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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Laboratory Calibrations of Fe xii–xiv Line-Intensity Ratios for Electron Density Diagnostics

In this work, we have used an electron beam ion trap to measure electron-density-diagnostic line-intensity ratios for extreme ultraviolet lines from Fe xii, xiii, and xiv at wavelengths of ≈185–205 and 255–276 Å. These ratios can be used as density diagnostics for astrophysical spectra and are especially relevant to solar physics. We found that density diagnostics using the Fe xiii 196.53/202.04 and the Fe xiv 264.79/274.21 and 270.52A/274.21 line ratios are reliable using the atomic data calculated with the Flexible Atomic Code (FAC). On the other hand, we found a large discrepancy between the FAC theory and experiment for the commonly used Fe xii (186.85 + 186.88)/195.12 line ratio. These FAC theory calculations give results similar to the data tabulated in CHIANTI, which are commonly used to analyze solar observations. Our results suggest that the discrepancies seen between solar coronal density measurements using the Fe xii (186.85 + 186.88)/195.12 and Fe xiii 196.54/202.04 line ratios are likely due to issues with the atomic calculations for Fe xii.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Enhancing Cluster Identification in Atom Probe Tomography Data Using Transfer Learning

Atom Probe Tomography (APT) is a powerful technique for visualizing the atomic-scale distribution of solutes in materials, but quantitative cluster analysis of APT datasets remains a challenge due to the need for subjective parameter selection in clustering algorithms. While distance-based and density-based methods such as HDBSCAN are widely used, their performance is highly sensitive to user-defined parameters, which undermines reproducibility and accuracy. This study proposes an image-based, deep learning-aided workflow for automating parameter selection and cluster detection in APT data analysis. By projecting 3D APT point clouds onto 2D planes, we leverage pretrained convolutional neural networks (ConvNeXt-Tiny and ResNet-50) through transfer learning to predict the number of clusters present in synthetic datasets. The output is used to guide K-means clustering and estimate HDBSCAN parameters, specifically minimum cluster size and minimum sample points. This approach reduces reliance on manual parameter tuning, improving consistency and scalability. The methodology demonstrates the feasibility of using image-based deep learning for interpreting complex spatial patterns in APT data, enabling faster and more objective analysis. The complete workflow and code are made publicly available to support reproducibility and future research.

Density-based clustering↗
DOE OSTI2025

Enhancing Cluster Identification in Atom Probe Tomography Data Using Transfer Learning

Atom probe tomography (APT) has enabled the direct visualization of solute clusters, providing valuable insights into material structures. This clustering is crucial for understanding the nanoscale composition and behavior of materials, which can significantly influence their mechanical and physical properties. However, the widely used clustering methods in the APT community face challenges such as subjective parametric selection and limited applicability, particularly in dealing with overlapping clusters, nested clusters, and artifacts across different scales, such as precipitates and dislocations. To address these challenges, we present a framework based on density-based cluster analysis that aims to be less dependent on user input, reproducible, and robust.

Density-based clustering↗
DOE OSTI2023

Fantastic Fits with fantasy of Active Galactic Nuclei Spectra: Exploring the Fe II Emission near the Hα Line

In this study, a refined approach for multicomponent fitting of active galactic nuclei (AGNs) spectra is presented utilizing the newly developed Python code Fully Automated pythoN Tool for AGN Spectra analYsis (fantasy). AGN spectra are modeled by simultaneously considering the underlying broken power-law continuum, predefined emission line lists, and an Fe II model, which is here extended to cover the wavelength range 3700–11000 Å. The Fe II model, founded solely on atomic data, effectively describes the extensive emission of the complex iron ion in the vicinity of the Hγ and Hβ lines, as well as near the Hα line, which was previously rarely studied. The proposed spectral fitting approach is tested on a sample of high-quality AGN spectra from the Sloan Digital Sky Survey Data Release 17. The results indicate that when Fe II emission is present near Hβ, it is also detected redward from Hα, potentially contaminating the broad Hα line wings and thus affecting the measurements of its flux and width. The production of Fe II emission is found to be strongly correlated with Eddington luminosity and appears to be controlled by a similar mechanism as the hydrogen Balmer lines. The study highlights the benefits of fitting type 1 AGN spectra with the fantasy code, pointing that it may be used as a robust tool for analyzing a large number of AGN spectra in the coming spectral surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Alpha Shape Analysis (ASA) Framework for Post- Clustering Property Determination in Atom Probe Tomographic Data

While application of clustering algorithms to atom probe tomography data have enabled quantification of solute clusters in terms of number density, size, and subcomposition there exist other properties (e.g., volume, surface area, and composition) that are better determined by defining an interface between the cluster and the surrounding matrix. The limitation in composition results from an ion selection step where the expected matrix ion types are omitted from the cluster search algorithm to enhance the contrast between the matrix and cluster and to reduce the complexity of the search. Previously, composition determination within solute clusters has utilized a secondary envelopment and erosion step on top of conventional methods such as maximum separation. In this work, we present a novel stochastic method that combines the particle identification fidelity of a conventional clustering algorithm with the analytical flexibility of mesh-based approaches through the generation of alpha shapes for each identified cluster. Furthermore, the corresponding mesh accounts for concave components of the clusters and determines the volume and surface area of the clusters; additionally, the mesh boundary is utilized to update the total composition according to the internal ions.

42 ENGINEERING↗
DOE OSTI2026

Precision of ENDF and ENDL Formatted Data Files

The purpose is to ensure that today’s processing codes produced output to meet today’s accuracy needs. Since 1958 ENDL and about 1965 ENDF have each used a text format to define nuclear and atomic data in 11 columns for each data field. When these formats originated this was judged to be adequate to reproduce the accuracy of data at the time and to meet the needs of our applications. When these formats originated the dominant computer language of the day was FORTRAN and if written using an E11.4 format it would include only 4 or 5 digits of precision, e.g., 0.1234E-03 or 1.2345E-02, varying from one computer/system to another the result was not even unique. In the case of ENDF the 4 digit precision was not even adequate to uniquely define the atomic weight of the target, e.g., U238 = 92238 = 0.9224E+5 = WRONG! From its inceptions the ENDF format had a precision problem. One of my first tasks when in 1967 fresh out of graduate school I joined what later became the National Nuclear Data Center (NNDC), was to address this precision problem. By working with ENDF producers and users throughout the U.S. we verified, 1) E or D is not required to define FORTRAN readable numbers, e.g., E+4 or +4 are both o.k. 2) With ENDF energy eV and cross section in barns, 2 digit exponents are almost never required. 3) Since energy is never negative we could use the first of the 11 columns for a digit. Knowing this allowed us to produce ENDF/B-II to 6 or 7 digit precision, e.g., blank, decimal point, 2 or 3 digit exponent, e.g., ^1.23456-12 or ^1.234567-3. Below is an example of the actual ENDF/B-II data released. Note, the date 1970 and the atomic weight, ZA, uniquely defined to 6-digit accuracy, ^9.22350+ 4.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images

Abstract The rise of automation and machine learning (ML) in electron microscopy has the potential to revolutionize materials research through autonomous data collection and processing. A significant challenge lies in developing ML models that rapidly generalize to large data sets under varying experimental conditions. We address this by employing a cycle generative adversarial network (CycleGAN) with a reciprocal space discriminator, which augments simulated data with realistic spatial frequency information. This allows the CycleGAN to generate images nearly indistinguishable from real data and provide labels for ML applications. We showcase our approach by training a fully convolutional network (FCN) to identify single atom defects in a 4.5 million atom data set, collected using automated acquisition in an aberration-corrected scanning transmission electron microscope (STEM). Our method produces adaptable FCNs that can adjust to dynamically changing experimental variables with minimal intervention, marking a crucial step towards fully autonomous harnessing of microscopy big data.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

FePt binary alloy with 32 atoms - LSMS-3 data

This dataset contains the estimate of atomic charge density, atomic magnetic moment and total energy for 32,000 configurations of the iron-platinum (FePt) binary alloy with body-centered cubic (BCC) structure. The configurations span all the compositions from 0%Fe - 100% Pt through 100%Fe - 0% Pt. The results have been produced running ab-initio density functional theory (DFT) calculations with the LSMS-3 code on OLCF supercomputer Titan. LSMS-3 GitHub repository: https://github.com/mstsuite/lsms Deep Learning research papers published with results based on this dataset: Fast and stable deep-learning predictions of material properties for solid solution alloys Massimiliano Lupo Pasini, Ying Wai Li, Junqi Yin, Jiaxin Zhang, Kipton Barros and Markus Eisenbach Published 14 December 2020 • © 2020 IOP Publishing Ltd Journal of Physics: Condensed Matter, Volume 33, Number 8 Citation Massimiliano Lupo Pasini et al 2021 J. Phys.: Condens. Matter 33 084005 https://iopscience.iop.org/article/10.1088/1361-648X/abcb10

36 MATERIALS SCIENCE↗
DOE OSTI2021

CuAu binary alloy with 32 atoms - LSMS-3 data

This dataset contains the estimate of atomic charge density, atomic magnetic moment and total energy for 32,000 configurations of the iron-platinum (CuAu) binary alloy with face-centered cubic (FCC) structure. The configurations span all the compositions from 0%Cu - 100% Au through 100%Cu - 0% Au. The results have been produced running ab-initio density functional theory (DFT) calculations with the LSMS-3 code on OLCF supercomputer Titan. LSMS-3 GitHub repository: https://github.com/mstsuite/lsms Deep Learning research papers published with results based on this dataset: Fast and stable deep-learning predictions of material properties for solid solution alloys Massimiliano Lupo Pasini, Ying Wai Li, Junqi Yin, Jiaxin Zhang, Kipton Barros and Markus Eisenbach Published 14 December 2020. 2020 IOP Publishing Ltd Journal of Physics: Condensed Matter, Volume 33, Number 8 Citation Massimiliano Lupo Pasini et al 2021 J. Phys.: Condens. Matter 33 084005 https://iopscience.iop.org/article/10.1088/1361-648X/abcb10

36 MATERIALS SCIENCE↗
DOE OSTI2024

Circuit-Based Leakage-to-Erasure Conversion in a Neutral-Atom Quantum Processor

Atom-loss errors are a major limitation of current state-of-the-art neutral-atom quantum computers and pose a significant challenge for scalable systems. In a quantum processor with cesium atoms, we demonstrate proof-of-principle circuit-based conversion of this form of leakage error to erasure errors via leakage-detection units (LDUs), which nondestructively map information about the presence or absence of the qubit onto the state of an ancilla. We benchmark the performance of the LDU using a three-outcome low-loss state-detection method and find that the LDU detects atom-loss errors with approximately 93.4% accuracy, limited by technical imperfections of our apparatus. We further compile and execute a SWAP LDU, wherein the roles of the original data atom and ancilla atom are exchanged under the action of the LDU, providing “free refilling” of atoms in the case of atom loss. This circuit-based leakage-to-erasure error conversion is a critical component of a neutral-atom quantum processor where the quantum information may significantly outlive the lifetime of any individual atom in the quantum register. Finally, we demonstrate that LDUs may also be used to handle other forms of leakage errors where population moves to states outside of the computational subspace.

Chow, Matthew N. H. [Sandia National Laboratories ↗
DOE OSTI2024

Unlocking the Sun's Secrets: Laboratory Insights into the Sun's Outer Atmosphere

The Sun’s corona, its outermost and hottest atmospheric layer, emits brightly in the extreme ultraviolet (EUV) and X-rays. This emission comes from atoms that have been highly ionized by the high temperature, forming a plasma of atomic ions and free electrons. By studying this light, scientists learn about the complex and dynamic drivers of solar activity. Many past, current, and future solar observatories focus on measuring EUV spectra from the Fe IX ion, as this ion provides powerful diagnostics for solar physics. Using Fe IX spectra, solar physicists can measure the plasma temperature, density, elemental abundances, and flow velocities in the corona. However, atomic data uncertainties concerning spectral line identification and plasma density diagnostics are a major problem for the accurate interpretation of these spectra.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Experimental inference of neutral and impurity transport in Alcator C-Mod using high-resolution x-ray and ultra-violet spectra

We present experimental inferences of cross-field impurity transport coefficients for AlcatorC-Mod plasmas without edge-localized modes, leveraging a novel forward model for the entire Ca Kα spectrum, including satellite lines within the spectral range, to compare to high-resolution x-ray imaging crystal spectroscopy (XICS). These measurements are complemented by extreme ultra-violet (EUV) spectroscopy that constrains transport closer to the edge. Using new atomic data sets for both XICS and EUV analysis has enabled consideration of line ratios across both spectral ranges and has increased the accuracy of inferred transport coefficients. Inclusion of charge exchange between edge thermal neutrals and impurities is shown to be extremely important in C-Mod pedestals. Atomic D neutral densities from experimental D Lyα measurements at the midplane are compared to SOLPS-ITER simulations, finding good agreement. Bayesian inferences of impurity transport coefficients are presented for L-, EDA H-, and I-mode discharges, making use of the Aurora package for forward modeling and combining our spectroscopic constraints. Experimentally inferred diffusion profiles are found to match turbulent transport models at midradius within uncertainties, using both quasilinear gyro-fluid TGLF SAT-1 and nonlinear ion-scale gyrokinetic CGYRO simulations. Furthermore, significant discrepancies in convection are observed in some cases, suggesting difficulties in predictions of flat or hollow impurity profiles.

neutrals↗
DOE OSTI2025

In search of truth: In memory of Balraj Singh

Born in Punjab (India) in December 1941, Balraj Singh is not only the single most prolific nuclear data evaluator and disseminator of nuclear structure and decay data with 148 evaluations in Nuclear Data Sheets — 85 as the first and often only author — plus other journals, but his upmost curiosity and dedication brought him to be one of the finest nuclear physicists, with an everlasting influence on many of us. Furthermore, Balraj passed away about a year ago on 9 October 2023 in Ottawa, Ontario (Canada) at the age of 81, and at Atomic Data and Nuclear Data Tables we would like to commemorate some of his scientific achievements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Theoretical level energies, radiative lifetimes and transitions in W IX

The atomic states of the W IX (W{sup 8+}) tungsten ion lying below the W{sup 9+} ionisation threshold have been studied theoretically, employing the multiconfiguration Dirac–Hartree–Fock method with configuration interaction. The level electronic structures and their energies are presented. The electric dipole (E1), magnetic dipole (M1), electric quadrupole (E2), and magnetic quadrupole (M2) radiative transitions have been computed in order to calculate the radiative lifetimes of given states. Transition wavelengths, energies, and decay rates are also presented for selected high-intensity E1 transitions. The configuration interaction method was applied to estimate electron correlation effects. The aim of the present research was to fill a lack of atomic data for low-charged tungsten ions, which may be useful in low-temperature plasma diagnostics and may form the base for collisional–radiative modelling of spectra for low-charged tungsten ions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Disentangling Coexisting Structural Order Through Phase Lock-In Analysis of Atomic-Resolution STEM Data

As a real-space technique, atomic-resolution STEM imaging contains both amplitude and geometric phase information about structural order in materials, with the latter encoding important information about local variations and heterogeneities present in crystalline lattices. Such phase information can be extracted using geometric phase analysis (GPA), a method which has generally focused on spatially mapping elastic strain. Here we demonstrate an alternative phase demodulation technique and its application to reveal complex structural phenomena in correlated quantum materials. As with other methods of image phase analysis, the phase lock-in approach can be implemented to extract detailed information about structural order and disorder, including dislocations and compound defects in crystals. Extending the application of this phase analysis to Fourier components that encode periodic modulations of the crystalline lattice, such as superlattice or secondary frequency peaks, we extract the behavior of multiple distinct order parameters within the same image, yielding insights into not only the crystalline heterogeneity but also subtle emergent order parameters such as antipolar displacements. When applied to atomic-resolution images spanning large (~0.5 × 0.5 μm 2 ) fields of view, this approach enables vivid visualizations of the spatial interplay between various structural orders in novel materials.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Exploring physics of ferroelectric domain walls via Bayesian analysis of atomically resolved STEM data

The physics of ferroelectric domain walls is explored using the Bayesian inference analysis of atomically resolved STEM data. We demonstrate that domain wall profile shapes are ultimately sensitive to the nature of the order parameter in the material, including the functional form of Ginzburg-Landau-Devonshire expansion, and numerical value of the corresponding parameters. The preexisting materials knowledge naturally folds in the Bayesian framework in the form of prior distributions, with the different order parameters forming competing (or hierarchical) models. Here, we explore the physics of the ferroelectric domain walls in BiFeO 3 using this method, and derive the posterior estimates of relevant parameters. More generally, this inference approach both allows learning materials physics from experimental data with associated uncertainty quantification, and establishing guidelines for instrumental development answering questions on what resolution and information limits are necessary for reliable observation of specific physical mechanisms of interest.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Validation of electron backscattering in the MCNP6.3 code

A new electron backscattering validation suite has been developed for the MCNP® code. The calculations in this suite cover both condensed history and single-event transport methods, seven different elemental materials ranging from beryllium to uranium, and incident electron energies from 200 eV to 14.1 MeV. In general, the accuracy of the MCNP code for electron backscattering calculations is energy-dependent. For incident electron energies below 2 keV, the condensed history method is not applied while the single-event method shows poor agreement with experimental measurements due to poor accuracy of the underlying atomic data. For incident energies between 2 keV and 256 keV, the single-event method typically shows better agreement with experiments, while for incident energies above 256 keV the condensed history method gives better accuracy. Additionally, agreement with experimental data is generally worse for low- Z materials such as beryllium and carbon, which may reflect deficiencies in the data or physics underlying electron transport through light elements in the MCNP code. The new electron backscattering validation suite, along with the previously introduced electron stopping power and Lockwood energy deposition validation suites, provide MCNP users with a quantified understanding of the uncertainties in electron transport calculations. Future improvements to electron transport in the MCNP code are necessary for low energies (≤ 2 keV) and for low- Z materials.

36 MATERIALS SCIENCE↗