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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

SDSS-IV MaNGA: The MaNGA Dwarf Galaxy Sample Presentation

We present the MaNGA Dwarf galaxy (MaNDala) Value Added Catalog (VAC), from the final release of the Sloan Digital Sky Survey-IV program. MaNDala consists of 136 randomly selected bright dwarf galaxies with M * < 10 9.1 M ⊙ and M g > -18.5, making it the largest integral field spectroscopy homogeneous sample of dwarf galaxies. We release a photometric analysis of the g, r, and z broadband imaging based on the DESI Legacy Imaging Surveys, as well as a spectroscopic analysis based on the Pipe3D SDSS-IV VAC. Our release includes the surface brightness (SB), geometric parameters, and color profiles, Sérsic fits as well as stellar population properties (such as stellar ages, metallicities, and star formation histories), and emission lines' fluxes within the FOV and the effective radii of the galaxies. We find that the majority of the MaNDala galaxies are star-forming late-type galaxies with $\langle$n Sersic,r $\rangle$ ~ 1.6 that are centrals (central/satellite dichotomy). MaNDala covers a large range of SB values (we find 11 candidate ultra-diffuse galaxies and three compact ones), filling the gap between classical dwarfs and low-mass galaxies in the Kormendy Diagram and in the size–mass/luminosity relation, which seems to flatten at 10 8 < M * /M ⊙ < 10 9 with $\langle$R e,r $\rangle$ ~ 2.7 kpc. A large fraction of MaNDala galaxies formed from an early low-metallicity burst of SF, but also from late SF events from more metal-enriched gas: half of the MaNDala galaxies assembled 50% of their mass at $\langle$z$\rangle$ > 2, while the last 20% was at $\langle$z$\rangle$ < 0.3. Finally, a bending of the sSFR-M * relation at M * ~ 10 9 M ⊙ for the main-sequence galaxies seems to be supported by MaNDala.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Realization of Non‐Equilibrium Wurtzite Structure in Heterovalent Ternary MgSiN 2 Film Grown by Reactive Sputtering

The piezoelectric and ferroelectric applications of heterovalent ternary materials are not well explored. Epitaxial MgSiN 2 films are grown at 600 °C on (111)Pt//(001)Al 2 O 3 substrates by the reactive sputtering method using metallic Mg and Si under the N 2 atmosphere. Detailed X-ray diffraction measurements and transmission electron microscopy observations revealed that the epitaxially grown films on the substrates have a hexagonal wurtzite structure with c-axis out-of-plane orientation. The random occupation of this structure by Mg and Si differs from that of the previously reported structure in which these two cations periodically occupy the cationic sites. However, the lattice spacings closely approximate those that are previously reported, irrespective of the ordering, and they are almost comparable with those of (Al 0.8 Sc 0.2 )N. The wide bandgap of >5.0 eV in deposited MgSiN 2 is compatible with that of AlN and suggests durability against the application of strong external electric fields, possibly to induce polarization switching. In addition, MgSiN 2 is shown to have piezoelectric properties with an effective d 33 value of 2.3 pm V −1 for the first time. This work demonstrates the compositional expansion of hexagonal wurtzite to heterovalent ternary nitrides for novel piezoelectric materials, whose ferroelectricity is expected.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Highly Oriented Organic Ferroelectric Films with Single-Crystal-Level Properties from Restrained Crystallization

Device applications often require thin film growth with quasi-two-dimensional morphology and crystallization which are not always compatible. In this work, we exploit the method of low-temperature deposition followed by restrained crystallization (LDRC) in the growth of crystalline organic molecular thin films, which tend to grow three-dimensionally with random orientations. We demonstrate that, for 2-methylbenzimidazole (MBI), a molecular ferroelectric that tends to crystalize in spherulites, the quasi-two-dimensional films can be grown using LDRC with highly oriented polar axes and single-crystal-level ferroelectric properties. The crystallization process was shown to occur during the post-deposition annealing process using the in situ electrical measurements. The limited diffusion, low nucleation density, and low activation energy was found critical for the formation of the plate-shaped quasi-two-dimensional films. These results mark an important step in elucidating the LDRC as an effective and general approach for fabricating films with balanced crystallinity and morphology which are critical for applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets

Graph neural networks (GNNs) are a class of Deep Learning models used in designing atomistic materials for effective screening of large chemical spaces. To ensure robust prediction, GNN models must be trained on large volumes of atomistic data on leadership class supercomputers. Even with the advent of modern architectures that consist of multiple storage layers that include node-local NVMe devices in addition to device memory for caching large datasets, extreme-scale model training faces I/O challenges at scale.We present DDStore, an in-memory distributed data store designed for GNN training on large-scale graph data. DDStore provides a hierarchical, distributed, data caching technique that combines data chunking, replication, low-latency random access, and high throughput communication. DDStore achieves near-linear scaling for training a GNN model using up to 1000 GPUs on the Summit and Perlmutter supercomputers, and reaches up to a 6.15x reduction in GNN training time compared to state-of-the-art methodologies.

Choi, Jong Youl↗
DOE OSTI2025

Floquet–Bloch manipulation of the Dirac gap in a topological antiferromagnet

Floquet–Bloch manipulation, achieved by driving a material periodically with a laser pulse, is a method that enables the engineering of electronic and magnetic phases in solids by effectively modifying the structure of their electronic bands. However, the application of Floquet–Bloch manipulation in topological magnetic systems, particularly those with inherent disorder, remains largely unexplored. Here, in this study, we realize Floquet–Bloch manipulation of the Dirac surface-state mass of the topological antiferromagnet MnBi 2 Te 4 . Using time- and angle-resolved photoemission spectroscopy, we show that opposite helicities of mid-infrared circularly polarized light result in substantially different Dirac mass gaps in the antiferromagnetic phase, despite the equilibrium Dirac cone being massless. We explain our findings in terms of a Dirac fermion with a random mass. Our results underscore Floquet–Bloch manipulation as a powerful tool for controlling topology, even in the presence of disorder, and for uncovering properties of materials that may elude conventional probes.

42 ENGINEERING↗
DOE OSTI2024

Geographical Insights into Suicide Mortality Through Spatial Machine Learning

Suicide mortality is a leading cause of death in the United States, with an upward trend that emphasizes its significance as a public health issue. Previous research has employed global models like ordinary least squares (OLS) regression and local models such as geographically weighted regression (GWR). While local models are useful for analyzing spatial variations in suicide mortality, they share limitations with traditional global models, particularly about their inability to handle multi-collinearity and non-linear relationships. Machine learning approaches, like random forests (RF), can address some of these limitations but often fail to account for spatial variability. This gap highlights the need for spatial ML models specifically designed to tackle suicide mortality. This research seeks to fill this void by using a geographically weighted random forest model (GWRF) to examine the associations between county-level suicide mortality in the U.S. from 2010 to 2020 and various social and environmental determinants of health. A key aspect of our methodology is disciplined feature selection, which reduces the pool of explanatory variables by about 90%. This refinement enhances the explanatory power of both global (R2 improved from 0.59 to 0.67) and local (R2 improved from 0.64 to 0.67) RF models while reducing their run times. An analysis of the importance scores for these selected features reveals that the drivers of suicide mortality vary by context. Thus, to effectively address regional disparities and inform targeted public health interventions, a holistic approach that incorporates multiple county-level characteristics is essential.

Lebakula, Viswadeep [ORNL] (ORCID:0000000152935914↗
DOE OSTI2020

Survey of Image Denoising Methods for Medical Image Classification

Medical imaging devices, such as X-ray machines, inherently produce images that suffer from visual noise. Our objectives were to (i.) determine the effect of image denoising on a medical image classification task, and (ii.) determine if there exists a correlation between image denoising performance and medical image classification performance. We performed the medical image classification task on chest X-rays using the DenseNet-121 convolutional neural network (CNN) and used the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics as the image denoising performance measures. We first found that different denoising methods can make a statistically significant difference in classification performance for select labels. We also found that denoising methods affect fine-tuned models more than randomly-initialized models and that fine-tuned models have significantly higher and more uniform performance than randomly-initialized models. Lastly, we found that there is no significant correlation between PSNR and SSIM values and classification performance for our task.

Michael, Peter↗
DOE OSTI2022

Augmented Gaussian random field: Theory and computation

We propose the novel augmented Gaussian random field (AGRF), which is a universal framework incorporating the data of observable and derivatives of any order. Rigorous theory is established. We prove that under certain conditions, the observable and its derivatives of any order are governed by a single Gaussian random field, which is the aforementioned AGRF. As a corollary, the statement "the derivative of a Gaussian process remains a Gaussian process" is validated, since the derivative is represented by a part of the AGRF. Moreover, a computational method corresponding to the universal AGRF framework is constructed. Both noiseless and noisy scenarios are considered. Formulas of the posterior distributions are deduced in a nice closed form. A significant advantage of our computational method is that the universal AGRF framework provides a natural way to incorporate arbitrary order derivatives and deal with missing data. We use four numerical examples to demonstrate the effectiveness of the computational method. The numerical examples are composite function, damped harmonic oscillator, Korteweg-De Vries equation, and Burgers' equation.

Mathematics↗
DOE OSTI2026

Telomere-to-telomere assemblies of chromosome 10 reveal complex adaptive variation of 3-ketoacyl-CoA-synthases in Populus trichocarpa likely driven by Helitrons

The model woody plant Populus trichocarpa displays an atypical alkene-diverse wax cuticle likely driven by copy number variation (CNV) of 3-ketoacyl-CoA synthases ( KCS ), which has been difficult to confirm with short-read assemblies. Long-read sequencing enables the development of telomere-to-telomere resources to detect cryptic variation, including CNVs, which are currently missed. Integrating this information can improve genomic prediction for breeding and provide insights into the evolutionary basis of important traits. Our analysis of 78 long-read haplotypes from chromosome 10 identified more than twice as many KCS genes as previously reported, and numerous intragenic non-synonymous substitutions. Random Forest predictive models highlighted the importance of Potri.010G079500 in producing very long chain alkenes; however, its absence did not predict previously reported alkene-deficient phenotypes. Instead, alkene levels are best predicted by the combinations of KCS copies. Additionally, amino acid substitutions clustered around ligand and donor binding pockets, suggesting they contribute to differing wax cuticle composition. Finally, each KCS gene and copy was linked to a Helitron transposon. A phylogenetic analysis suggests Helitrons are the evolutionary mechanism for generating KCS tandem arrays. Long-read generated telomere-to-telomere assemblies of P. trichocarpa chromosome 10 revealed large-effect loci critical to genetic studies that are unattainable from short-reads. This new resource produced novel insights into genome structure and function, and a novel mechanism for generating tandem gene duplication. Our results highlight that, given current challenges in annotation and assembly, detailed and focused long-read sequences are key to interpreting complex genomic regions that contain tandem copy number variants.

09 BIOMASS FUELS↗
DOE OSTI2024

Ensemble variational Fokker-Planck methods for data assimilation

Particle flow filters solve Bayesian inference problems by smoothly transforming a set of particles into samples from the posterior distribution. Particles move in state space under the flow of an McKean-Vlasov-Itˆo process. This work introduces the Variational Fokker-Planck (VFP) framework for data assimilation, a general approach that includes previously known particle flow filters as special cases. The McKean-Vlasov-Itˆo process that transforms particles is defined via an optimal drift that depends on the selected diffusion term. It is established that the underlying probability density - sampled by the ensemble of particles - converges to the Bayesian posterior probability density. For a finite number of particles the optimal drift contains a regularization term that nudges particles toward becoming independent random variables. Based on this analysis, we derive computationally-feasible approximate regularization approaches that penalize the mutual information between pairs of particles, and avoid particle collapse. Moreover, the diffusion plays a role akin to a particle rejuvenation approach that aims to alleviate particle collapse. The VFP framework is very flexible. Different assumptions on prior and intermediate probability distributions can be used to implement the optimal drift, and localization and covariance shrinkage can be applied to alleviate the curse of dimensionality. A robust implicit-explicit method is discussed for the efficient integration of stiff McKean- Vlasov-Itˆo processes. Here, the effectiveness of the VFP framework is demonstrated on three progressively more challenging test problems, namely the Lorenz ’63, Lorenz ’96 and the quasi-geostrophic equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Constructing a spatiotemporally coherent long-term PM2.5 concentration dataset over China during 1980–2019 using a machine learning approach

The lack of long-term observations and satellite retrievals of health-damaging fine particulate matter in China has demanded the estimates of historical PM 2.5 (particulate matter less than 2.5 µm in diameter) concentrations. This study constructs a gridded near-surface PM 2.5 concentration dataset across China covering 1980–2019 using the space-time random forest model with atmospheric visibility observations and other auxiliary data. The modeled daily PM2.5 concentrations are in excellent agreement with ground measurements during 2015–2019, with a coefficient of determination of 0.95 and mean relative error of 12%. Besides the atmospheric visibility which explains 30% of total importance of variables in the model, both emissions and meteorological conditions are also essential factors affecting PM2.5 predictions. From 1980 to 2014, the model-predicted PM2.5 concentrations increased constantly with the maximum growth rate of 5–10 µg/m3/decade over eastern China. Due to the clean air actions, PM 2.5 concentrations have decreased effectively at a rate over 50 µg/m 3 /decade in the North China Plain and 20–50 µg/m 3 /decade over eastern China during 2014–2019. The newly generated dataset of 1-degree gridded PM 2.5 concentrations for the past 40 years across China provides a useful means for investigating interannual and decadal environmental and climate impacts related to aerosols.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Quantum graph learning and algorithms applied in quantum computer sciences and image classification

Graph and network theory play a fundamental role in quantum computer sciences, including quantum information and computation. Random graphs and complex network theory are pivotal in predicting novel quantum phenomena, where entangled links are represented by edges. Quantum algorithms have been developed to enhance solutions for various network problems, giving rise to quantum graph computing and quantum graph learning (QGL). Here, in this review, we explore graph theory and graph learning methods as powerful tools for quantum computers to generate efficient solutions to problems beyond the reach of classical systems. We delve into the development of quantum complex network theory and its applications in quantum computation, materials discovery, and research. We also discuss quantum machine learning (QML) methodologies for effective image classification using qubits, quantum gates, and quantum circuits. Additionally, the paper addresses the challenges of QGL and algorithms, emphasizing the steps needed to develop flexible QGL solvers. This review presents a comprehensive overview of the fields of QGL and QML, highlights recent advancements, and identifies opportunities for future research.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Extensive analysis of reconstruction algorithms for DESI 2024 baryon acoustic oscillations

Reconstruction of the baryon acoustic oscillation (BAO) signal has been a standard procedure in BAO analyses over the past decade and has helped to improve the BAO parameter precision by a factor of ∼2 on average. The Dark Energy Spectroscopic Instrument (DESI) BAO analysis for the first year (DR1) data uses the “standard” reconstruction framework, in which the displacement field is estimated from the observed density field by solving the linearized continuity equation in redshift space, and galaxy and random positions are shifted in order to partially remove non-linearities. There are several approaches to solving for the displacement field in real survey data, including the multigrid (MG), iterative Fast Fourier Transform (iFFT), and iterative Fast Fourier Transform particle (iFFTP) algorithms. In this work, we analyze these algorithms and compare them with various metrics including two-point statistics and the displacement itself using realistic DESI mocks. We focus on three representative DESI samples, the emission line galaxies (ELG), quasars (QSO), and the bright galaxy sample (BGS), which cover the extreme redshifts and number densities, and potential wide-angle effects. We conclude that the MG and iFFT algorithms agree within 0.4% in post-reconstruction power spectrum on BAO scales with the RecSym convention, which does not remove large-scale redshift space distortions (RSDs), in all three tracers. The RecSym convention appears to be less sensitive to displacement errors than the RecIso convention, which attempts to remove large-scale RSDs. However, iFFTP deviates from the first two; thus, we recommend against using iFFTP without further development. In addition, we provide the optimal settings for reconstruction for five years of DESI observation. The analyses presented in this work pave the way for DESI DR1 analysis as well as future BAO analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The drivers and predictability of wildfire re-burns in the western United States (US)

Evidence is mounting that the effectiveness of using prescribed burns as a management tactic may be diminishing due to the higher incidence of wildfire re-burns. The development of predictive models of re-burns is thus essential to better understand their primary drivers so that forest management practices can be updated to account for these events. First, we assess the potential for human activity as a driver of re-burns by evaluating re-burn trends both within and outside of the wildland–urban interface (WUI) of the western US. Next, we investigate the predictability of re-burns through the application of both random forest and the explanatory machine learning non-negative matrix factorization using k-means clustering (NMFk) algorithms to predict re-burn occurrence over California based on a number of climate factors. Our findings indicate that while most states showed increasing trends within the WUI when trends were conducted over longer moving windows (e.g. 20 years), California was the only state where the rate of increase was consistently higher in the WUI, indicating a stronger potential for human activity as a driver in that location. Furthermore, we find model performance was found to be robust over most of California (Testing F1 scores = 0.688), although results were highly variable based on EPA level III Ecoregion (F1 scores = 0.0–0.778). Insights provided from this study will lead to a better understanding of climate and human activity drivers of re-burns and how these vary at broad spatial scales so that improvements in forest management practices can be tuned according to the level of change that is expected for a given region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Modeling intrinsic galaxy alignment in the MICE simulation

The intrinsic alignment (IA) of galaxies is potentially a major limitation in deriving cosmological constraints from weak lensing surveys. In order to investigate this effect, we assign intrinsic shapes and orientations to galaxies in the light-cone output of the MICE simulation, spanning ~ 5000 deg 2 and reaching redshift Z = 1.4. This assignment is based on a semianalytic IA model that uses photometric properties of galaxies as well as the spin and shape of their host halos. Advancing on previous work, we include more realistic distributions of galaxy shapes and a luminosity-dependent galaxy-halo alignment. The IA model parameters are calibrated against COSMOS and BOSS LOWZ observations. The null detection of IA in observations of blue galaxies is accounted for by setting random orientations for these objects. We compare the two-point alignment statistics measured in the simulation against predictions from the analytical IA models NLA and TATT over a wide range of scales, redshifts, and luminosities for red and blue galaxies separately. We find that both models fit the measurements well at scales above 8h -1 Mpc, while TATT outperforms NLA at smaller scales. The IA parameters derived from our fits are in broad agreement with various observational constraints from red galaxies. Finally, we build a realistic source sample, mimicking DES Year 3 observations and use it to predict the IA contamination to the observed shear statistics. We find this prediction to be within the measurement uncertainty, which might be a consequence of the random alignment of blue galaxies in the simulation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Interactions Between Climate Mean and Variability Drive Future Agroecosystem Vulnerability

ABSTRACT Agriculture is crucial for global food supply and dominates the Earth's land surface. It is unknown, however, how slow but relentless changes in climate mean state, versus random extreme conditions arising from changing variability , will affect agroecosystems' carbon fluxes, energy fluxes, and crop production. We used an advanced weather generator to partition changes in mean climate state versus variability for both temperature and precipitation, producing forcing data to drive factorial‐design simulations of US Midwest agricultural regions in the Energy Exascale Earth System Model. We found that an increase in temperature mean lowers stored carbon, plant productivity, and crop yield, and tends to convert agroecosystems from a carbon sink to a source, as expected; it also can cause local to regional cooling in the earth system model through its effects on the Bowen Ratio. The combined effect of mean and variability changes on carbon fluxes and pools was nonlinear, that is, greater than each individual case. For instance, gross primary production reduces by 9%, 1%, and 13% due to change in mean temperature, change in temperature variability, and change in both temperature mean and variability, respectively. Overall, the scenario with change in both temperature and precipitation means leads to the largest reduction in carbon fluxes (−16% gross primary production), carbon pools (−35% vegetation carbon), and crop yields (−33% and −22% median reduction in yield for corn and soybean, respectively). By unambiguously parsing the effects of changing climate mean versus variability and quantifying their nonadditive impacts, this study lays a foundation for more robust understanding and prediction of agroecosystems' vulnerability to 21st‐century climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Initial plant community responses to hardwood control treatments in restoration of remnant longleaf pine (Pinus palustris) woodlands

Changes in land use over the past century have contributed to substantial losses of longleaf pine (Pinus palustris) woodlands in the southeastern USA and replacement with higher density, mixed pine and hardwood stands that suppress understory development and limit application of prescribed fire. To increase understanding of limiting factors and identify potential approaches for restoring longleaf pine woodlands, we studied in this work initial availability of light and soil water and 2-year (2018–2019) plant community responses after controlling overstory hardwoods in five remnant longleaf pine woodlands having no evidence of previous agriculture at the Savannah River Site near Aiken, SC, USA. Seven hardwood control treatments and a non-treated check were compared in a randomized compete block experiment: cutting, cutting + shredding of logging residues, stem injection with imazapyr herbicide, cutting + basal spray with imazapyr herbicide, cutting + basal spray with triclopyr herbicide, cutting + directed foliar spray with a mixture of glyphosate and imazapyr herbicides, and cutting + broadcast foliar spray with the same herbicide mixture. In the year prior to hardwood cutting (2016), understory light availability averaged 23% of full sun. Throughout the year of treatment installation (2017), average soil water content (SWC) was below permanent wilting point (5.5% SWC) where overstory hardwoods were retained; whereas, it was above wilting point (7.5%) where they had been cut. Thus, combined effects of shade and root competition from overstory hardwoods probably limited cover of herbaceous species in the non-treated check. In the 2 years following treatment, hardwood survival averaged > 99%, 62%, 42% and < 1% for the non-treated check, cutting, shredding, and herbicide treatments, respectively. Relative to the non-treated check, herbaceous species richness was increased by shredding, stem injection, basal spray, or directed foliar treatments; herbaceous cover was increased by basal spray or directed foliar treatments; and woody cover was decreased by directed or broadcast foliar treatments. The directed foliar spray was the most effective treatment for achieving many of the desired understory characteristics of a longleaf pine woodland, including a diverse understory dominated by herbaceous vegetation capable of supporting periodic prescribed fires. Although the imazapyr basal spray treatment increased cover of remnant woodland indicator species, none of the treatments increased richness of this species group, suggesting that restoration of native species composition will be expedited by enrichment seedings or plantings.

60 APPLIED LIFE SCIENCES↗