seen in such detail. The steady diffraction-limited images allow this medium-size telescope to reach faint galaxies of 30th stellar magnitude. Some of these galaxies are seen as early as 2 billion years after the Big Bang in a 15 billion year old universe. Up until recently, astronomers assumed that all of the laws of physics and astronomy applied back then as they do today. Now, using the discovery that certain supernovae are standard candles, astronomers have found that the universe is expanding faster today than it was back then: the universe is accelerating in its expansion.

Wood, H. John↗
DOE OSTI2022

Celeritas: GPU-accelerated particle transport for detector simulation in High Energy Physics experiments

Within the next decade, experimental High Energy Physics (HEP) will enter a new era of scientific discovery through a set of targeted programs recommended by the Particle Physics Project Prioritization Panel (P5), including the upcoming High Luminosity Large Hadron Collider (LHC) HL-LHC upgrade and the Deep Underground Neutrino Experiment (DUNE). These efforts in the Energy and Intensity Frontiers will require an unprecedented amount of computational capacity on many fronts including Monte Carlo (MC) detector simulation. In order to alleviate this impending computational bottleneck, the Celeritas MC particle transport code is designed to leverage the new generation of heterogeneous computer architectures, including the exascale computing power of U.S. Department of Energy (DOE) Leadership Computing Facilities (LCFs), to model targeted HEP detector problems at the full fidelity of Geant4. This paper presents the planned roadmap for Celeritas, including its proposed code architecture, physics capabilities, and strategies for integrating it with existing and future experimental HEP computing workflows.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Non‐Equilibrium Synthesis Methods to Create Metastable and High‐Entropy Nanomaterials

Stabilizing multiple elements within a single phase enables the creation of advanced materials with exceptional properties arising from their complex composition. However, under equilibrium conditions, the Hume–Rothery rules impose strict limitations on solid-state miscibility, restricting combinations of elements with mismatched crystal structures, atomic radii, valence states, or electronegativities. This severely narrows the accessible compositional space for creating new inorganic materials. In this review, we highlight how non-equilibrium synthesis methods, featuring ultrafast heating and quenching, can overcome these thermodynamic barriers, enabling integration of immiscible elements into metastable and high-entropy nanostructures. The resulting materials benefit from both kinetic trapping and stabilization by high configurational entropy, leading to enhanced phase stability. These materials can exhibit unique structural and functional properties that are needed for advancing catalysis, energy storage, thermoelectrics, and sensing. Furthermore, the ability of non-equilibrium methods to generate unconventional compositions and structures expands the material design space dramatically, offering rich datasets for AI-guided materials discovery. When combined with their inherent high-throughput and scalable characteristics, these approaches enable rapid, iterative optimization and accelerate the development and industrial production of next-generation inorganic materials.

high-entropy materials↗
DOE OSTI2024

CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation

Molecular conformer generation (MCG) is an important task in cheminformatics and drug discovery. The ability to efficiently generate low-energy 3D structures can avoid expensive quantum mechanical simulations, leading to accelerated virtual screenings and enhanced structural exploration. Several generative models have been developed for MCG, but many struggle to consistently produce high-quality conformers for meaningful downstream applications. To address these issues, we introduce CoarsenConf, which coarse-grains molecular graphs based on torsional angles and integrates them into an SE(3)-equivariant hierarchical variational autoencoder. Through equivariant coarse-graining, we aggregate the fine-grained atomic coordinates of subgraphs connected via rotatable bonds, creating a variable-length coarse-grained latent representation. Our model uses a novel aggregated attention mechanism to restore fine-grained coordinates from the coarse-grained latent representation, enabling efficient generation of accurate conformers. Furthermore, we evaluate the chemical and biochemical quality of our generated conformers on multiple downstream applications, including property prediction and large-scale oracle-based protein docking. Overall, CoarsenConf generates more accurate conformer ensembles compared to prior generative models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2018

The Advanced Energetic Pair Telescope for Gamma-Ray Polarimetry

The Advanced Energetic Pair Telescope (AdEPT), a future NASA/GSFC MIDEX mission, is being developed to perform high-sensitivity medium-energy (5-200 MeV) astronomy and revolutionary gamma-ray polarization measurements. The enabling technology for AdEPT is the Three- Dimensional Track Imager (3-DTI), a large volume gaseous time projection chamber with 2- dimentional micro-well detector (MWD) readout. The low density and high spatial resolution of the 3-DTI allows AdEPT to achieve high angular resolution (~0.5 at 67.5 MeV) and, for the first time, exceptional gamma-ray polarization sensitivity. These capabilities enable a wide range of scientific discovery potential for AdEPT. The key science goals of the AdEPT mission include: 1) Explore fundamental processes of particle acceleration in active astrophysical objects, 2) Reveal the magnetic field configuration of the most energetic accelerators in the Universe, 3) Explore the origins and acceleration of cosmic rays and the Galactic MeV diffuse emission, 4) Search for dark matter in the Galactic center, and 5) Test relativity with polarization measurements. We report on the latest developments of the MWDs for the 3-DTI.

Hunter, Stanley D.↗
NASA NTRS2019

Advanced Astrophysics Discovery Technology in the Era of Data Driven Astronomy

Astrophysics is at the threshold of a new epoch in which increasinglycomplex, heterogeneous datasets will challenge our existing information infrastructure and traditional approaches to analysis. The rapid advancement of graphics processing units, compact field programmable gate arrays and dedicated artificial intelligence accelerator chips is now permitting the use of scientific methods, processes and algorithms to extract knowledge and insights from structured and unstructured data in ways never before seen. Miniaturization of spacecraft architectures and supporting infrastructure is opening new observing strategies and new discovery spaces for science. The community is just beginning to awaken to these imminent challenges as evidenced by their relative lack of emphasis in the New Worlds, New Horizons ASTRO2010 decadal survey, in the ExoPAG Science Analysis Group 11 report andin the formulation of the WFIRST Data Challenge. We suggest that the Astrophysics Science Division (ASD), which has clearly recognized this new epoch of rapidly evolving information technology, could be more affirmative in its approach. We offer a modest structural solution.

Barry, Richard K.↗
DOE OSTI2025

Toward accelerating rare-earth metal extraction using equivariant neural networks

The separation of rare-earth metals, vital for numerous advanced technologies, is hampered by their similar chemical properties, making ligand discovery a significant challenge. Traditional experimental and quantum chemistry approaches for identifying effective ligands are often resource-intensive. We introduce a machine learning protocol based on an equivariant neural network, Allegro, for the rapid and accurate prediction of binding energies in rare-earth complexes. Key to this work is our newly curated dataset of rare-earth metal complexes—made publicly available to foster further research—systematically generated using the Architector program. This dataset distinctively features functionalized derivatives of proven rare-earth-chelating scaffolds, hydroxypyridinone (HOPO), catecholamide (CAM), and their thio-analogues, selected for their established efficacy in binding these elements. Trained on this valuable resource, our Allegro models demonstrate excellent performance, particularly when trained to directly predict DFT-level binding energies, yielding highly accurate results that closely correlate with theoretical calculations on a diverse test set. Furthermore, this strategy exhibited strong out-of-sample generalization, accurately predicting binding energies for an isomeric HOPO-derivative ligand not seen during training. By substantially reducing computational demands, this machine learning framework, alongside the provided dataset, represent powerful tools to accelerate the high-throughput screening and rational design of novel ligands for efficient rare-earth metal separation.

Gupta, Ankur K. [Lawrence Berkeley National Labora↗
DOE OSTI2023

Smooth, homogeneous, high-purity Nb 3 Sn superconducting RF resonant cavity by seed-free electrochemical synthesis

Abstract Workbench-size particle accelerators, enabled by Nb 3 Sn-based superconducting radio-frequency (SRF) cavities, hold the potential of driving scientific discovery by offering a widely accessible and affordable source of high-energy electrons and x-rays. Thin-film Nb 3 Sn RF superconductors with high quality factors, high operation temperatures, and high-field potentials are critical for these devices. However, surface roughness, non-stoichiometry, and impurities in Nb 3 Sn deposited by conventional Sn-vapor diffusion prevent them from reaching their theoretical capabilities. Here we demonstrate a seed-free electrochemical synthesis that pushes the limit of chemical and physical properties in Nb 3 Sn. Utilization of electrochemical Sn pre-deposits reduces the roughness of converted Nb 3 Sn by five times compared to typical vapor-diffused Nb 3 Sn. Quantitative mappings using chemical and atomic probes confirm improved stoichiometry and minimized impurity concentrations in electrochemically synthesized Nb 3 Sn. We have successfully applied this Nb 3 Sn to the large-scale 1.3 GHz SRF cavity and demonstrated ultra-low BCS surface resistances at multiple operation temperatures, notably lower than vapor-diffused cavities. Our smooth, homogeneous, high-purity Nb 3 Sn provides the route toward high efficiency and high fields for SRF applications under helium-free cryogenic operations.

Physics↗
NASA NTRS1985

Search for stellar collapse with the MACRO detector at Gran Sasso

It is viewed that in stellar evolution stars in the range of 8 to 12 solar masses evolve gradually as increasingly heavier nuclei are produced and then consumed in a series of exothermic thermonuclear processes ultimately leading to the formation of a core composed almost entirely of nickel and iron. When the mass of this hot iron-nickel core reaches the critical value of approximately 1.4 solar masses, electron degeneracy pressure is no longer able to support the outer layers of the star and a collapse process begins. Since the core has exhausted its thermonuclear fuel, further stages of thermonuclear burning cannot prevent a runaway collapse. As the density reaches 10 to the 10th power gm sub/cm at a temperature near 10 to the 10th power k, most of the heavy nuclei are dissociated into free nucleons and electron capture on free protons leads to a decrease in the degeneracy pressure and further acceleration of the collapse process. Although this general picture has received substantial confirmation over the past two decades with the discovery of radio pulsars (neutron stars), X-ray pulsars (accreting binary neutron stars) and Cyg X-1 (probably an accreting black hole), an actual neutrino burst is not yet convincingly detected.

Steinberg, R.↗
NASA NTRS2008

Pulsars and Acceleration Sites

Rotation-powered pulsars are excellent laboratories for the studying particle acceleration as well as fundamental physics of strong gravity, strong magnetic fields and relativity. But even forty years after their discovery, we still do not understand their pulsed emission at any wavelength. I will review both the basic physics of pulsars as well as the latest developments in understanding their high-energy emission. Special and general relativistic effects play important roles in pulsar emission, from inertial frame-dragging near the stellar surface to aberration, time-of-flight and retardation of the magnetic field near the light cylinder. Understanding how these effects determine what we observe at different wavelengths is critical to unraveling the emission physics. Fortunately the Gamma-Ray Large Area Space Telescope (GLAST), with launch in May 2008 will detect many new gamma-ray pulsars and test the predictions of these models with unprecedented sensitivity and energy resolution for gamma-rays in the range of 30 MeV to 300 GeV.

Harding, Alice↗
DOE OSTI2020

Thermally activated adsorption in metal–organic frameworks with a temperature–tunable diffusion barrier layer

Modification of the external surfaces of metal organic frameworks offers a new level of control over their adsorption behavior. Here, we have previously shown that capping of MOFs with ethylenediamine (EDA) can effectively retain small gaseous molecules at room temperature. Here, we report a temperature–induced variation in the capping–layer gate opening mechanism through a combination of in situ IR experiments and ab initio simulations of the capping layer. We find an atypical acceleration and increase in the loading of weakly adsorbed molecules upon raising the temperature above room temperature. Our findings show the discovery of a novel temperature–dependent kinetics that goes beyond standard kinetics and suggest a new avenue for tailoring selective adsorption by thermally tuning the surface barrier.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Characterizing Defect Dynamics in Silicon Carbide Using Symmetry-Adapted Collective Variables and Machine Learning Interatomic Potentials

Silicon carbide (SiC) divacancies are attractive candidates for spin-defect qubits possessing long coherence times and optical addressability. The high activation barriers associated with SiC defect formation and motion pose challenges for their study by first-principles molecular dynamics. In this work, we develop and deploy machine learning interatomic potentials (MLIPs) to accelerate defect dynamics simulations while retaining ab initio accuracy. We employ an active learning strategy comprising symmetry-adapted collective variable discovery and enhanced sampling to compile configurationally diverse training data, calculation of energies and forces using density functional theory (DFT), and training of an E(3)-equivariant MLIP based on the Allegro model. Here, the trained MLIP reproduces DFT-level accuracy in defect transition activation free energy barriers, enables the efficient and stable simulation of multidefect 216-atom supercells, and permits an analysis of the temperature dependence of defect thermodynamic stability and formation/annihilation kinetics to propose an optimal annealing temperature to maximally stabilize VV divacancies.

Computer simulations↗
DOE OSTI2025

Mechanistic Insights toward Accelerated Selenium-Catalyzed Allylic and Propargylic C–H Amination

Catalytic allylic and propargylic C–H aminations present valuable opportunities for the late-stage modification of pharmacophores in drug discovery. However, modern methodology is limited by reliance on expensive transition metal catalysts or slow reactivity. While selenium-catalyzed methods avoid some of these issues, in their current state, they require high catalyst loadings (15 mol %), superstoichiometric quantities of the amine and oxidant source, and long reaction times. Furthermore, our understanding of the mechanism remains incomplete: e.g., what is the catalytically active species, how is the precatalyst converted to it, and what is the role of the ligand? Here, in this paper, we report an N-heterocyclic carbene selenide (NHC-Se) that allows for substantially reduced loadings of selenium without sacrificing reaction time, improves conversions and yields for challenging substrates, and even exhibits the capacity to catalyze 1,4-allylic diamination of alkenes. To understand the origin of the enhanced activity compared to the state-of-the-art NHC-Se precatalyst, we conducted a mechanistic study that supports ligand-free selenium diimide as the active enophile in these systems, while the NHC-derived byproducts, like the corresponding urea, facilitate turnover. We also isolate and structurally characterize NHC-Se mono- and diimido species and explore their mechanistic role. Thus, this work advances C–H amination methodology, our understanding of its mechanistic underpinnings, and selenium chemistry at large.

allylic amination↗
DOE OSTI2025

Dark branches of immortal stars at the Galactic Center

We show that stars in the inner parsec of the Milky Way can be significantly affected by dark matter annihilation, producing population-level effects that are visible in a Hertzsprung-Russell (HR) diagram. We establish the dark HR diagram, where stars lie on a new stable dark main sequence with similar luminosities, but lower temperatures, than the standard main sequence. The dark matter density in these stars continuously replenishes, granting these stars immortality and solving multiple stellar anomalies. Upcoming telescopes could detect the dark main sequence, offering a new dark matter discovery avenue.

astrophysical simulations↗
DOE OSTI2021

Toward Discovering the Structure and Dynamics of the Sun-Interstellar Medium System (Los Alamos LDRD Report)

We have taken major steps toward developing the tools necessary to explore with unprecedented resolution the plasma structure and dynamics of the Sun-interstellar medium (ISM) interaction region, known as the heliosheath. We have applied a rigorous imaging technique known as Generalized Ridge Regression (GRR) to construct statistically robust sky maps of hydrogen energetic neutral atoms (ENAs) emanating from this region and detected by the Los Alamos-led IBEX-Hi ENA imager [1] on NASA’s Interstellar Boundary Explorer (IBEX) mission [2]. Our methods go far beyond the map reconstruction process currently applied by the IBEX Science Operations Center (ISOC), opening up the possibility of new discovery science with the IBEX data set and positioning LANL for a lead science role for the upcoming Interstellar Mapping and Acceleration Probe (IMAP) mission [3] for which LANL is providing two key experiments. We have successfully demonstrated that the new maps are higher resolution and are capable of revealing structures that are not presently resolveable in standard ISOC maps

58 GEOSCIENCES↗
NASA NTRS1963

Are the recently observed soft gamma-ray bursts from stellar super flares?

The discovery of soft gamma ray bursts has led to the speculation that these bursts are caused by the bremsstrahlung of electrons accelerated to high energies in a stellar flare event. If the stellar flare hypothesis is verified, it may imply a significant source of low energy cosmic rays in the solar neighborhood, depending on the frequency and intensity of the flares.

Stecker, F. W.↗