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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Exploring extreme magnetization phenomena in directly driven imploding cylindrical targets

Abstract This paper uses extended-magnetohydrodynamics (MHD) simulations to explore an extreme magnetized plasma regime realizable by cylindrical implosions on the OMEGA laser facility. This regime is characterized by highly compressed magnetic fields (greater than 10 kT across the fuel), which contain a significant proportion of the implosion energy and induce large electrical currents in the plasma. Parameters governing the different magnetization processes such as Ohmic dissipation and suppression of instabilities by magnetic tension are presented, allowing for optimization of experiments to study specific phenomena. For instance, a dopant added to the target gas-fill can enhance magnetic flux compression while enabling spectroscopic diagnosis of the imploding core. In particular, the use of Ar K-shell spectroscopy is investigated by performing detailed non-LTE atomic kinetics and radiative transfer calculations on the MHD data. Direct measurement of the core electron density and temperature would be possible, allowing for both the impact of magnetization on the final temperature and thermal pressure to be obtained. By assuming the magnetic field is frozen into the plasma motion, which is shown to be a good approximation for highly magnetized implosions, spectroscopic diagnosis could be used to estimate which magnetization processes are ruling the implosion dynamics; for example, a relation is given for inferring whether thermally driven or current-driven transport is dominating.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

In situ synchrotron X-ray total scattering measurements and analysis of colloidal CsPbX 3 nanocrystals during flow synthesis

In situ X-ray scattering measurements of CsPbX 3 (X = Cl, Br, I) nanocrystal formation and halide exchange at NSLS-II beamlines were performed in an automated flow reactor. Total scattering measurements were performed at the 28-ID-2 (XPD) beamline and small-angle X-ray scattering at the 16-ID (LiX) beamline. Nanocrystal structural parameters of interest, including size, size distribution and atomic structure, were extracted from modeling the total scattering data. The results highlight the potential of these beamlines and the measurement protocols described in this study for studying dynamic processes of colloidal nanocrystal synthesis in solution with timescales on the order of seconds.

nucleation↗
DOE OSTI2025

Topography restoration in white-light interferometry using an instrument transfer function evaluated with binary pseudo-random arrays

Accurate knowledge of the instrument transfer function (ITF) is vital for topography measurements using white-light interferometry (WLI). To this end, we derive a complete set of analytical expressions for the power spectral density (PSD) of a discretely sampled binary pseudo-random array (BPRA) as a theoretical benchmark. We then determine the ITF by comparing this theoretical PSD with the measured PSD of the BPRA. For the Zygo ZeGage Pro HR with a objective, the determined ITF closely matches the nominal modulation transfer function (MTF). Accordingly, we integrate the nominal MTF into Fourier-domain restoration filters and apply them to practical WLI topography data, yielding restored topographies that show improved agreement with atomic force microscopy measurements. Overall, our integrated BPRA-based ITF measurement and filtering methodology offers a robust yet practical means of characterizing WLI performance and enhancing the accuracy of surface measurements.

Hirose, Shigenobu [Japan Agency for Marine Earth S↗
DOE OSTI2022

R-Value Measurements Performed on Uranium Targets Irradiated with Fission Spectrum Neutrons FY 2021 for F2019 Project

The separation and characterization of two irradiated uranium targets, a depleted uranium (DU) and a highly enriched uranium (HEU) target, was conducted in April of 2021. The two targets were assembled at Los Alamos National Laboratory (LANL) and irradiated using the critical assembly at the National Criticality Experiments Research Center (NCERC). Splits of the dissolved targets were received by Pacific Northwest National Laboratory (PNNL) after which the PNNL and LANL teams chemically separated the solutions using independent separation schemes and analyzed the separated fractions for short lived actinides and fission products. Chemical separations at PNNL were traced with stable or radioactive tracers to allow for the determination of chemical yields, analyzing using either inductively coupled plasma optical emission spectroscopy (ICP-OES), inductively coupled plasma mass spectrometry (ICP-MS) or gamma emission analysis (GEA) depending on the nature of the tracer. Several other analytical techniques were used by PNNL including kinetic phosphorescence analysis (KPA) and thermal ionization mass spectrometry (TIMS) depending on the analyte’s need. Comparisons were made between current and historical PNNL and LANL, as well as literature values. Overall, there was agreement between the two laboratories for the bulk of analytes, with some notable exceptions such as 111 Ag, and 141,143,144 Ce. Included in these comparisons were the short-lived actinides 237 U, 239 Np, the fission products 89 Sr, 91 Y, 95/97 Zr, 99 Mo, 111 Ag, 115/115 mCd, 136/137 Cs, 140 Ba, 141/143/144 Ce, 147 Nd, 153 Sm, 156 Eu, and 161 Tb, providing both total atoms as well as the R-values. The data presented in this report represents the sixth NCERC irradiation of HEU and DU (FY13, FY14, FY15, FY17, FY18, FY21), and their subsequent separation and analysis.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Bridging the time scale in exascale computing of chemical systems (Final Technical Report)

This report summarizes the work carried out with support of the United States Department of Energy under Award DE-SC0019441. The theme of this project was to develop and apply methods that allowed for the acceleration of atomistic calculations, particularly in challenging areas such as multiphase systems, electrified interfaces, uncertainty estimation, and applications requiring chemical accuracy, which tend to be applications where simulation time is severely bottlenecked by the computational time requirements. Much of the focus was on the application of emerging machine-learning methodologies, although a wide range of methodologies were employed. This report has two major sections. The first focuses on the methodological advances themselves. Within this part, we report a number of major advances, a few examples of which are described here. We report the first machine-learning scheme for the acceleration of electronically grand-canonical calculations (that is, those applicable to electrochemistry). We report new methods of performing transfer learning, in which physics-based priors can be used to provide predictions, often with uncertainty estimates, of images well outside of training sets; we also offer ways to fine-tune these transfer-learning models. We provide a new systematic means to generate and apply minimal training data sets to very large (10,000’s of atoms) systems, with only small training sets appropriate for electronic structure. We developed new methodologies to integrate surface vibrations into surface adsorption calculations. We made advances to the applicability of diffusion Monte Carlo methods to allow (learned) force prediction, finite-size error correction, and force-free means of searching for transition states. We integrated machine-learned atomistic predictions into mechanism generation codes. Additionally, we released new software including AmpTorch, a modernized version of our original atomistic machine-learning code Amp. The second part of this report focuses on the scientific applications that accompanied, and were often enabled by, the methodological advances described earlier. A few examples follow, but full details are in the individual chapters of the report. For example, we developed a general theory of phonon-induced friction on molecular adsorbates. We showed fundamentally how solvent influences the adsorption and desorption process and how it differs from the processes typically involved at the solid–gas interface, making aqueous-phase and electrocatalysis different from traditional thermocatalysis. We examined how metal–insulator and magnetic transitions can be probed, and accelerated exciton dynamics via Frenkel Hamiltonian parameters. We showed that the nearsighted force-training approach, developed within this project, can predict both the stability and reactivity of large nanoparticles, and can also lead to insights on catalyst coverage on binding energies and entropies. These applied studies, which generally integrated with our method development, allowed us to push forward the theoretical understanding of several reaction classes.

08 HYDROGEN↗
DOE OSTI2021

Rapid and flexible segmentation of electron microscopy data using few-shot machine learning

Abstract Automatic segmentation of key microstructural features in atomic-scale electron microscope images is critical to improved understanding of structure–property relationships in many important materials and chemical systems. However, the present paradigm involves time-intensive manual analysis that is inherently biased, error-prone, and unable to accommodate the large volumes of data produced by modern instrumentation. While more automated approaches have been proposed, many are not robust to a high variety of data, and do not generalize well to diverse microstructural features and material systems. Here, we present a flexible, semi-supervised few-shot machine learning approach for segmentation of scanning transmission electron microscopy images of three oxide material systems: (1) epitaxial heterostructures of SrTiO 3 /Ge, (2) La 0.8 Sr 0.2 FeO 3 thin films, and (3) MoO 3 nanoparticles. We demonstrate that the few-shot learning method is more robust against noise, more reconfigurable, and requires less data than conventional image analysis methods. This approach can enable rapid image classification and microstructural feature mapping needed for emerging high-throughput characterization and autonomous microscope platforms.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM)

Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapid generation of complex nanoscale atomic structures which enables fast, systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large, diverse synthetic datasets. Using Construction Zone, we develop an end-to-end machine learning workflow for training neural network models to analyze experimental atomic resolution HRTEM images on the task of nanoparticle image segmentation purely with simulated databases. Further, we study the data curation process to understand how various aspects of the curated simulated data—including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions—affect model performance across three benchmark experimental HRTEM image datasets. Using our workflow, we are able to achieve state-of-the-art segmentation performance on these experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data. Construction Zone and its documentation are available at https://github.com/lerandc/construction_zone.

36 MATERIALS SCIENCE↗
DOE OSTI2020

What are the current limits on determination of protonation state using neutron macromolecular crystallography?

The rate of deposition of models determined by neutron diffraction, or a hybrid approach that combines X-ray and neutron diffraction, has increased in recent years. The benefit of neutron diffraction is that hydrogen atom (H) positions are detectable, allowing for the determination of protonation state and water molecule orientation. This study analyses all neutron models deposited in the Protein Data Bank to date, focusing on protonation state and properties of H (or deuterium, D) atoms as well as the details of water molecules. In particular, clashes and hydrogen bonds involving H or D atoms are investigated. As water molecules are typically the least reproducible part of a structural model, their positions in neutron models were compared to those in homologous high-resolution X-ray structures. For models determined by joint refinement against X-ray and neutron data, the water structure comparison was also carried out for models re-refined against the X-ray data alone. The homologues have generally fewer conserved water molecules where X-ray only was used and the positions of equivalent waters vary more than in the case of the hybrid X-ray model. As neutron diffraction data are generally less complete than X-ray data, the influence of neutron data completeness on nuclear density maps was also analyzed. Here, we observe and discuss systematic map quality deterioration as result of data incompleteness.

Hydrogen bond↗
DOE OSTI2026

Automated and High-Throughput Phase Separation Control for Supramolecular Polymer Blends Enabled by Machine Learning

Supramolecular polymer blends (SPBs) offer tunable morphologies that dictate their macroscopic properties, yet their rational design is limited by the absence of predictive structure−morphology models. Here, we introduce a data-driven highthroughput workflow that integrates modular polymer synthesis, robotic formulation, automated morphology characterization, and machine learning (ML) for accelerated SPB discovery. Using a plug-and-play synthetic strategy, 33 hydrogen-bonding endfunctional homopolymers were prepared and orthogonally combined to generate 260 SPBs in 1 day. A fully automated atomic force microscopy (AFM) pipeline enabled systematic imaging, producing 2340 morphology data sets with minimal human intervention. Domain spacings were extracted through complementary imageprocessing methods and used to train ML models. A support vector regression (SVR) model accurately predicted target phase-separation sizes (50, 100, and 150 nm), which were experimentally validated. This work demonstrates the power of coupling high-throughput experimentation with ML to accelerate morphology discovery and provides one of the first large-scale experimental data sets for supramolecular polymer systems.

ML-guided polymer design↗
DOE OSTI2023

Structure and Anharmonicity of α- and β -Sb 2 O 3 at Low Temperature

Antimony oxides are important materials for catalysis and flame-retardant applications. The two most common phases, α-Sb 2 O 3 (senarmontite) and β-Sb 2 O 3 (valentinite), have been studied extensively. Specific focus has been placed recently on their lattice dynamics properties and how they relate to the α-β phase transformation and their potential anharmonicity. However, there has not been any direct investigation of anharmonicity in these systems, and a surprising lack of low-temperature structural information has prevented further study. Here, we report the powder neutron diffraction data of both phases of Sb 2 O 3 , as well as structural information. α-Sb 2 O 3 behaved as expected, but β-Sb 2 O 3 revealed a small region of zero thermal expansion along the c axis. Additionally, while the β phase matched well with reported atomic displacement parameters, the α phase displayed a marked deviation. This data will enable further investigations into these systems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Radiochronometric discordance in cast uranium metal: a multi-laboratory intercomparison exercise

Here, the model age of a nuclear material is crucial in nuclear forensic analysis. Uranium metals with complex production histories often exhibit discordant model ages from the 230 Th– 234 U and 231 Pa– 235 U chronometers. Recent studies involving targeted uranium metal castings have enhanced our understanding of decay product behavior during casting, aiding nuclear forensic interpretation. Building on this prior work, forensics laboratories at Atomic Weapons Establishment (AWE), Lawrence Livermore National Laboratory (LLNL), and Los Alamos National Laboratory (LANL) conducted an interlaboratory comparison to investigate spatial heterogeneity in uranium metal cast under controlled conditions. Each laboratory measured samples of a mixed feedstock and its corresponding cast product. This work furthers our understanding of discordant model ages and the use of discordance as a signature to enhance confidence in interpretations of radiochronometric data for nuclear forensics.

230Th/234U↗
DOE OSTI2020

SAS PDF: pair distribution function analysis of nanoparticle assemblies from small-angle scattering data

SASPDF, a method for characterizing the structure of nanoparticle assemblies (NPAs), is presented. The method is an extension of the atomic pair distribution function (PDF) analysis to the small-angle scattering (SAS) regime. The PDFgetS3 software package for computing the PDF from SAS data is also presented. An application of the SASPDF method to characterize structures of representative NPA samples with different levels of structural order is then demonstrated. The SASPDF method quantitatively yields information such as structure, disorder and crystallite sizes of ordered NPA samples. The method was also used to successfully model the data from a disordered NPA sample. The SASPDF method offers the possibility of more quantitative characterizations of NPA structures for a wide class of samples.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Radiation Damage Studies on Titanium Alloys as High Intensity Proton Accelerator Beam Window Materials

A high-strength dual a+ß phase titanium alloy Ti–6Al–4V is utilized as a material for beam windows in several accelerator target facilities. However, relatively little is known about how material properties of this alloy are affected by high-intensity proton beam irradiation. With plans to upgrade neutrino facilities at J-PARC and Fermilab to over 1 MW beam power, the radiation damage in the window material will reach a few displacements per atom (dpa) per year, significantly above the ~0.3 dpa level of existing data. The international RaDIATE collaboration, Radiation Damage In Accelerator Target Environments, has conducted a high intensity proton beam irradiation of various target and window material specimens at Brookhaven Linac Isotope Producer (BLIP) facility, including a variety of titanium alloys. Post-Irradiation Examination (PIE) of the specimens in the 1st capsule, irradiated at up to 0.25 dpa, is in progress. Tensile tests in a hot cell at Pacific Northwest National Laboratory (PNNL) exhibited a clear signature of radiation hardening and loss of ductility for Ti–6Al–4V, while Ti–3Al–2.5V, with less ß phase, exhibited less severe hardening. Microstructural investigations will follow to study the cause of the difference in tensile behavior between these alloys. High-cycle fatigue (HCF) performance is critical to the lifetime estimation of beam windows exposed to a periodic thermal stress from a pulsed proton beam. The first HCF data on irradiated titanium alloys are to be obtained by a conventional bend fatigue test at Fermilab and by an ultrasonic mesoscale fatigue test at Culham Laboratory. Specimens in the 2nd capsule, irradiated at up to ~1 dpa, cover typical titanium alloy grades, including possible radiation-resistant candidates. These systematic studies on the effects of radiation damage of titanium alloy materials are intended to enable us not only to predict realistic lifetimes of current beam windows made of Ti–6Al–4V, but also to extend the lifetime by choosing a more radiation and thermal shock tolerant alloy with a preferable heat treatment, or even by developing new materials.

Titanium Alloy, Beam Window, Proton Beam, Radiatio↗
DOE OSTI2020

Simple and efficient algorithms for training machine learning potentials to force data

Machine learning models, trained on data from ab initio quantum simulations, are yielding molecular dynamics potentials with unprecedented accuracy. One limiting factor is the quantity of available training data, which can be expensive to obtain. A quantum simulation often provides all atomic forces, in addition to the total energy of the system. These forces provide much more information than the energy alone. It may appear that training a model to this large quantity of force data would introduce significant computational costs. Actually, training to all available force data should only be a few times more expensive than training to energies alone. Here, we present a new algorithm for efficient force training, and benchmark its accuracy by training to forces from real-world datasets for organic chemistry and bulk aluminum.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Single-size and cluster dynamics modeling of intra-granular fission gas bubbles in UO 2

For this work, we perform simulations of intra-granular fission gas bubble evolution in UO 2 using both a relatively simple, computationally inexpensive single-size model and a detailed cluster dynamics model. Simulations encompass 36 experimental cases from 4 different databases, covering various temperature and burnup levels. We systematically compare results from the two models to each other and to post-irradiation experimental data of bubble average size and number density. Overall, the model-to-model comparisons reveal an excellent agreement across the set of simulations. This outcome indicates that, in spite of the underlying assumptions, the single-size model provides a good approximation of the complex physical behavior that is more rigorously described by the cluster dynamics model. Qualitatively, both models reproduce the trends of the experimental data with temperature and burnup correctly. Quantitatively, calculated results are either in good agreement with the data or within errors that appear consistent with the inherent uncertainties. Moreover, for the single-size model, we demonstrate and assess a multiscale approach whereby values for the fission gas atom diffusion coefficient from separate atomistic calculations are used. Systematic comparisons to experimental data point out a credible accuracy of the multiscale model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Molecular Dynamics Simulation of the Structures, Dynamics, and Aggregation of Dissolved Organic Matter

Dissolved organic matter (DOM) plays a significant role in the transport and transformation of pollutants in the aquatic environment. However, the experimental characterization of DOM has been limited mainly to bulk properties, and the molecular-level interactions among various components of DOM remain to be fully characterized. In this study, we use molecular dynamics (MD) simulations to probe the structural properties of model DOM systems at atomic detail. The 200 ns simulations, validated by available experimental data, reveal processes and mechanisms by which chemical species (cations, peptides, lipids, lignin, carbohydrates, and some low-molecular-weight aliphatic and aromatic compounds) aggregate to form complex DOM. The DOM aggregates are dynamic, consisting of a hydrophobic core and amphiphilic exterior. The lipid tails and other hydrophobic fragments form the core, with hydrophilic and amphiphilic groups exposed to water, making DOM accessible to both polar and nonpolar species. Thus, the lipid component acts as a nucleator, whereas cations (especially Ca 2+ ) connect the molecular fragments on the surface by coordinating with the O-containing functional groups of DOM. The structural details revealed here provide new insights including surface accessible atoms, overall assemblage, and interactions among the molecules of DOM for understanding the kinetics and mechanisms through which DOM interacts with metal and other contaminants.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Combining Physics and Machine Learning for the Next Generation of Molecular Simulation

Simulating molecules and atomic systems at quantum accuracy is a grand challenge for science in the 21 st century. Quantum-accurate simulations would enable the design of new medicines and the discovery of new materials. The defining problem in this challenge is that quantum calculations on large molecules, like proteins or DNA, are fundamentally impossible with current algorithms. In this work, we explore a range of different methods that aim to make large, quantum-accurate simulations possible. We show that using advanced classical models, we can accurately simulate ion channels, an important biomolecular system. We show how advanced classical models can be implemented in an exascale-ready software package. Lastly, we show how machine learning can learn the laws of quantum mechanics from data and enable quantum electronic structure calculations on thousands of atoms, a feat that is impossible for current algorithms. Altogether, this work shows that combining advances in physics models, computing, and machine learning, we are moving closer to the reality of accurately simulating our molecular world.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

High-throughput spin-bath characterization of spin defects in semiconductors

Detailed knowledge of the local environments of spin defects in semiconductors, such as nitrogenvacancy (NV) centers in diamond or divacancies in silicon carbide, is crucial for optimizing control and entanglement protocols in quantum sensing and information applications. However, at present a direct experimental characterization of individual defect environments is not scalable, as conventional spin-bath measurements are time consuming and difficult to automate. Achieving high-throughput characterization requires short experiments to probe the spin bath. However, with fewer and noisier measurements, the inverse problem of recovering spin-bath properties from measured data becomes ill posed, with multiple spin baths having a high likelihood of yielding the same data. In this work, we present a set of computational tools to resolve the ill-posed inverse problem of recovering the atomic positions and hyperfine couplings of random nuclei surrounding spin defects from sparse, noisy experimental coherence data, which can be obtained in hours. Here, we use a trans-dimensional Bayesian approach that incorporates ab initio data to yield full posterior distributions over nuclear spin environments, enabling robust recovery from limited data. We also provide practical tools and guidelines to determine the limits of detectability for hyperfine couplings under specific dynamical decoupling sequences and sampling conditions. In addition, we demonstrate how the tools developed here, in combination with ab initio simulations of spin baths, can guide the design of efficient experimental protocols for application-specific high-throughput screening. To showcase the utility of our approach, we apply it to design fast dynamical decoupling experiments to characterize the spin baths often individual NV centers in diamond. While the primary focus is on accelerating spin-bath characterization of spin defects, this Bayesian approach also lays the foundation for digital-twin studies of spin defects, where a virtual model of the spin-defect system evolves in real time with ongoing experimental measurements. Together, the set of tools we designed and applied paves the way for scalable deployment of spin defects in semiconductors for quantum sensing and information applications.

Bayesian methods↗