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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The Complete α 8 m Contributions to the 1s Lamb Shift in Hydrogen

Precision studies of simple atoms, such as hydrogen, play an essential role in tests of bound-state QED and determining fundamental constants, such as the Rydberg constant and the proton charge radius. One of the QED predictions is for the Lamb shift of hydrogenic energy levels and, in particular, of the ground state. Here, the value of the 1s Lamb shift in hydrogen and deuterium is required for an accurate determination of the Rydberg constant and the proton charge radius utilizing data from high-resolution spectroscopy of hydrogen and deuterium atoms, as well as for precision tests of bound-state QED. The dominant QED contribution to the uncertainty is due to the α 8 m external-field contributions. We discuss here our recent results on the two- and three-loop contributions, which essentially reduce the theoretical uncertainty. Combined with recent calculations of Laporta on the slope of the Dirac form factor in the three-loop level, our results allow for completion of calculations of α 8 m contributions to the Lamb shift of the ground state in the hydrogen atom.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Quantifying Atomically Dispersed Catalysts Using Deep Learning Assisted Microscopy

The catalytic performance of atomically dispersed catalysts (ADCs) is greatly influenced by their atomic configurations, such as atom–atom distances, clustering of atoms into dimers and trimers, and their distributions. Scanning transmission electron microscopy (STEM) is a powerful technique for imaging ADCs at the atomic scale; however, most STEM analyses of ADCs thus far have relied on human labeling, making it difficult to analyze large data sets. Here, we introduce a convolutional neural network (CNN)-based algorithm capable of quantifying the spatial arrangement of different adatom configurations. The algorithm was tested on different ADCs with varying support crystallinity and homogeneity. Results show that our algorithm can accurately identify atom positions and effectively analyze large data sets. Here, this work provides a robust method to overcome a major bottleneck in STEM analysis for ADC catalyst research. We highlight the potential of this method to serve as an on-the-fly analysis tool for catalysts in future in situ microscopy experiments.

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

Blue Keanu: A Scientific Visualization Tool For Network Data

This software allows the user to visualize complex PCAP-ng files captured from network capture software such as Wireshark. The visualization runs in a GUI window that can be zoomed or moved to areas of interest in a waterfall type display. The user then can see an area of interest that looks different than the typical traffic visually, such as a human interaction or non-repetitive area of data. The program will tell the user the packet number and byte offset of interest for fast analysis of discrete atomic or non-random events. This is particularly useful for visualization of unknown binary format data, such as in PLC or SCADA protocols that may have human or other non-repetitive activity for further analysis, reverse engineering, or fast forensic analysis.

Durller, MichaelGeorge↗
DOE OSTI2024

Transferring predictions of formation energy across lattices of increasing size*

In this study, we show the transferability of graph convolutional neural network (GCNN) predictions of the formation energy of the nickel-platinum solid solution alloy across atomic structures of increasing sizes. The original dataset was generated with the large-scale atomic/molecular massively parallel simulator using the second nearest-neighbor modified embedded-atom method empirical interatomic potential. Geometry optimization was performed on the initially randomly generated face centered cubic crystal structures and the formation energy has been calculated at each step of the geometry optimization, with configurations spanning the whole compositional range. Using data from various steps of the geometry optimization, we first trained our open-source, scalable implementation of GCNN called HydraGNN on a lattice of 256 atoms, which accounts well for the short-range interactions. Using this data, we predicted the formation energy for lattices of 864 atoms and 2048 atoms, which resulted in lower-than-expected accuracy due to the long-range interactions present in these larger lattices. We accounted for the long-range interactions by including a small amount of training data representative for those two larger sizes, whereupon the predictions of HydraGNN scaled linearly with the size of the lattice. Therefore, our strategy ensured scalability while reducing significantly the computational cost of training on larger lattice sizes.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Uncertainty-driven dynamics for active learning of interatomic potentials

Machine learning (ML) models, if trained to data sets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse data sets. In this approach, the ML model provides an uncertainty estimate along with its prediction for each new atomic configuration. If the uncertainty estimate passes a certain threshold, then the configuration is included in the data set. Here we develop a strategy to more rapidly discover configurations that meaningfully augment the training data set. The approach, uncertainty-driven dynamics for active learning (UDD-AL), modifies the potential energy surface used in molecular dynamics simulations to favor regions of configuration space for which there is large model uncertainty. The performance of UDD-AL is demonstrated for two AL tasks: sampling the conformational space of glycine and sampling the promotion of proton transfer in acetylacetone. The method is shown to efficiently explore the chemically relevant configuration space, which may be inaccessible using regular dynamical sampling at target temperature conditions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Evaluation of the GaAs displacement damage metric using updated nuclear data

The emerging use of the physics-based athermal recombination-corrected displacement per atom (arc-dpa) model for the displacement damage efficiency has motivated a re-evaluation of the historical empirically-derived GaAs damage response function with the purpose of highlighting needs for future analytical and experimental work. The 1-MeV neutron damage equivalence methodology used in the ASTM E-722 standard for GaAs has been re-evaluated using updated nuclear data. This yielded a higher fidelity representation of the GaAs displacement kerma and, through the use of the refined PKA recoil energy-dependent damage efficiency model, an updated 1-MeV(GaAs) displacement damage function. This re-evaluation included use of the Norgett-Robinson-Torrens (NRT) model for an updated threshold treatment, rather than the sharp-threshold Kinchin-Pease model used in the current ASTM standard. The underlying nuclear data evaluations have been updated to use the ENDF/VIII.0 {sup 75}As and TENDL-2019 {sup 71}Ga/{sup 69}Ga evaluations. The displacement kerma and 1-MeV-equivalent damage responses were calculated using a modified NJOY-2016 code which allowed for refinements in some of the damage models. This paper shows that an updated displacement damage function, based upon the latest nuclear data, is consistent with the experimental data used to develop the current ASTM E-722 GaAs standard. Using a double ratio approach to compare the available experimental data with the calculated response, the average legacy double ratio was found to be 0.97 ± 0.05 and the average updated double ratio was found to be 0.94 ± 0.05. (authors)

36 MATERIALS SCIENCE↗
DOE OSTI2020

Insights from 20 years of the Molecule of the Month

For 20 years, Molecule of the Month articles have highlighted the functional stories of 3D structures found in the Protein Data Bank (PDB). The PDB is the primary archive of atomic structures of biological molecules, currently providing open access to more than 150,000 structures studied by researchers around the world. The wealth of knowledge embodied in this resource is remarkable, with structures that allow exploration of nearly any biomolecular topic, including the basic science of genetic mechanisms, mechanisms of photosynthesis and bioenergetics, and central biomedical topics like cancer therapy and the fight against infectious disease. The central motivation behind the Molecule of the Month is to provide a user-friendly introduction to this rich body of data, charting a path for users to get started with finding and exploring the many available structures. The Molecule of the Month and related materials are updated regularly at the education portal PDB-101 (http://pdb101.rcsb.org/), offering an ongoing resource for molecular biology educators and students around the world.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

High‐Throughput Evaluation of Hardening Coefficients of Eight Alloying Elements in Magnesium

Liquid–solid diffusion couples (LSDCs) are employed to generate a composition gradient in the single‐phase hexagonal closed‐packed (hcp) solid solution with compositions up to the solubility limit of various solutes in Mg. Nanoindentation scanning across the composition gradient in LSDCs allows effective evaluation of composition‐dependent hardness of eight alloying elements (Al, Ca, Ce, Gd, Li, Sn, Y, and Zn) in the hcp Mg phase. The hardening coefficients, an indicator of the potency of solid‐solution hardening, are evaluated from the measured composition‐hardness data and correlated with various materials properties such as atomic radius, shear modulus, and elastic modulus of the solutes. The rank of hardening potency of Al, Gd, Sn, Y, and Zn measured by nanoindentation is in good agreement with that measured by microindentation reported in the literature. The hardening coefficient (potency) from the strongest to the weakest is Ce > Ca > Y ≈ Gd > Zn > Al ≈ Sn > Li in Mg‐based hcp binary solid solutions. The hardening coefficient is found to be closely correlated with the strengthening potency.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Prediction of α $IIb$ $β$ 3 integrin structures along its minimum free energy activation pathway

The adhesion protein integrin is a transmembrane heterodimer that plays a pivotal role in cellular processes such as cell signaling and cell migration. To execute its function, integrin undergoes extensive conformational changes from a bent-closed to an extended-open state. Resolving the structures across these changes remains a challenge with both experimental and computational methods, but it is crucial for understanding the activation mechanism of integrin. We address this challenge for the platelet integrin α IIb β 3 by employing finite temperature string method with structures of the images along the initial guess path generated by a multiscale data-driven framework. The full-length all-atom structures along the resulting minimum free energy path between the inactive bent-closed and active extended-open states of α IIb β 3 integrin are consistent with a variety of experimentally resolved structures. Changes in these predicted structures along the path show that the extension and separation of the α and β subunits from the bent-closed to the extended-open state require correlated movements between the subdomain pairs in α IIb β 3 . Furthermore, these results provide new insights into integrin activation mechanism, and the predicted structures have potential applications in guiding the design of integrin-targeting therapeutics.

Dasetty, Siva [University of Chicago, IL (United S↗
DOE OSTI2025

Homoleptic 1,2-benzenedithiolate complexes of thorium and uranium

Reaction of 4 equiv. of [Li(TMEDA)] 2 [1,2-S 2 C 6 H 4 ] with [ThCl 4 (DME) 2 ] or [UCl 4 (THF) 3 ] in THF results in formation of [Li(THF) 2 ] 4 [An(1,2-S 2 C 6 H 4 ) 4 ] (An = Th, 1; An = U, 2), whereas reaction of 4 equiv. of [Li(TMEDA)] 2 [1,2-S 2 C 6 H 4 ] with UCl 4 in Et 2 O results in formation of [Li(TMEDA)] 4 [U(1,2-S 2 C 6 H 4 ) 4 ] (3). Complexes 1–3 represent the first reported benzenedithiolate complexes of the actinides. Here, they were characterized by NMR spectroscopy and X-ray crystallography. In the solid state, complexes 1–3 exhibit triangular dodecahedral geometries about their actinide centers. Additionally, their Li + cations are bound by two sulfur atoms of adjacent [1,2-S 2 C 6 H 4 ] 2− ligands, in addition to two solvent donor atoms. In solution, complexes 2 and 3 exhibit spectral data consistent with S 4 symmetry (and non-exchanging Li + sites), whereas complex 1 exhibits spectral properties consistent with labile Li + cations.

X-ray↗
DOE OSTI2021

A linked-scale coupled model of mass erosion and redistribution in plasma-exposed micro-foam surfaces

Surface evolution due to exposure to harsh environments is of importance in many scientific and technological applications. In plasma-exposed materials, the surface receives charged particles from the plasma, leading to a series of processes that drive the system far from equilibrium and may lead to the severe deterioration of the surface properties. Although surface morphological changes are driven by atomic collisions taking place over picoseconds and nanometers, these processes result in mass loss and redistribution of matter over much larger length and time scales. This necessitates a multi-scale approach capable of capturing the range of processes linking primary atomic collision events with engineering-level surface geometry changes. In this paper, we develop a computational model to simulate the morphological evolution and effective erosion rate of micro-architected tungsten foams during low-energy plasma ion bombardment. Furthermore, the model acts on several length scales, with the energy and angular dependence of the sputtering yield of flat tungsten surfaces determined using the SRIM code based on the binary collision approximation. This information is introduced into a low-fluence, short-term Monte Carlo raytracing model, and further into a high-fluence, long-term particle transport model. In the latter, material particles representing billions of atoms are described in a digitized 3-D representation of the foam structure from X-ray tomography data, and are sputtered off and redeposited using the atomistic information. We show that the redeposition of sputtered atoms leads to partial self-healing in the bottom layers with a sharp reduction in the sputtering coefficient of low density foams, roughly 25% of the solid W value. This is in qualitative agreement with recent experiments on low porosity W structures. At high fluence, the foam structure degrades considerably as there are fewer ligaments available to recapture sputtered atoms and, consequently, the sputtering rate increases again.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Phase evolution of U-Zr system in a thermal cycling neutron diffraction experiment: as-cast U-35Zr and U-50Zr

Here, phase evolution of as-cast U-35 wt% Zr and U-50 wt% Zr alloys during thermal cycling (303–1073 K) was investigated using in-situ neutron diffraction. Analysis was performed using Rietveld crystal and microstructure refinements from time-of-flight neutron diffraction data, with a focus on evolution of lattice parameter, atom ordering, unit cell volume, and weight fractions during the thermal cycling. Disordered δ-UZr 2 and residual γ-(U,Zr) phase are retained in the field of δ of the U-Zr equilibrium phase diagram for the U-50Zr sample, and in the field between δ and α-U for the U-35Zr sample. The α phase that is present in the reported phase diagrams of U-Zr was not observed in the diffraction patterns. The evolutions of lattice parameter and unit cell volume of δ and γ are affected by both thermal expansion and chemistry.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

First measurement of coherent ρ 0 photoproduction in ultra-peripheral Xe–Xe collisions at s NN = 5.44 TeV

The first measurement of the coherent photoproduction of ρ 0 vector mesons in ultra-peripheral Xe–Xe collisions at s NN = 5.44 TeV is presented. This result, together with previous HERA γ p data and γ –Pb measurements from ALICE, describes the atomic number ( A ) dependence of this process, which is particularly sensitive to nuclear shadowing effects and to the approach to the black-disc limit of QCD at a semi-hard scale. The cross section of the Xe + Xe → ρ 0 + Xe + Xe process, measured at midrapidity through the decay channel ρ 0 → π + π – , is found to be d σ / d y = 131.5 ± 5.6 ( stat . ) – 16.9 + 17.5 ( syst . ) mb. The ratio of the continuum to resonant contributions for the production of pion pairs is also measured. In addition, the fraction of events accompanied by electromagnetic dissociation of either one or both colliding nuclei is reported. The dependence on A of cross section for the coherent ρ 0 photoproduction at a centre-of-mass energy per nucleon of the γA system of W γ A , n = 65 GeV is found to be consistent with a power-law behaviour σ ( γ A → ρ 0 A ) ∝ A α with a slope α = 0.96 ± 0.02 ( syst . ) . This slope signals important shadowing effects, but it is still far from the behaviour expected in the black-disc limit.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Softening of a flat phonon mode in the kagome ScV 6 Sn 6

Geometrically frustrated kagome lattices are raising as novel platforms to engineer correlated topological electron flat bands that are prominent to electronic instabilities. Here, we demonstrate a phonon softening at the k z = π plane in ScV 6 Sn 6 . The low energy longitudinal phonon collapses at ~98 K and q = $\tfrac{1}{3}$ $\tfrac{1}{3}$ $\tfrac{1}{2}$ due to the electron-phonon interaction, without the emergence of long-range charge order which sets in at a different propagation vector q CDW = $\tfrac{1}{3}$ $\tfrac{1}{3}$ $\tfrac{1}{3}$. Theoretical calculations corroborate the experimental finding to indicate that the leading instability is located at $\tfrac{1}{3}$ $\tfrac{1}{3}$ $\tfrac{1}{2}$ of a rather flat mode. We relate the phonon renormalization to the orbital-resolved susceptibility of the trigonal Sn atoms and explain the approximately flat phonon dispersion. Our data report the first example of the collapse of a kagome bosonic mode and promote the 166 compounds of kagomes as primary candidates to explore correlated flat phonon-topological flat electron physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Improving dynamic collision frequencies: Impacts on dynamic structure factors and stopping powers in warm dense matter

Simulations and diagnostics of high-energy-density plasmas and warm dense matter rely on models of material response properties, both static and dynamic (frequency-dependent). Here, in this work, we systematically investigate variations in dynamic electron–ion collision frequencies ν(ω) in warm dense matter using data from a self-consistent-field average-atom model. We show that including the full quantum density of states, strong collisions, and inelastic collisions lead to significant changes in ν(ω). These changes result in red shifts and broadening of the plasmon peak in the dynamic structure factor, an effect observable in x-ray Thomson scattering spectra, and modify stopping powers around the Bragg peak. These changes improve the agreement of computationally efficient average-atom models with first-principles time-dependent density functional theory in warm dense aluminum, carbon, and deuterium.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Phosphorus diffusion and deactivation during SiGe oxidation

Dopant profiles near the semiconductor–oxide interface are critical for microelectronic device performance. As the incorporation of Si 1−x Ge x into transistors continues to increase, it is necessary to understand the behavior of dopants in Si 1−x Ge x . In this paper, the diffusion and electrical activation of phosphorus within a strained, single-crystal Si 0.7 Ge 0.3 layer on Si during oxidation are reported. Both layers were uniformly doped, in situ, with an average phosphorus concentration of 4 × 10 19 atoms/cm 3 . After high-temperature oxidation, secondary ion mass spectrometry measurements revealed that the bulk of the phosphorus diffuses out of only the SiGe layer and segregates at the oxidizing SiGe–SiO 2 interface. Hall effect measurements corroborate the observed phosphorus loss and show that the phosphorus diffusing to the oxidizing interface is electrically inactive. Through density functional theory (DFT) calculations, it is shown that phosphorus interstitials prefer sites near the SiGe–SiO 2 interface. Finally, based on a combination of experimental data and DFT calculations, we propose that the phosphorus atoms are displaced from their lattice sites by Ge interstitials that are generated during SiGe oxidation. The phosphorus atoms then migrate toward the SiGe–SiO 2 interface through a novel mechanism of hopping between Ge sites as P–Ge split interstitials. Once they reach the interface, they are electrically inactive, potentially in the form of interstitial clusters or as part of the reconstructed interface or oxide.

Thornton, Chappel S.↗
DOE OSTI2024

GradDFT. A software library for machine learning enhanced density functional theory

Density functional theory (DFT) stands as a cornerstone method in computational quantum chemistry and materials science due to its remarkable versatility and scalability. Yet, it suffers from limitations in accuracy, particularly when dealing with strongly correlated systems. To address these shortcomings, recent work has begun to explore how machine learning can expand the capabilities of DFT: an endeavor with many open questions and technical challenges. In this work, we present GradDFT a fully differentiable JAX-based DFT library, enabling quick prototyping and experimentation with machine learning-enhanced exchange–correlation energy functionals. GradDFT employs a pioneering parametrization of exchange–correlation functionals constructed using a weighted sum of energy densities, where the weights are determined using neural networks. Moreover, GradDFT encompasses a comprehensive suite of auxiliary functions, notably featuring a just-in-time compilable and fully differentiable self-consistent iterative procedure. To support training and benchmarking efforts, we additionally compile a curated dataset of experimental dissociation energies of dimers, half of which contain transition metal atoms characterized by strong electronic correlations. The software library is tested against experimental results to study the generalization capabilities of a neural functional across potential energy surfaces and atomic species, as well as the effect of training data noise on the resulting model accuracy.

Chemistry↗