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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Ultrafast photoinduced charge carrier dynamics of L-cysteine and oleylamine stabilized CsPbBr3 perovskite quantum dots coupled with gold nanoparticles

We have synthesized L-cysteine and oleylamine stabilized CsPbBr3 perovskite quantum dots (PQDs) and coupled them with gold nanoparticles (AuNPs). The PQDs and AuNPs, as well as their hybrid nanostructures (HNS), were characterized using UV–visible (UV–vis) and photoluminescence (PL) spectroscopy. The UV–vis spectra show absorption bands of the HNS at 503 and 520 nm, attributed mainly to PQDs and AuNPs, respectively. The PQDs show a strong excitonic PL band peaked at 513 nm from PQDs. The HR-TEM results show the formation of hybrid structures between PQDs and AuNPs, which is also supported by the PL quenching of the PQDs by the coupled AuNPs. Ultrafast dynamics of the exciton and charge carriers in the HNS and pristine PQD were studied using femtosecond transient absorption. Multiexponential fitting of the dynamic data revealed the existence of shallow and deep trap states in pristine PQDs and ultrafast electron transfer from PQDs to AuNPs in the HNS. A kinetic model was proposed to account for the key dynamic processes involved and to extract the time for electron transfer from PQDs to AuNPs in the HNS, found to be ∼2 ps. Dynamic processes in pristine PQDs are largely unchanged by HNS formation with AuNPs.

Chemistry↗
DOE OSTI2015

Stripped elliptical galaxies as probes of ICM physics. II. Stirred, but mixed? Viscous and inviscid gas stripping of the Virgo elliptical M89

Elliptical galaxies moving through the intracluster medium (ICM) are progressively stripped of their gaseous atmospheres. X-ray observations reveal the structure of galactic tails, wakes, and the interface between the galactic gas and the ICM. This fine-structure depends on dynamic conditions (galaxy potential, initial gas contents, orbit in the host cluster), orbital stage (early infall, pre-/post-pericenter passage), as well as on the still ill-constrained ICM plasma properties (thermal conductivity, viscosity, magnetic field structure). Paper I describes flow patterns and stages of inviscid gas stripping. Here we study the effect of a Spitzer-like temperature dependent viscosity corresponding to Reynolds numbers, Re, of 50–5000 with respect to the ICM flow around the remnant atmosphere. Global flow patterns are independent of viscosity in this Reynolds number range. Viscosity influences two aspects. In inviscid stripping, Kelvin–Helmholtz instabilities (KHIs) at the sides of the remnant atmosphere lead to observable horns or wings. Increasing viscosity suppresses KHIs of increasing length scale and thus observable horns and wings. Furthermore, in inviscid stripping, stripped galactic gas can mix with the ambient ICM in the galaxy’s wake. This mixing is suppressed increasingly with increasing viscosity, such that viscously stripped galaxies have long X-ray bright, cool wakes. We provide mock X-ray images for different stripping stages and conditions. While these qualitative results are generic, we tailor our simulations to the Virgo galaxy M89 (NGC 4552), where Re≈50 corresponds to a viscosity of 10% of the Spitzer level. Paper III compares new deep Chandra and archival XMM-Newton data to our simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Linking galaxies to dark matter halos at z ∼ 1: Dependence of galaxy clustering on stellar mass and specific star formation rate

We study the dependence of angular two-point correlation functions on stellar mass (M{sub *}) and specific star formation rate (sSFR) of M{sub ∗}>10{sup 10}M{sub ⊙} galaxies at z∼1. The data from the UK Infrared Telescope Infrared Deep Sky Survey Deep eXtragalactic Survey and Canada–France–Hawaii Telescope Legacy Survey cover 8.2 deg{sup 2} sample scales larger than 100 h{sup −1} Mpc at z∼1, allowing us to investigate the correlation between clustering, M{sub *}, and star formation through halo modeling. Based on halo occupation distributions (HODs) of M{sub *} threshold samples, we derive HODs for M{sub *} binned galaxies, and then calculate the M{sub ∗}/M{sub halo} ratio. The ratio for central galaxies shows a peak at M{sub halo}∼10{sup 12}h{sup −1}M{sub ⊙}, and satellites predominantly contribute to the total stellar mass in cluster environments with M{sub ∗}/M{sub halo} values of 0.01–0.02. Using star-forming galaxies split by sSFR, we find that main sequence galaxies (log sSFR/yr{sup −1}∼−9) are mainly central galaxies in ∼10{sup 12.5}h{sup −1}M{sub ⊙} halos with the lowest clustering amplitude, while lower sSFR galaxies consist of a mixture of both central and satellite galaxies where those with the lowest M{sub *} are predominantly satellites influenced by their environment. Considering the lowest M{sub halo} samples in each M{sub *} bin, massive central galaxies reside in more massive halos with lower sSFRs than low mass ones, indicating star-forming central galaxies evolve from a low M{sub *}–high sSFR to a high M{sub *}–low sSFR regime. We also find that the most rapidly star-forming galaxies (log sSFR/yr{sup −1}>−8.5) are in more massive halos than main sequence ones, possibly implying galaxy mergers in dense environments are driving the active star formation. These results support the conclusion that the majority of star-forming galaxies follow secular evolution through the sustained but decreasing formation of stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Evolution of star formation properties of high-redshift cluster galaxies since z = 2

Using a stellar mass-limited sample of ∼46,600 galaxies (M{sub ∗}>10{sup 9.1} M{sub ⊙}) at 0.5 10{sup 10} M{sub ⊙} out to z = 2. On the other hand, the environmental quenching becomes efficient at z<1 regardless of galaxy mass, and it serves as a main mechanism for quenching star formation for lower mass galaxies. Our result is based on deep optical and near-infrared imaging data over 2800 arcmin{sup 2}, enabling us to negate cosmic variance and identify 46 galaxy cluster candidates with M∼10{sup 14} M{sub ⊙}. From M{sub ∗}∼10{sup 9.5} to 10{sup 10.5} M{sub ⊙}, the fraction of quiescent galaxies increases by a factor of ∼10 over the entire redshift range, but the difference between cluster and field environment is negligible. Rapid evolution in the quiescent fraction is seen from z = 2 to z = 1.3 for massive galaxies, suggesting a build-up of massive quiescent galaxies at z>1.3. For galaxies with M{sub ∗}<10{sup 10} M{sub ⊙} at z<1.0, the quiescent fraction is found to be as much as a factor of 2 larger in clusters than in field, showing the importance of environmental quenching in low-mass galaxies at low redshift. Most high-mass galaxies are already quenched at z>1, therefore environmental quenching does not play a significant role for them, although the efficiency of environmental quenching is nearly identical between high- and low-mass galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Pushing automated morphological classifications to their limits with the Dark Energy Survey

ABSTRACT We present morphological classifications of ∼27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classification scheme separates: (a) early-type galaxies (ETGs) from late-type galaxies (LTGs); and (b) face-on galaxies from edge-on. Our convolutional neural networks (CNNs) are trained on a small subset of DES objects with previously known classifications. These typically have mr ≲ 17.7 mag; we model fainter objects to mr < 21.5 mag by simulating what the brighter objects with well-determined classifications would look like if they were at higher redshifts. The CNNs reach 97 per cent accuracy to mr < 21.5 on their training sets, suggesting that they are able to recover features more accurately than the human eye. We then used the trained CNNs to classify the vast majority of the other DES images. The final catalogue comprises five independent CNN predictions for each classification scheme, helping to determine if the CNN predictions are robust or not. We obtain secure classifications for ∼87 per cent and 73 per cent of the catalogue for the ETG versus LTG and edge-on versus face-on models, respectively. Combining the two classifications (a) and (b) helps to increase the purity of the ETG sample and to identify edge-on lenticular galaxies (as ETGs with high ellipticity). Where a comparison is possible, our classifications correlate very well with Sérsic index (n), ellipticity (ϵ), and spectral type, even for the fainter galaxies. This is the largest multiband catalogue of automated galaxy morphologies to date.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The luminosity function of ringed galaxies

ABSTRACT We perform an analysis of the luminosity functions (LFs) of two types of ringed galaxies – polar-ring galaxies and collisional ring galaxies – using data from the Sloan Digital Sky Survey (SDSS). Both classes of galaxies were formed as a result of interaction with their environment and they are very rare objects. We constructed LFs of galaxies by different methods and found their approximations by the Schechter function. The luminosity functions of both types of galaxies show a systematic fall-off at low luminosities. The polar structures around bright (Mr ≤ −20m) and red (g − r > +0.8) galaxies are about twice as common as around blue ones. The LF of collisional rings is shifted towards brighter luminosities compared to polar-ring galaxies. We analysed the published data on the ringed galaxies in several deep fields and confirmed the increase in their volume density with redshift: up to z ∼ 1 their density grows as (1 + z)m, where m ≳ 5.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Heat transport in liquid water from first-principles and deep neural network simulations

In this work, we compute the thermal conductivity of water within linear response theory from equilibrium molecular dynamics simulations, by adopting two different approaches. In one, the potential energy surface (PES) is derived on the fly from the electronic ground state of density functional theory (DFT) and the corresponding analytical expression is used for the energy flux. In the other, the PES is represented by a deep neural network (DNN) trained on DFT data, whereby the PES has an explicit local decomposition and the energy flux takes a particularly simple expression. By virtue of a gauge invariance principle, established by Marcolongo, Umari, and Baroni, the two approaches should be equivalent if the PES were reproduced accurately by the DNN model. We test this hypothesis by calculating the thermal conductivity, at the GGA (PBE) level of theory, using the direct formulation and its DNN proxy, finding that both approaches yield the same conductivity, in excess of the experimental value by approximately 60%. Besides being numerically much more efficient than its direct DFT counterpart, the DNN scheme has the advantage of being easily applicable to more sophisticated DFT approximations, such as meta-GGA and hybrid functionals, for which it would be hard to derive analytically the expression of the energy flux. We find in this way that a DNN model, trained on meta-GGA (SCAN) data, reduces the deviation from experiment of the predicted thermal conductivity by about 50%, leaving the question open as to whether the residual error is due to deficiencies of the functional, to a neglect of nuclear quantum effects in the atomic dynamics, or, likely, to a combination of the two.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Hyper Suprime-Cam Year 3 results: Cosmology from galaxy clustering and weak lensing with HSC and SDSS using the emulator based halo model

We present cosmology results from a blinded joint analysis of cosmic shear, galaxy-galaxy weak lensing, and projected galaxy clustering, measured from the Hyper Suprime-Cam three-year (HSC-Y3) shape catalog and the Sloan Digital Sky Survey (SDSS) DR11 spectroscopic galaxy catalog—a cosmology analysis. We define luminosity-cut, and therefore nearly volume-limited, samples of SDSS galaxies to serve as the tracers of w p and as the lens samples for Δ⁢Σ in three spectroscopic redshift bins spanning the range 0.15± and Δ⁢Σ measurements, we use a single sample of about seven million source galaxies over 416 deg 2 , selected from HSC-Y3 based on having photometric redshifts (photo-z) greater than 0.75. The deep, high-quality HSC-Y3 data enable significant detections of the Δ⁢Σ signals, with integrated signal-to-noise ratio S/N~24 in the range 3≤R/[ℎ –1 Mpc]≤30 over the three lens samples. ξ ± has S/N ~19 in the range 8' ≤$\theta$ ≤5⁢0' and 30' ≤$\theta$ ≤15⁢0' for ξ + and ξ – , respectively. For cosmological parameter inference, we use the dark emulator package, combined with a halo occupation distribution prescription for the relation between galaxies and halos, to model w p and Δ⁢Σ down to quasinonlinear scales, and we estimate cosmological parameters after marginalizing over nuisance parameters. In our baseline analysis we employ an uninformative flat prior of the residual photo-z error, given by Π⁡(Δz ph )=$U$⁡(–1,1), to model a residual bias in the mean redshift of HSC source galaxies. Comparing the relative lensing amplitudes for Δ⁢Σ in the three redshift bins and for ξ ± with the single HSC source galaxy sample allows us to calibrate the photo-z parameter Δ⁢z ph to the precision of σ⁡(Δ⁢z ph )≃0.09. With these methods, we obtain a robust constraint on the cosmological parameters for the flat Λ⁢CDM model: S 8 =σ 8 ⁢(Ω m /0.3) 0.5 =$0.763^{+0.040}_{–0.036}$, or the best-constrained parameter given by $S^{'}_{8}$ = σ 8 ⁢(Ω m /0.3) 0.22 =0.721±0.028, determined with about 4% fractional precision. Based on multidimensional tension metrics, HSC-Y3 data exhibits about 2.5σ tension with the cosmological constraint inferred by Planck for the Λ⁢CDM model, and hints at a nonzero residual photo-z bias implying that the true mean redshift of the HSC galaxies at z ≳0.75 is higher than that implied by the original photo-z estimates.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

We introduce a scheme for molecular simulations, the deep potential molecular dynamics (DPMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with ab initio data. The neural network model preserves all the natural symmetries in the problem. It is first-principles based in the sense that there are no ad hoc components aside from the network model. We show that the proposed scheme provides an efficient and accurate protocol in a variety of systems, including bulk materials and molecules. In all these cases, DPMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Pixel-Resolved Long-Context Learning for Turbulence at Exascale: Resolving Small-scale Eddies Toward the Viscous Limit

Turbulence plays a crucial role in multiphysics applications, including aerodynamics, fusion, and combustion. Accurately capturing turbulence's multiscale characteristics is essential for reliable predictions of multiphysics interactions, but remains a grand challenge even for exascale supercomputers and advanced deep learning models. The extreme-resolution data required to represent turbulence, ranging from billions to trillions of grid points, pose prohibitive computational costs for models based on architectures like vision transformers. To address this challenge, we introduce a multiscale hierarchical Turbulence Transformer that reduces sequence length from billions to a few millions and a novel RingX sequence parallelism approach that enables scalable long-context learning. We perform scaling and science runs on the Frontier supercomputer. Our approach demonstrates excellent performance up to 1.1 EFLOPS on 32,768 AMD GPUs, with a scaling efficiency of 94\%. To our knowledge, this is the first AI model for turbulence that can capture small-scale eddies down to the dissipative range in three-dimensional turbulence at high Reynolds numbers.

Yin, Junqi [ORNL] (ORCID:0000000338435520)↗
DOE OSTI2025

Postearthquake Damage Mapping via Remote Sensing: Lessons From the 2023 Türkiye Disaster

This review addresses the urgent need for scalable, accurate, and reproducible remote sensing solutions following the February 2023 Türkiye earthquakes. It synthesizes the contributions of five peer-reviewed studies published in the IEEE JSTARS Special Issue on postearthquake damage and risk assessment. These studies cover areas such as damage classification with deep learning, fusion of multisource remote sensing data, creation of benchmark datasets, detailed damage mapping, and analysis of geophysical signals using outgoing longwave radiation. The article summarizes the methodological approaches and the practical relevance of the reviewed studies for detecting, evaluating, and quantifying damage, and outlines key challenges, including model generalization, class ambiguity, and data integration. It also discusses emerging trends, including explainable artificial intelligence, multimodal data fusion, and open-data platforms. This synthesis provides a foundation for building robust, interpretable, and real-time disaster response systems and aims to guide future research in earthquake-related Earth observation and rapid damage assessment.

Taskin, Gulsen [Istanbul Technical University] (OR↗
DOE OSTI2021

High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function

Computational biology is one of many scientific disciplines ripe for innovation and acceleration with the advent of high-performance computing (HPC). In recent years, the field of machine learning has also seen significant benefits from adopting HPC practices. In this work, we present a novel HPC pipeline that incorporates various machine-learning approaches for structure-based functional annotation of proteins on the scale of whole genomes. Our pipeline makes extensive use of deep learning and provides computational insights into best practices for training advanced deep-learning models for high-throughput data such as proteomics data. We showcase methodologies our pipeline currently supports and detail future tasks for our pipeline to envelop, including large-scale sequence comparison using SAdLSA and prediction of protein tertiary structures using AlphaFold2.

Gao, Mu↗
DOE OSTI2021

Intelliquench: An Adaptive Machine Learning System for Detection of Superconducting Magnet Quenches

In superconducting magnets, the irreversible transition of a portion of the conductor to resistive state is called a “quench.” Having large stored energy, magnets can be damaged by quenches due to localized heating, high voltage, or large force transients. Unfortunately, current quench protection systems can only detect a quench after it happens, and mitigating risks in Low Temperature Superconducting (LTS) accelerator magnets often requires fast response (down to ms). Additionally, protection of High Temperature Superconducting (HTS) magnets is still suffering from prohibitively slow quench detection. In this study, we lay the groundwork for a quench prediction system using an auto-encoder fully-connected deep neural network. After dynamically trained with data features extracted from acoustic sensors around the magnet, the system detects anomalous events seconds before the quench in most of our data. While the exact nature of the events is under investigation, we show that the system can “forecast” a quench before it happens under magnet training conditions through a randomized experiment. This opens up the way of integrated data processing, potentially leading to faster and better diagnostics and detection of magnet quenches

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Risk-Aware Measurement Synchronization and Recovery for DSSE With Heterogeneous Data Sources

Power distribution systems are increasingly integrating heterogeneous sensors with varying data reporting rates and types, which pose challenges to achieving observability at the desired temporal resolution of distribution system state estimation (DSSE). Multisensor failures caused by extreme events exacerbate these issues, introducing substantial uncertainties into DSSE. This article proposes a novel solution to these challenges by ensuring high-resolution system observability despite heterogeneous data sources and multisensor failures. First, a deep learning architecture combining long short-term memory (LSTM) and graph convolutional network (GCN) is employed to synchronize meters with different reporting rates, aiming to achieve system observability. A random-walk-model-based approach is introduced to generate pseudo-measurements while properly characterizing their uncertainties under multisensor failures. Finally, a disaster-risk-informed observability metric (RiOM) is defined to quantify the uncertainty associated with state estimation results. The proposed framework offers deeper insights into the system observability on the fly compared with conventional analysis. The effectiveness of the framework is demonstrated on an IEEE standard test case and a large-scale real-world distribution feeder in mid-Minnesota in the U.S.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Amazon drought resilience – emerging results point to new empirical needs

Will climate change turn tropical forests from a carbon sink to a source (Pan et al., 2011)? Tropical forests cycle more carbon, water, and energy than any other biome (Bonan, 2008). The Amazon is the largest tropical forest, spanning one-third of South America, and any perturbation in its function has repercussions on the global climate. Mild and severe droughts in the Amazon are predicted to double and triple in area, respectively, by 2100, while the area under wet extremes will increase (Duffy et al., 2015). Our understanding of the role of plants' hydrological environments in determining their drought response, however, is limited (Chitra-Tarak et al., 2018, 2021). An important review by Costa et al. (2023; pp. 714–733) published in this issue of New Phytologist highlights that shallow-water table (WT) forests constitute c. 50% of the Amazon and may act as hydrological refugia during droughts, yet they have been neglected in Amazon forest research to date. Emerging results suggest that the shallow-WT forests that dominate the Amazon basin may increase in productivity under moderate droughts due to relief from hypoxia, suggesting a potential buffer from drought relative to deeper WT forests (Fig. 1). During severe droughts, however, shallow-WT forests may be vulnerable to collapse due to drought-intolerant traits. Addressing the underrepresentation of relatively drought-resilient shallow-WT forests in the Amazon's forest inventories may help resolve the much-debated incongruence in recent studies of the Amazon's drought resilience. Data from forest inventories (biased toward deep-WT forests) indicate that the Amazon's carbon sink is declining, partially because of drought impacts (Phillips et al., 2009; Brienen et al., 2015), whereas basin-wide satellite-based measures of gross primary productivity, which include shallow-WT forests, indicate varied regional responses to droughts (Saleska et al., 2007; Brando et al., 2010). Costa et al.'s (2023) descriptive insights of the intricate balance of geology, topography, hydrology, vegetation, and drought on ecosystem function, as well as their conceptual predictive framework, are useful for developing new empirical research in these understudied ecosystems and improving Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning

Abstract One of the critical components in Industrial Gas Turbines (IGT) is the turbine blade. Design of turbine blades needs to consider multiple aspects like aerodynamic efficiency, durability, safety and manufacturing, which make the design process sequential and iterative. The sequential nature of these iterations forces a long design cycle time, ranging from several months to years. Due to the reactionary nature of these iterations, little effort has been made to accumulate data in a manner that allows for deep exploration and understanding of the total design space. This is exemplified in the process of designing the individual components of the IGT resulting in a potential unrealized efficiency. To overcome the aforementioned challenges, we demonstrate a probabilistic inverse design machine learning framework, namely PMI (PMI), to carry out an explicit inverse design. PMI calculates the design explicitly without costly iteration and overcomes the challenges associated with ill-posed inverse problems. In this work the framework will be demonstrated on inverse aerodynamic design of three-dimensional turbine blades.

Engineering↗
DOE OSTI2017

Superconducting properties of (Pb{sub 0.05}Sn{sub 0.95})Te doped with indium under conditions of hydrostatic compression

The superconducting properties of Pb{sub 0.05}Sn{sub 0.95}Te semiconductor alloy doped with 5 at % of In are investigated at a hydrostatic pressure of P < 7 kbar. At increasing pressure P > 1.35 kbar and T ≥ 1.3 K, the resistivity step to ρ = 0 in the temperature and magnetic-field dependences ρ(T) and ρ(H), which corresponds to the transition to the superconducting state, disappears. The experimental results are indicative of a decrease in the density of states at the Fermi level with increasing pressure, which can be interpreted as an indium-level shift deep into the valence band. The obtained data refine and supplement the results of studies devoted to investigation of the baric dependences of the critical parameters of the superconducting transition in (Pb{sub z}Sn{sub 1–z}){sub 0.95}In{sub 0.05}Te with varying lead content z.

36 MATERIALS SCIENCE↗

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