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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Freeze It or Leave It? Evaluating the Role of Cryo-Electron Microscopy in Battery Research

Cryogenic electron microscopy (cryo-EM) continues to gain prominence in materials science, particularly in battery research where it has enabled high-resolution, multimodal characterization of electrode materials and interfaces that otherwise degrade quickly under electron beam irradiation. But as anyone who has attempted cryo-EM techniques knows, freezing comes at a cost; cryo-EM experiments are time-consuming, highly sensitive, and carry an increased risk of artifacts due to issues such as frost contamination. Thus, when planning new characterization of battery materials or other beam-sensitive samples, it is critical to consider whether (and which) cryo-EM techniques are appropriate, based on study goals and an understanding of electron beam-sample interactions. Here we review such considerations for battery materials to elucidate the questions of when, why, and how to freeze to achieve high-quality characterization.

25 ENERGY STORAGE↗
DOE OSTI2022

On the frontiers of coupled extreme environments

Coupled extreme environments pose among the highest demands on materials, stressing the limits of temperature, pressure, electric and magnetic fields, atomic displacement rates, and chemical potentials that can be simultaneously sustained. Here, this issue highlights exciting new materials science methods that will enable rapid progress in what has traditionally been the most difficult materials arena. New opportunities in artificial intelligence, multi-physics simulations capabilities, the use of extreme conditions to synthesize novel materials, and our ability to interrogate materials under relevant conditions provide new avenues to understand and design new materials.

36 MATERIALS SCIENCE↗
DOE OSTI2017

1L Mark-IV Target Design Review

This presentation includes General Design Considerations; Current (Mark-III) Lower Tier; Mark-III Upper Tier; Performance Metrics; General Improvements for Material Science; General Improvements for Nuclear Science; Improving FOM for Nuclear Science; General Design Considerations Summary; Design Optimization Studies; Expected Mark-IV Performance: Material Science; Expected Mark-IV Performance: Nuclear Science (Disk); Mark IV Enables Much Wider Range of Nuclear-Science FOM Gains than Mark III; Mark-IV Performance Summary; Rod or Disk? Center or Real FOV?; and Project Cost and Schedule.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Neutron Tomography at the Los Alamos Neutron Science Center

Neutron imaging is an incredibly powerful tool for non-destructive sample characterization and materials science. Neutron tomography is one technique that results in a three-dimensional model of the sample, representing the interaction of the neutrons with the sample. This relies both on reliable data acquisition and on image processing after acquisition. Over the course of the project, the focus has changed from the former to the latter, culminating in a large-scale reconstruction of a meter-long fossilized skull. The full reconstruction is not yet complete, though tools have been developed to improve the speed and accuracy of the reconstruction. This project helps to improve the capabilities of LANSCE and LANL with regards to imaging large or unwieldy objects.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Moving closer to experimental level materials property prediction using AI

Abstract While experiments and DFT-computations have been the primary means for understanding the chemical and physical properties of crystalline materials, experiments are expensive and DFT-computations are time-consuming and have significant discrepancies against experiments. Currently, predictive modeling based on DFT-computations have provided a rapid screening method for materials candidates for further DFT-computations and experiments; however, such models inherit the large discrepancies from the DFT-based training data. Here, we demonstrate how AI can be leveraged together with DFT to compute materials properties more accurately than DFT itself by focusing on the critical materials science task of predicting “formation energy of a material given its structure and composition”. On an experimental hold-out test set containing 137 entries, AI can predict formation energy from materials structure and composition with a mean absolute error (MAE) of 0.064 eV/atom; comparing this against DFT-computations, we find that AI can significantly outperform DFT computations for the same task (discrepancies of $$>0.076$$ > 0.076 eV/atom) for the first time.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Electrical control of magnetism by electric field and current-induced torques

The remanent magnetization of ferromagnets has long been studied and used to store binary information. While early magnetic memory designs relied on magnetization switching by locally generated magnetic fields, key insights in condensed matter physics later suggested the possibility of doing it by electrical means instead. In the 1990s, Slonczewski and Berger formulated the concept of current-induced spin torques in magnetic multilayers through which a spin-polarized current generated by a first ferromagnet may be used to switch the magnetization of a second one. This discovery drove the development of spin-transfer-torque magnetic random-access memories (MRAMs). More recent fundamental research revealed other types of current-induced torques named spin-orbit torques (SOTs) and will lead to a new generation of devices including SOT MRAMs and skyrmion-based devices. Parallel to these advances, multiferroics and their magnetoelectric coupling, first investigated experimentally in the 1960s, experienced a renaissance. Dozens of multiferroic compounds with new magnetoelectric coupling mechanisms were discovered and high-quality multiferroic films were synthesized (notably of BiFeO 3 ), also leading to novel device concepts for information and communication technology such as the magnetoelectric spin-orbit (MESO) transistor. The story of the electrical switching of magnetization, which is discussed in this review, is that of a dance between fundamental research (in spintronics, condensed matter physics, and materials science) and technology (MRAMs, MESO transistors, microwave emitters, spin diodes, skyrmion-based devices, components for neuromorphics, etc.). This pas de deux has led to major scientific and technological breakthroughs in recent decades (such as the conceptualization of pure spin currents, the observation of magnetic skyrmions, and the discovery of spin-charge interconversion effects). As a result, this field has not only propelled MRAMs into consumer electronics products but also fueled discoveries in adjacent research areas such as ferroelectrics or magnonics. Here, in this review, recent advances in the control of magnetism by electric fields and by current-induced torques are covered. Fundamental concepts in these two directions are reviewed first, their combination is then discussed, and finally current various families of devices harnessing the electrical control of magnetic properties for various application fields are addressed. The review concludes by giving perspectives in terms of both emerging fundamental physics concepts and new directions in materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine learning-based microstructure prediction during laser sintering of alumina

Abstract Predicting material’s microstructure under new processing conditions is essential in advanced manufacturing and materials science. This is because the material’s microstructure hugely influences the material’s properties. We demonstrate an elegant machine learning algorithm that faithfully predicts the microstructure under new conditions, without the need of knowing the governing laws. We name this algorithm, RCWGAN-GP, which is regression-based conditional generative adversarial networks with Wasserstein loss function and gradient penalty. This algorithm was trained with experimental SEM micrographs from laser-sintered alumina under various laser powers. The RCWGAN-GP realistically regenerates the SEM micrographs under the trained laser powers. Impressively, it also faithfully predicts the alumina’s microstructure under unexplored laser powers. The predicted microstructure features, including the morphology of the sintered particles and the pores, match the experimental SEM micrographs very well. We further quantitatively examined the prediction accuracy of the RCWGAN-GP. We trained the algorithm with computer-created micrograph datasets of secondary-phase growth governed by the well-known Johnson–Mehl–Avrami (JMA) equation. The RCWGAN-GP accurately regenerates the micrographs at the trained time series, in terms of the grains’ shapes, sizes, and spatial distributions. More importantly, the predicted secondary phase fraction accurately follows the JMA curve.

08 HYDROGEN↗
DOE OSTI2018

Developing quantitative capabilities for a scanning microwave impedance microscope

Currently no instrument exists to quantitatively measure electrical properties (dielectric constant and conductivity) on sub-micron regions, a capability of extreme importance to measure the functioning of sub-micron structures and devices. In particular, such electrical measurements could support the development and manufacture of advanced energy related materials used in technologies such as photovoltaic cells, fuel cells and advanced batteries, thereby increasing performance and/or lowering production costs. The goal of this project is to develop microwave imaging add-on modules and probes capable of quantitatively measuring the electrical properties of small regions of a material using microwaves. These modules for scanning probe microscopes, such as atomic force microscopes, make use of the near-field interaction between the exciting microwaves and a material and can resolve regions 10 nanometers in size (less than a million atoms) with little perturbation of the material. In Phase 1 commercial probes and modified hardware were used to establish the feasibility of using microwave imaging systems to quantify local permittivity and conductivity. Repeatable measurements that vary in a systematic way with electrical parameters were obtained from a variety of samples. In Phase 2 research prototypes of the needed hardware and probes were developed and their performance against test structures and real-world samples was demonstrated. Result of this research project include demonstration of quantification of both permittivity and doping level at precisions up to 10% using multiple technical approaches. The level of performance and ease of use meets market expectations and the technology and protocols researched under this grant are currently being commercialized. Quantitative capabilities of this sort will provide a key capability to energy related technologies, and to a wide variety of semiconductor, biological, and materials science applications. Applications include both materials research in academic and industrial labs, as well as manufacturing applications. In a manufacturing environment the quantitative measurement and imaging capabilities could be used both as metrology for process control and as a tool for failure analysis and yield improvement.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Local Dynamics in Liquids and Glassy Materials

Atomic dynamics in strongly disordered matter, such as liquids and glasses, is characterized by correlated local dynamics. The conventional descriptions of atomic dynamics in condensed matter in terms of phonons and diffusion are inadequate for their dynamics, and instead we have to describe the dynamics in terms of spatial and temporal atomic correlations. Experimentally this means that it is insufficient to describe the dynamics through the dispersion of collective excitations in the dynamic structure factor, S(Q,E), where Q and E are the momentum and energy exchanges in scattering. As an alternative, herein we propose a real-space description in terms of the dynamic pair-density function (DyPDF) and the Van Hove function (VHF), and discuss recent results on relaxor ferroelectrics and superfluid 4 He measured by inelastic neutron scattering and water by inelastic x-ray scattering. As the subjects of research in condensed matter physics and materials science turn to more complex materials, the real-space approach is likely to become the mainstream method of research in atomic dynamics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Materials Research at Idaho National Laboratory

I am going to spent 8 mins to introduce materials science related research at INL to the student and early career researchers who attend MRS Falls 2025. I am one of 10 panelists, all of whom are from different national labs.

36 - MATERIALS SCIENCE↗
NASA NTRS1992

NASA-UVA light aerospace alloy and structures technology program (LA2ST)

The NASA-UVa Light Aerospace Alloy and Structure Technology (LAST) Program continues to maintain a high level of activity, with projects being conducted by graduate students and faculty advisors in the Departments of Materials Science and Engineering, Civil Engineering and Applied Mechanics, and Mechanical and Aerospace Engineering at the University of Virginia. This work is funded by the NASA-Langley Research Center under Grant NAG-1-745. Here, we report on progress achieved between January 1 and June 30, 1992. The objectives of the LA2ST Program is to conduct interdisciplinary graduate student research on the performance of the next generation, light weight aerospace alloys, composites and thermal gradient structures in collaboration with Langley researchers. Technical objectives are established for each research project. We aim to produce relevant data and basic understanding of material mechanical response, corrosion behavior, and microstructure; new monolithic and composite alloys; advanced processing methods; new solid and fluid mechanics analyses; measurement advances; and critically, a pool of educated graduate students for aerospace technologies. The accomplishments presented in this report cover topics including: (1) Mechanical and Environmental Degradation Mechanisms in Advance Light Metals and Composites; (2) Aerospace Materials Science; (3) Mechanics of Materials and Composites for Aerospace Structures; and (4) Thermal Gradient Structures.

Gangloff, Richard P.↗
DOE OSTI2025

Keeping LAMMPS cutting edge

Since its inception 30 years ago, LAMMPS has grown to be a world-class molecular dynamics code and a cornerstone of computational materials science research. This project aimed to keep LAMMPS at the forefront of molecular dynamics simulations by adapting LAMMPS to the latest developments in machine learning technology and hardware. Initially, the project set out to provide a unified implementation of active learning for efficient training data generation in LAMMPS, but the research trajectory pivoted to address more immediate and impactful opportunities. On the hardware side, recent record-breaking molecular dynamics simulations were developed on the Cerebras wafer-scale AI chip, and this project has developed an interface between LAMMPS and the hardware-specific molecular dynamics code to accelerate and simplify development and user adoption. On the software side, PyTorch’s Ahead-of-Time (AOT) compilation features promised increased performance for state-of-the-art equivariant neural network potentials, and this project laid the groundwork for their adoption in LAMMPS, resulting in a nearly 20x acceleration in extreme cases. Combined with a comprehensive benchmark study of LAMMPS across all current exascale systems, this project has reinforced LAMMPS’s role as a versatile, high-performance tool for current and future materials science applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Uncertainty-Driven Rapid Thermodynamic Assessment of Nb-Ta-Zr System and Effects of C impurities (L25GF9298S): Annual Progress Report

An integrated computational materials engineering (ICME) method is in development for rapid thermodynamic experimental investigation and high-fidelity computational modeling of refractory multi-principal element alloys (RMPEAs). These ultra-high-temperature (UHT) alloys are of interest for structural applications in extreme environments, but deficiency of reliable data, especially melting temperatures, impedes the prediction of alloys with favorable properties. The method leverages UHT capabilities and computational expertise of LLNL’s Materials Science Division and the McCormack Lab’s UHT conical nozzle levitation (CNL) system to iteratively map the Nb-Ta-Zr phase space, with focus on the liquidus surface, through targeted experiments selected by quantifying uncertainty in the thermodynamic model fitting parameters. This method will reduce the time to map uncharted RMPEA phase space and thereby accelerate discovery and development of advanced materials for applications in extreme environments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Exploiting Machine Learning in Multiscale Modelling of Materials

Recent developments in efficient machine learning algorithms have spurred significant interest in the materials community. The inherently complex and multiscale problems in Materials Science and Engineering pose a formidable challenge. The present scenario of machine learning research in Materials Science has a clear lacunae, where efficient algorithms are being developed as a separate endeavour, while such methods are being applied as ‘black-box’ models by others. The present article aims to discuss pertinent issues related to the development and application of machine learning algorithms for various aspects of multiscale materials modelling. The authors present an overview of machine learning of equivariant properties, machine learning-aided statistical mechanics, the incorporation of ab initio approaches in multiscale models of materials processing and application of machine learning in uncertainty quantification. In addition to the above, the applicability of Bayesian approach for multiscale modelling will be discussed. Critical issues related to the multiscale materials modelling are also discussed.

42 ENGINEERING↗
DOE OSTI2015

Office of Fusion Energy Sciences. A ten-year perspective (2015-2025)

The vision described here builds on the present U.S. activities in fusion plasma and materials science relevant to the energy goal and extends plasma science at the frontier of discovery. The plan is founded on recommendations made by the National Academies, a number of recent studies by the Fusion Energy Sciences Advisory Committee (FESAC), and the Administration’s views on the greatest opportunities for U.S. scientific leadership.This report highlights five areas of critical importance for the U.S. fusion energy sciences enterprise over the next decade: 1) Massively parallel computing with the goal of validated whole-fusion-device modeling will enable a transformation in predictive power, which is required to minimize risk in future fusion energy development steps; 2) Materials science as it relates to plasma and fusion sciences will provide the scientific foundations for greatly improved plasma confinement and heat exhaust; 3) Research in the prediction and control of transient events that can be deleterious to toroidal fusion plasma confinement will provide greater confidence in machine designs and operation with stable plasmas; 4) Continued stewardship of discovery in plasma science that is not expressly driven by the energy goal will address frontier science issues underpinning great mysteries of the visible universe and help attract and retain a new generation of plasma/fusion science leaders; 5) FES user facilities will be kept world-leading through robust operations support and regular upgrades. Finally, we will continue leveraging resources among agencies and institutions and strengthening our partnerships with international research facilities.

Source record↗
DOE OSTI2020

Uncertainty Quantification of Machine Learning Predicted Creep Property of Alumina-Forming Austenitic Alloys

The development of machine learning (ML) approaches in materials science offers the opportunity to exploit existing engineering and developmental alloy datasets, such as Oak Ridge National Laboratory (ORNL)’s consistently measured creep-rupture dataset for alumina-forming austenitic (AFA) alloys, to accelerate their further development. As a first step toward achieving ML insights for improved alloy design, the potential sources of uncertainty and their impacts on ML output are examined. It is observed that the selection of algorithms and features as well as data sampling significantly affects the performance of ML models, either positively or negatively. Further, the performance of various ML models in predicting the creep properties of AFA alloys is compared, with further evaluation by assessment of a small set of new developmental AFA alloys that were not part of the training dataset. The present study demonstrates that uncertainty quantification (UQ) is essential in materials science for evaluating the performance of ML algorithms with specifically selected feature sets and obtaining a comprehensive understanding of their limitations and the resultant capability of effective prediction in complex materials systems.

36 MATERIALS SCIENCE↗
DOE OSTI2013

An in situ accelerator-based diagnostic for plasma-material interactions science on magnetic fusion devices

This paper presents a novel particle accelerator-based diagnostic that nondestructively measures the evolution of material surface compositions inside magnetic fusion devices. The diagnostic's purpose is to contribute to an integrated understanding of plasma-material interactions in magnetic fusion, which is severely hindered by a dearth of in situ material surface diagnosis. The diagnostic aims to remotely generate isotopic concentration maps on a plasma shot-to-shot timescale that cover a large fraction of the plasma-facing surface inside of a magnetic fusion device without the need for vacuum breaks or physical access to the material surfaces. Our instrument uses a compact (∼1 m), high-current (∼1 milliamp) radio-frequency quadrupole accelerator to inject 0.9 MeV deuterons into the Alcator C-Mod tokamak at MIT. We control the tokamak magnetic fields – in between plasma shots – to steer the deuterons to material surfaces where the deuterons cause high-Q nuclear reactions with low-Z isotopes ∼5 μm into the material. The induced neutrons and gamma rays are measured with scintillation detectors; energy spectra analysis provides quantitative reconstruction of surface compositions. An overview of the diagnostic technique, known as accelerator-based in situ materials surveillance (AIMS), and the first AIMS diagnostic on the Alcator C-Mod tokamak is given. Experimental validation is shown to demonstrate that an optimized deuteron beam is injected into the tokamak, that low-Z isotopes such as deuterium and boron can be quantified on the material surfaces, and that magnetic steering provides access to different measurement locations. The first AIMS analysis, which measures the relative change in deuterium at a single surface location at the end of the Alcator C-Mod FY2012 plasma campaign, is also presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Ab Initio Molecular Dynamics of Uranium Incorporated in Goethite (α-FeOOH): Interpretation of X-ray Absorption Spectroscopy of Trace Polyvalent Metals

Incorporation of economically or environmentally consequential polyvalent metals into iron (oxyhydr)oxides has applications in environmental chemistry, remediation, and materials science. A primary tool for characterizing the local coordination environment of such metals, and therefore building models to predict their behavior, is extended X-ray absorption fine structure spectroscopy (EXAFS). Accurate structural information can be lacking, yet is required to constrain and inform data interpretation. In this regard, ab initio molecular dynamics (AIMD) was used to calculate the local coordination environment of minor amounts of U incorporated in the structure of goethite (α-FeOOH). U oxidation state (VI, V, and IV) and charge compensation scheme (CCS) were varied. Simulated trajectories were used to calculate the U LIII-edge EXAFS function and fit experimental EXAFS data for U incorporated into goethite under reducing conditions. Calculations that closely matched the U EXAFS of the well-characterized mineral uraninite (UO2), and constrained the S02 parameter to be 0.909, validated the approach. The results for the U-goethite system indicated that U(V) substituted for structural Fe(III) in octahedral uranate coordination. Charge balance was achieved by the loss of one structural proton coupled to injection of one electron into the solid (–1 H+, + 1 e-). The ability of AIMD to model higher-energy states thermally accessible at room temperature is particularly relevant for protonated systems such as goethite, where proton transfers between adjacent octahedra had a dramatic effect on the calculated EXAFS. Vibrational effects as a function of temperature were also estimated using AIMD, allowing separate quantification of thermal and configurational disorder. In summary, coupling AIMD structural modeling and EXAFS experiments enables modeling of the redox behavior of polyvalent metals that are incorporated in conductive materials such as iron (oxyhydr)oxides, with applications over a broad swath of chemistry and materials science.

36 MATERIALS SCIENCE↗