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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

AI-powered exploration of molecular vibrations, phonons, and spectroscopy

The vibrational dynamics of molecules and solids play a critical role in defining material properties, particularly their thermal behaviors. However, theoretical calculations of these dynamics are often computationally intensive, while experimental approaches can be technically complex and resource-demanding. Recent advancements in data-driven artificial intelligence (AI) methodologies have substantially enhanced the efficiency of these studies. This review explores the latest progress in AI-driven methods for investigating atomic vibrations, emphasizing their role in accelerating computations and enabling rapid predictions of lattice dynamics, phonon behaviors, molecular dynamics, and vibrational spectra. Key developments are discussed, including advancements in databases, structural representations, machine-learning interatomic potentials, graph neural networks, and other emerging approaches. Compared to traditional techniques, AI methods exhibit transformative potential, dramatically improving the efficiency and scope of research in materials science. The review concludes by highlighting the promising future of AI-driven innovations in the study of atomic vibrations.

Han, Bowen [Oak Ridge National Laboratory (ORNL), ↗
DOE OSTI2024

Evaluating downscaled products with expected hydroclimatic co-variances

Abstract. There has been widespread adoption of downscaled products amongst practitioners and stakeholders to ascertain risk from climate hazards at the local scale (e.g., ∼ 5 km resolution). Such products must nevertheless be consistent with physical laws to be credible and of value to users. Here we evaluate statistically and dynamically downscaled products by examining local co-evolution of downscaled temperature and precipitation during convective and frontal precipitation events (two mechanisms testable with just temperature and precipitation). We find that two widely used statistical downscaling techniques (Localized Constructed Analogs version 2, LOCA2, and Seasonal Trends and Analysis of Residuals Empirical Statistical Downscaling Model, STAR-ESDM) generally preserve expected co-variances during convective precipitation events over the historical and future projected intervals as compared to European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) and two observation-based data products (Livneh and nClimGrid-Daily). However, both techniques dampen future intensification of frontal precipitation that is otherwise robustly captured in global climate models (i.e., prior to downscaling) and with process-based dynamical downscaling across five different regional climate models. In the case of LOCA2, this leads to appreciable underestimation of future frontal precipitation event intensity. This study is one of the first to quantify a likely ramification of the stationarity assumption underlying statistical downscaling methods and identify a phenomenon where projections of future change diverge depending on data production method employed. Finally, our work proposes expected co-variances during convective and frontal precipitation as useful evaluation diagnostics that can be universally applied to a wide range of statistically downscaled products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

From Chaos to Clarity: Autonomous Materials Discovery for Extreme Environments [Slides]

The pursuit of advanced functional materials for energy applications demands an understanding of their behavior under the most challenging conditions. Extreme environments, characterized by intense radiation, high temperatures, and corrosive chemistries, push materials to their limits, often revealing unexpected behaviors and degradation pathways. Traditional materials research approaches, relying on trial-and-error experimentation, are often slow and resource-intensive, ill-suited to the complexities of extreme environments. This talk will explore the transformative potential of autonomous materials science in revolutionizing our understanding of materials synthesis and degradation in extreme environments. By integrating advanced microscopy techniques, artificial intelligence, and robotic experimentation, we can accelerate the discovery and design of resilient materials for a sustainable future. The presentation will highlight recent breakthroughs in autonomous microscopy, computer vision, and machine learning, showcasing their ability to unravel complex material transformations at the atomic scale. The talk will also delve into the challenges and opportunities associated with deploying autonomous systems to probe extreme environments, emphasizing the importance of robust algorithms, real-time data analysis, and adaptive experimentation. The ultimate goal is to empower scientists with unprecedented capabilities to explore, understand, and engineer materials that can withstand the harshest conditions, paving the way for innovations in energy, aerospace, and beyond.

14 SOLAR ENERGY↗
DOE OSTI2024

From Chaos to Clarity: Autonomous Materials Discovery for Extreme Environments

The pursuit of advanced functional materials for energy applications demands an understanding of their behavior under the most challenging conditions. Extreme environments, characterized by intense radiation, high temperatures, and corrosive chemistries, push materials to their limits, often revealing unexpected behaviors and degradation pathways. Traditional materials research approaches, relying on trial-and-error experimentation, are often slow and resource-intensive, ill-suited to the complexities of extreme environments. This talk will explore the transformative potential of autonomous materials science in revolutionizing our understanding of materials synthesis and degradation in extreme environments. By integrating advanced microscopy techniques, artificial intelligence, and robotic experimentation, we can accelerate the discovery and design of resilient materials for a sustainable future. The presentation will highlight recent breakthroughs in autonomous microscopy, computer vision, and machine learning, showcasing their ability to unravel complex material transformations at the atomic scale. The talk will also delve into the challenges and opportunities associated with deploying autonomous systems to probe extreme environments, emphasizing the importance of robust algorithms, real-time data analysis, and adaptive experimentation. Our ultimate goal is to empower scientists with unprecedented capabilities to explore, understand, and engineer materials that can withstand the harshest conditions, paving the way for innovations in energy, aerospace, and beyond.

artificial intelligence↗
DOE OSTI2025

Interactions Between Aerosols, Meteorology, and Early Convective Cloud Lifecycle as Measured During CACTI (Final Technical Report)

The research supported by this award sought to improve the understanding and forecasting of thunderstorms. We did so by using data from the RELAMPAGO-CACTI project, which deployed a suite of instruments around a mountain range in Argentina that sees thunderstorms erupt over the same general area almost daily. The main portion of our research examined the relationship between the concentration of atmospheric particulates (dust, smoke, etc) on the intensity of thunderstorms. Despite prior studies finding that increased particulate concentration corresponded to more intense storms, we found no effect, or if anything a slightly opposite effect.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

GPU-enabled extreme-scale turbulence simulations: Fourier pseudo-spectral algorithms at the exascale using OpenMP offloading

Fourier pseudo-spectral methods for nonlinear partial differential equations are of wide interest in many areas of advanced computational science, including direct numerical simulation of three-dimensional (3-D) turbulence governed by the Navier-Stokes equations in fluid dynamics. This paper presents a new capability for simulating turbulence at a new record resolution up to 35 trillion grid points, on the world's first exascale computer, Frontier, comprising AMD MI250x GPUs with HPE's Slingshot interconnect and operated by the US Department of Energy's Oak Ridge Leadership Computing Facility (OLCF). Key programming strategies designed to take maximum advantage of the machine architecture involve performing almost all computations on the GPU which has the same memory capacity as the CPU, performing all-to-all communication among sets of parallel processes directly on the GPU, and targeting GPUs efficiently using OpenMP offloading for intensive number-crunching including 1-D Fast Fourier Transforms (FFT) performed using AMD ROCm library calls. With 99% of computing power on Frontier being on the GPU, leaving the CPU idle leads to a net performance gain via avoiding the overhead of data movement between host and device except when needed for some I/O purposes. Memory footprint including the size of communication buffers for MPI_ALLTOALL is managed carefully to maximize the largest problem size possible for a given node count. Detailed performance data including separate contributions from different categories of operations to the elapsed wall time per step are reported for five grid resolutions, from 2048 3 on a single node to 32768 3 on 4096 or 8192 nodes out of 9408 on the system. Both 1D and 2D domain decompositions which divide a 3D periodic domain into slabs and pencils respectively are implemented. The present code suite (labeled by the acronym GESTS, GPUs for Extreme Scale Turbulence Simulations) achieves a figure of merit (in grid points per second) exceeding goals set in the Center for Accelerated Application Readiness (CAAR) program for Frontier. The performance attained is highly favorable in both weak scaling and strong scaling, with notable departures only for 2048 3 where communication is entirely intra-node, and for 32768 3 , where a challenge due to small message sizes does arise. Communication performance is addressed further using a lightweight test code that performs all-to-all communication in a manner matching the full turbulence simulation code. Performance at large problem sizes is affected by both small message size due to high node counts as well as dragonfly network topology features on the machine, but is consistent with official expectations of sustained performance on Frontier. Overall, although not perfect, the scalability achieved at the extreme problem size of 32768 3 (and up to 8192 nodes — which corresponds to hardware rated at just under 1 exaflop/sec of theoretical peak computational performance) is arguably better than the scalability observed using prior state-of-the-art algorithms on Frontier's predecessor machine (Summit) at OLCF. New science results for the study of intermittency in turbulence enabled by this code and its extensions are to be reported separately in the near future.

3D fast Fourier transform↗
DOE OSTI2024

Enabling in-situ LIBS measurements of liquids and slurries

The aim of this work was to explore the use of, and further develop, a in-situ and near-real time LIBS (Laser Induced Breakdown Spectroscopy) instrument to measure liquids relevant to the Hanford site. Specifically, we investigated the feasibility of using LIBS to measure the cation composition of HLW (High Level Waste) streams. In addition, we focused on enabling continuous interrogation of those liquids using the LIBS system. For the first part, we investigated simple (sodium nitrate) solutions as well as various Hanford tank waste surrogate slurries. We showed that relevant cations (such as sodium, aluminum, magnesium, potassium) can be measured via LIBS under good conditions but the results – both quantitative and qualitative (aka reproducibility) – strongly depend on the kind and concentration of the material used. The dependence of signal intensity on concentration was also confirmed by other measurements conducted (sodium chloride and sodium nitrate in water). For the second part, we investigated the influence of varying depth and concentration of a liquid sample. We observed that for the concentrations and fill depths investigated, the liquid fill depth did not impact the emission intensity of the analyte, while concentration is confirmed to have significant effects. The results of the sample depth experiments influenced testing of waste surrogates. In addition, we focused on further developing and adapting data fitting tools in order to efficiently, accurately, and reproducibly identify the elements and materials present in a LIBS spectrum.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Desmearing two-dimensional small-angle neutron scattering data by central moment expansions

Resolution smearing is a critical challenge in the quantitative analysis of two-dimensional small-angle neutron scattering (SANS) data, particularly in studies of soft-matter flow and deformation using SANS. Here, we present a central moment expansion technique to address smearing in anisotropic scattering spectra, offering a model-free desmearing methodology. By accounting for directional variations in resolution smearing and enhancing computational efficiency, this approach reconstructs desmeared intensity distributions from smeared experimental data. Computational benchmarks using interacting hard-sphere fluids and Gaussian chain models validate the accuracy of the method, while simulated noise analyses confirm its robustness under experimental conditions. Experimental validation using rheological SANS data from shear-induced micellar structures demonstrates the practicality and effectiveness of the proposed algorithm. The desmearing technique provides a powerful tool for advancing the quantitative analysis of anisotropic scattering patterns, enabling precise insights into the interplay between material microstructure and macroscopic flow behavior.

anisotropic scattering spectra↗
DOE OSTI2025

A profile monitor for proton radiography experiments at the Los Alamos Neutron Science Center

The Proton Radiography (pRad) facility at the Los Alamos Neutron Science Center utilizes pulses of protons delivered by the 800 MeV linear accelerator to produce a series of radiographic images to study the dynamic behavior of materials under extreme conditions. Radiographs taken with an empty field of view, or beam pictures, are used to normalize transmission. However, because the center of the proton beam shifts between pulses, an in situ method for measuring beam position is required to normalize images for beam movement to perform absolute radiography. The beam profile monitor described here uses an array of scintillating fibers positioned in the beam path to produce light proportional to beam intensity across the beam cross section. This light is detected using fast photodiodes and a digital oscilloscope, providing a response time of several nanoseconds—suitable for measuring the 50-ns proton pulses used in pRad. The profile monitor achieves a measured position precision of 40 μm and an intensity precision of 0.7%, allowing for beam movement corrections to be applied to images, thereby improving data accuracy and image quality.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Machine learning enables reconstruction of past fire regimes from charcoal-derived fire intensity and fuel composition

Background Fire is a foundational ecological process that shapes ecosystem structure, diversity, and resilience. Quantifying paleofire regime attributes such as frequency, severity, and intensity is essential for understanding the historical range of variability in fire behavior and its ecological effects. While frequency and severity are often reconstructed in paleofire studies, quantitative reconstructions of fire intensity remain limited. Recent work has shown that maximum pyrolysis temperature—a proxy for fire intensity—and plant species type can be inferred from charcoal using transmission Fourier-transform infrared (FTIR) spectroscopy. However, the sample preparation for transmission FTIR is destructive and time-consuming, limiting application and reuse of materials for other analyses. We evaluated reflectance FTIR spectroscopy as a non-destructive alternative for reconstructing combustion temperature and plant species from laboratory-generated charcoal. We also examined the influence of contrasting airflow environments (ambient air versus nitrogen-rich) on pyrolysis temperature and plant species reconstruction prediction accuracies and compared predictive performance between a novel, neural network–based deep learning model with the traditional modern analogue technique (MAT) using k-nearest neighbor functions. As proof of concept, we apply our enhanced methodology to ancient charcoal to demonstrate applicability at improving long-term fire regime reconstructions and the ability to link paleofire records with contemporary fire ecology. Results Our analysis shows that transmission and reflectance FTIR spectra yield comparable spectral profiles. However, sample preparation for reflectance FTIR is minimal and non-destructive, unlike transmission FTIR which is destructive. We demonstrate that oxygen environments improved reconstruction accuracy relative to nitrogen-rich conditions. Finally, our deep learning neural network (DL) achieved testing accuracies of 98.7% for temperature and 96.2% for species identification, outperforming MAT’s k-NN approach (89.8% and 65.9%, respectively). A Shapley importance analysis identified 5 key spectral regions that greatly influenced the model’s temperature or species categorization. When applied to ancient charcoal, our results show historic fires from the most recent past primarily burned at low intensities (400–500 °C), reflective of natural fire regimes in ponderosa pine forests. Our results corroborate charcoal morphology data that suggests all ancient charcoal originated from burned woody plant types. Conclusions By combining reflectance FTIR spectroscopy with a deep learning approach, we provide the first accuracies high enough to confidently identify both species and temperature from laboratory-produced charcoal, improving quantitative reconstructions of fire intensity and fuel composition from paleofire records. This opens a wide range of research into the link between fire and larger drivers (i.e., climate or human) and greater ecological understanding of fire regimes beyond that of burn scars or recent observations. These methodological improvements have direct relevance for fire management by improving interpretation of historical fire behavior, informing fuel–fire relationships, and providing a scalable analytical framework applicable to both long-term ecological studies and contemporary fire science.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

CACTI: Fast Liquid Water Content

These data were collected during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI; https://www.arm.gov/research/campaigns/amf2018cacti ) field campaign in the Sierras de Córdoba mountain range of north-central Argentina as part of the ARM Aerial Facility (AAF) deployment. The ARM Aerial Facility Gulfstream-1 was operated from Las Higueras Airport (IATA: RCU, ICAO: SAOC), Río Cuarto, Córdoba, Argentina, for the Intensive Observation Period (IOP) from Nov. 1 through Dec. 15, 2018. The G-1 aircraft performed 22 research flights over the first ARM Mobile Facility (AMF1) location in the Sierras de Córdoba mountain range to measure atmospheric state and turbulence, cloud water content and droplet size distributions, aerosol precursor gases, and aerosol chemical composition and size distributions. The current data set presents re-processed Particle Volume Monitor PVM-100A (aka Gerber probe) data: Liquid Water Content (LWC), Particle Surface Area (PSA), and the effective droplet radius (re) averaged to 50Hz, 10Hz, and 1Hz.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A data-driven method to estimate contamination from light ion beam transmutation at colliders

Collisions of relativistic light ions such as oxygen, neon, and magnesium, have been proposed as a way to examine the system-size dependence of dynamics typically associated with the quark-gluon plasma produced in collisions of heavier ions such as xenon, gold, or lead. Recent efforts at both the Relativistic Heavy Ion Collider (RHIC) and Large Hadron Collider (LHC) have produced large datasets of proton-oxygen, oxygen-oxygen, and neon-neon collisions, catalyzing intense interest in experimental backgrounds associated with light ion collisions. In particular, electromagnetic dissociation of light ions while they are circulating in a collider can result in beam contamination that is difficult to simulate precisely. Here we propose a data-driven method for evaluating the potential impact of beam contaminants on physics analyses. The method exploits the time-dependence and smaller size of contaminant ion species to define control regions that can be used to quantify potential contamination effects. A simple mode

Accelerator Physics (physics.acc-ph)↗
DOE OSTI2025

Advances in detection for neutron reflectometry with time-resolved imaging detectors

Thermal neutron detection in neutron scattering science is a challenging endeavour due to a limited number of technologies which are sensitive to these weakly interacting particles. While many improvements to conventional detectors are underway at various facilities, there is a great opportunity to make a leap in performance by combining the spatial resolution benefits of imaging and the temporal resolution and background discrimination of event-driven radiation detectors. This idea has been realized by using a neutron sensitive scintillator read out by a single-photon sensitive camera based on a Timepix3 ASIC. We demonstrate how such data-driven imaging sensors can enable unprecedented performance in neutron reflectometry using the ASTERIX instrument at the Los Alamos Neutron Scattering Center. Several samples were measured with both the new and a conventional 3 He detector systems. The results from this work demonstrate that these imaging based systems can satisfy performance parameters for the future QIKR reflectometer to be built at the Second Target Station at Oak Ridge National Laboratory. Further improvements to the detector are already underway which will allow streamlined and expedited experiments. We demonstrate at least a two order of magnitude increase in detection rate at an acceptable dead time and introduce a new way of tuning the detector efficiency using light collecting optics to accommodate highly intense direct beams which cannot be measured with any current detectors without severe attenuation. This will allow measurements of complete reflectometry profiles and using a single sample measurement combined with fewer direct beam calibration measurements on QIKR and potentially other reflectometers.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Source shape estimation for neutron imaging systems using convolutional neural networks

Neutron imaging systems are important diagnostic tools for characterizing the physics of inertial confinement fusion reactions at the National Ignition Facility (NIF). In particular, neutron images give diagnostic information on the size, symmetry, and shape of the fusion hot spot and surrounding cold fuel. Images are formed via collection of neutron flux from the source using a system of aperture arrays and scintillator-based detectors. Currently, reconstruction of fusion source geometry from the collected neutron images is accomplished by solving a computationally intensive maximum likelihood estimation problem via expectation maximization. In contrast, it is often useful to have simple representations of the overall source geometry that can be computed quickly. In this work, we develop convolutional neural networks (CNNs) to reconstruct the outer contours of simple source geometries. We compare the performance of the CNN for penumbral and pinhole data and provide experimental demonstrations of our methods on both non-noisy and noisy data.

Machine learning, neutron imaging, source reconstr↗
DOE OSTI2024

ACE-ENA: Fast Liquid Water Content

These data were collected during the Aerosol and Cloud Experiments in the Eastern North Atlantic field campaign as part of ARM Aerial Facility deployment (ACE-ENA, https://www.arm.gov/research/campaigns/aaf2017ace-ena). The ARM Aerial Facility Gulfstream-1 was deployed at Lajes Air Base (IATA: TER, ICAO: LPLA), on Terceira Island in the Azores, Portugal, for the two Intensive Observation Periods from June 20 through July 22, 2017 (IOP#1) and from January 11 through February 22, 2018 (IOP#2). The G-1 aircraft performed 20+19 research flights over the ARM Eastern North Atlantic (ENA) site and Atlantic Ocean to measure atmospheric turbulence, cloud water content and drop size distributions, aerosol precursor gases, aerosol chemical composition and size distributions. The current data set presents re-processed Particle Volume Monitor PVM-100A (aka Gerber probe) data: Liquid Water Content (LWC), Particle Surface Area (PSA), and the effective droplet radius (re) averaged to 50 Hz, 10 Hz, and 1 Hz.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The Scaling of MCS, Non‐MCS, and Total Extreme Precipitation With Temperature Over the Central United States

Abstract While extreme precipitation is expected to increase in a warming climate, its scaling with temperature at weather timescales often produces puzzling results. Here, we focus on the summer months over the central U.S. to investigate the scaling of extreme precipitation intensity (EPI) with local temperature and determine the contribution of mesoscale convective systems (MCSs) to the EPI scaling. Using an observational data set that differentiates precipitation associated with MCS and non‐MCS storms, we find that MCS storms contribute to 70% of EPI samples at temperatures lower than 298 K where EPI increases with temperature. However, at temperatures of 298–305 K, MCSs' contribution to EPI decreases as the predominant storm type shifts from MCS to non‐MCS storms, causing EPI to decrease with temperature due to the weaker rainfall intensity associated with non‐MCS storms compared with MCS storms. Our findings underscore the important role of different storm types in affecting the EPI scaling relationships.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Bayesian Gaussian process inference for neutron spin echo measurement

Neutron spin echo (NSE) spectroscopy provides unique access to microscopic dynamics, but its application is often constrained by low neutron flux, long acquisition times, and significant noise. Here, we present a Bayesian inference approach based on Gaussian process regression (GPR) to reconstruct high-quality spin echo signals from sparse and noisy data by exploiting correlations in reciprocal space. Benchmarks on synthetic datasets and validation with experimental NSE measurements of dendrimers show that GPR suppresses noise, interpolates missing intensity values, and accommodates irregular observations. The method improves accuracy, shortens acquisition times, and enables high-throughput and real-time studies. Beyond NSE, the framework is broadly applicable to other low signal-to-noise ratio scattering techniques, thereby extending the scope of neutron spectroscopy.

Tung, Chi-Huan [Oak Ridge National Laboratory (ORN↗
DOE OSTI2024

Untargeted metabolite data from a root surface in a rhizobox

Raw data is provided from samples analyzed using separate reverse phase chromatographic methods on a high performance liquid chromatograph with mass spectrometry. These porewater samples were collected from a microdialysis which generated samples along the surface of a growing A. sative root (all_hc). This data was used to answer questions connecting rhizosphere metabolite (putatively identified metabolites, hc_putative_norm) changes over time (root growth) with changes in the surrounding rhizosphere biogeochemistry (DOC, redox, pH). Rhizosphere biogeochemistry values are provided in the hc_putative_norm file as averages over their respective range of time that they were collected at. The hc_putative_norm file also contains all normalized values over only the intensity values collected for putatively identified metabolites.

54 ENVIRONMENTAL SCIENCES↗