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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Low-Lying Excited States of Linear All- Trans Polyenes: Insights from Analytic Gradient and Nonadiabatic Coupling Calculations Based on Multireference Configuration Interaction

Polyenes serve as a rigorous test for theoretical models and electronic structure methods, playing a key role in advancing computational and theoretical chemistry. Here, we present a high-level theoretical investigation of linear, all-trans polyenes using energy gradients and nonadiabatic coupling vectors based on an MR-CISD wave function to describe electronic transitions involving the ground state (1 1 A g – ) and three low-lying excited states (2 1 A g – , 1 1 B u + , and 2 1 B u – ) of hexatriene, octatetraene, and decapentaene. This approach enables accurate evaluation of both adiabatic and vertical excitation and emission energies, yielding results in excellent agreement with experiment, as well as locating minima on the crossing seam between adiabatic states. Our results show that vertical excitation energies to the 1 1 B u + state are blue-shifted by 0.2–0.3 eV relative to the experimental absorption maximum, whereas the vertical emission energy from the 2 1 A g – state is red-shifted by ∼0.2 eV relative to the experimental emission maximum. Upon relaxation from the Franck–Condon geometry, the 2 1 A g – state stabilizes by around 1 eV, compared to 0.2–0.3 eV for the 1 1 B u + state. An analysis of the S 1 /S 0 crossing seam in hexatriene shows that its minimum involves asymmetric backbone deformations and provides an efficient channel for ultrafast internal conversion to the ground state, consistent with the absence of detectable fluorescence in this molecule. These results demonstrate the power of analytic gradients and nonadiabatic coupling vectors based on an MR-CISD wave function for accurately characterizing the electronic structure and photophysics of polyenes.

Excited states↗
DOE OSTI2025

COLUMBUS─An Efficient and General Program Package for Ground and Excited State Computations Including Spin–Orbit Couplings and Dynamics

The COLUMBUS program system provides the tools for performing high-level multireference (MR) computations, including the multireference configuration interaction (MRCI) method and its multireference averaged quadratic coupled cluster (MR-AQCC) extension, allowing computations on a wide range of fascinating atomic and molecular systems, including the treatment of open-shells and complicated excited state phenomena. The inclusion of spin−orbit coupling (SOC) directly within the MRCI step enables the description of systems containing heavy elements, such as lanthanides and actinides, whose properties are strongly influenced by SOC. Analytic energy gradients and nonadiabatic couplings at the correlated MRCI level provide the foundation for a variety of dynamics studies, giving insight into ultrafast photochemistry. New and ongoing method developments in COLUMBUS include the computation of spin densities, improved descriptions of ionic states, enhancements to the AQCC method, and the porting of COLUMBUS to graphical processing units (GPUs). New external interfaces enable an enhanced description of electronic resonances and molecules in strong laser fields. This work highlights these new developments while providing a detailed account of the diverse applications of COLUMBUS in recent years.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A Mass Conservation Relaxed (MCR) LSTM Model for Streamflow Simulation Across CONUS

The recent development of the physics-aware Mass-Conserving Long Short-Term Memory network (MC-LSTM) provides an alternative to other data-driven Deep Learning (DL) models in hydrology. Mass-Conserving Long Short-Term Memory incorporates mass conservation directly into the LSTM architecture. Despite the theoretical advancements, studies have reported a surprisingly limited performance of the MC-LSTM in streamflow simulation. We hypothesize that such a limitation is due to the unrealistic mass conservation scheme in MC-LSTM, which overlooks unobserved incoming water fluxes beyond precipitation. As an attempt to verify this hypothesis, we propose a Mass Conservation Relaxed LSTM (MCR-LSTM), which incorporates a bi-directional mass relaxation (MR) component to account for potential incoming water fluxes beyond precipitation. We train and test the proposed MCR-LSTM model across 531 watersheds in the contiguous United States (CONUS) against three baseline models: the Sacramento Soil Moisture Accounting, LSTM, and MC-LSTM. Our results show that MCR-LSTM outperforms MC-LSTM despite its underperformance compared to LSTM. Specifically, MCR-LSTM's advantage over MC-LSTM is mainly seen in the Plains and Western U.S., where the newly incorporated MR component better simulates water loss and suggests the likely existence of additional incoming water fluxes beyond precipitation, respectively. The novelty and contribution of this study are twofold: firstly, it introduces an alternative physics-aware DL tool (i.e., MCR-LSTM) in hydrology with higher accuracy in specific regions compared to MC-LSTM. Secondly, it provides a diagnosis of regions where strict, precipitation-based mass conservation constraints may be unrealistic in streamflow simulation.

deep learning↗
DOE OSTI2025

Detection of Diversion in a Realistic Heat Pipe Microreactor Using Supervised Machine Learning

Microreactors (MRs) pose new challenges for international safeguards. Here, their small size and mass reproducibility make them ideal for deployment in greater numbers and in remote locations, making the job of safeguards inspectors more challenging. Machine learning (ML) is currently being applied to many fields to augment human performance and increase automation; in particular, ML could be used to provide insight for international inspectors to help detect the diversion of nuclear fuel from MR cores. Four ML model types (k-nearest neighbors, decision tree, random forest, and histogram-based gradient boosted ensemble) were trained on integrated flux and critical control drum angle data generated with Serpent 2 for a realistic heat pipe MR design, achieving nearly 100% binary classification accuracy of nominal and diversion core configurations by the end of 1 full power year for three of the four model types. Regression model variants were also trained, using the same input data, for predicting the number of fuel pins diverted. Root-mean-square errors below 5% of the total number of fuel pins were achieved by the 1 full power year mark for all models.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Magnetic polaron formation in EuZn2P2

Colossal magnetoresistance (CMR) has been observed across many Eu2+-based materials; however, its origin is not completely understood. Here we investigate the antiferromagnetic insulator EuZn2⁢P2 through single crystal x-ray diffraction, transmission electron microscopy, electrical transport, magnetization, dilatometry, and electron spin resonance measurements complemented by density functional theory calculations. Our electrical resistivity data reveal a large negative magnetoresistance, MR=[𝑅⁡(𝐻)−𝑅⁡(0)]/𝑅⁡(0), that reaches MR=−99.7% at 9 T near the antiferromagnetic ordering temperature 𝑇𝑁=23K. Dilatometry measurements show an accompanying field-induced lattice strain. Additionally, Eu2+ electron spin resonance reveals a strong ferromagnetic exchange interaction between Eu2+ and conduction electrons. Our experimental results in EuZn2⁢P2 are consistent with a magnetic polaron scenario and suggest magnetic polaron formation as a prevailing explanation of CMR in Eu2+-based compounds.

Cook, Matt [ORNL] (ORCID:0000000288777505)↗
DOE OSTI2025

Bayesian model mixing with multireference energy density functional

Reliably predicting nuclear properties across the entire chart of isotopes is important for applications ranging from nuclear astrophysics to superheavy science to nuclear technology. To this day, however, all the theoretical models that can scale at the level of the chart of isotopes remain semiphenomenological. Because they are fitted locally, their predictive power can vary significantly; different versions of the same theory provide different predictions. Bayesian model mixing takes advantage of such imperfect models to build a local mixture of a set of models to make improved predictions. Earlier attempts to use Bayesian model mixing for mass table calculations relied on models treated at single-reference energy density functional level, which fail to capture some of the correlations caused by configuration mixing or the restoration of broken symmetries. In this study we have applied Bayesian model mixing techniques within a multireference energy density functional (MR-EDF) framework. We considered predictions of two-particle separation energies from particle number projection or angular momentum projection with four different energy density functionals—a total of eight different MR-EDF models. We used a hierarchical Bayesian stacking framework with a Dirichlet prior distribution over weights together with an inverse log-ratio transform to enable positive correlations between different models. We found that Bayesian model mixing provides significantly improved predictions compared to the participating models. Published by the American Physical Society 2025

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Microreactor Assembly Transportation Cask Model Description for Criticality Safety Validation Basis Assessment

Criticality safety analyses are completed on a transportation cask used for microreactor assembly shipment to provide an example of model and analysis to industry for reproducing this type of study on their microreactor fuel shipment. The fuel assembly considered is based on a gas-cooled microreactors (GC-MR), which utilizes HALEU fuel in the form of TRISO particles and utilizes various design options considered in industry designs. Various versions of this GC-MR assembly were studied, with and without YH 2 moderator, providing similar conclusions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Section-level genome sequencing and comparative genomics of Aspergillus sections Cavernicolus and Usti.

The genus Aspergillus is diverse, including species of industrial importance, human pathogens, plant pests, and model organisms. Aspergillus includes species from sections Usti and Cavernicolus, which until recently were joined in section Usti, but have now been proposed to be non-monophyletic and were split by section Nidulantes, Aenei and Raperi. To learn more about these sections, we have sequenced the genomes of 13 Aspergillus species from section Cavernicolus (A. cavernicola, A. californicus, and A. egyptiacus), section Usti (A. carlsbadensis, A. germanicus, A. granulosus, A. heterothallicus, A. insuetus, A. keveii, A. lucknowensis, A. pseudodeflectus and A. pseudoustus), and section Nidulantes (A. quadrilineatus, previously A. tetrazonus). We compared these genomes with 16 additional species from Aspergillus to explore their genetic diversity, based on their genome content, repeat-induced point mutations (RIPs), transposable elements, carbohydrate-active enzyme (CAZyme) profile, growth on plant polysaccharides, and secondary metabolite gene clusters (SMGCs). All analyses support the split of section Usti and provide additional insights: Analyses of genes found only in single species show that these constitute genes which appear to be involved in adaptation to new carbon sources, regulation to fit new niches, and bioactive compounds for competitive advantages, suggesting that these support species differentiation in Aspergillus species. Sections Usti and Cavernicolus have mainly unique SMGCs. Section Usti contains very large and information-rich genomes, an expansion partially driven by CAZymes, as section Usti contains the most CAZyme-rich species seen in genus Aspergillus. Section Usti is clearly an underutilized source of plant biomass degraders and shows great potential as industrial enzyme producers. Citation: Nybo JL, Vesth TC, Theobald S, Frisvad JC, Larsen TO, Kjaerboelling I, Rothschild-Mancinelli K, Lyhne EK, Barry K, Clum A, Yoshinaga Y, Ledsgaard L, Daum C, Lipzen A, Kuo A, Riley R, Mondo S, LaButti K, Haridas S, Pangalinan J, Salamov AA, Simmons BA, Magnuson JK, Chen J, Drula E, Henrissat B, Wiebenga A, Lubbers RJM, Müller A, dos Santos Gomes AC, Mäkelä MR, Stajich JE, Grigoriev IV, Mortensen UH, de Vries RP, Baker SE, Andersen MR (2025). Section-level genome sequencing and comparative genomics of Aspergillus sections Cavernicolus and Usti. Studies in Mycology 111: 101-114. doi: 10.3114/sim.2025.111.03.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Laboratory Measurements of n >= 3 K-shell Transition Energies of Sulfur Ions from F-like S viii to Li-like S xiv

Inner-shell transitions are ubiquitous in nonequilibrium collisionally ionized plasmas, such as supernova remnants, and in photoionized plasmas, such as outflows from active galactic nuclei and X-ray binaries. Inner-shell X-ray emission can help determine key parameters of these systems, such as ionization time, τ, and ionization parameter, ξ. Despite their importance, only theoretical inner-shell transition energies are available for many ions. To provide experimental benchmarks, we have measured the dominant n → 1 K-shell transitions of sulfur ions where n ≥ 3 from Li-like S xiv to F-like S viii using LLNL’s SuperEBIT electron beam ion trap and the NASA/GSFC EBIT Calorimeter Spectrometer (ECS). We identify over 30 spectral features and measure their energies with uncertainties in the ∼0.1–1 eV range. We compare these results to Flexible Atomic Code (FAC) and multireference Møller–Plesset (MR-MP) calculations and find differences between theory and experiment of ∼1 eV for FAC and <0.5 eV for most MR-MP calculations. We also compare these results to two widely used atomic databases, AtomDB and CHIANTI, and find discrepancies as high as 7 eV. Furthermore, many transitions are missing from these databases despite being prominent in our data.

Atomic data benchmarking↗
DOE OSTI2025

Gamma Rays as a Signature of r -process Producing Supernovae: Remnants and Future Galactic Explosions

We consider the question of whether core-collapse supernovae (CCSNe) can produce rapid neutron capture process (r-process) elements and how future MeV gamma-ray observations could address this. Rare types of CCSNe characterized by substantial magnetic fields and rotation, known as magnetorotational supernovae (MR-SNe), are theoretically predicted to produce these elements, although direct observational evidence is lacking. We suggest that this critical question be addressed through the study of some of the 11 CCSN remnants located within 10 kpc, as well as through the detection of gamma-ray emission from a future Galactic supernova. We use a two-dimensional MR-SN model to estimate the expected gamma flux stemming from nuclear decays in the range of a few tens of keV to a few MeV. Our results indicate that an observation of 126 Sn ( 126 Sb) in a remnant stands out as a signature of an r-process-producing supernova. Since the neutron-rich conditions that lead to the production of the r-process could also enhance the production of 60 Fe, the detection of substantial 60 Fe ( 60 Co) would be indicative of favorable conditions for the r-process. In the case of a future supernova explosion, when the evolution of the spectrum is studied over 10 days to a few years, a rich picture emerges. At various epochs, the second peak r-process isotopes such as 125 Sn, 131 I, 132 Te, 132 I, and 140 La produce signals that are not obscured by the gamma emission from explosive burning products and electron–positron annihilation. The weak r-process isotopes 95 Nb, 103 Ru, and 106 Rh also have periods of prominence.

Gamma-rays↗
DOE OSTI2026

Modeling CO 2 flow through faulted/fractured reservoirs using tEDFM in corner-point grids

The interest in underground CO 2 storage has increased significantly over the last decade because of the rising concern about global warming due to the growing levels of greenhouse gases in the atmosphere. Considering that CO 2 accounts for 80% of these greenhouse gases, carbon capture, utilization, and storage (CCUS) is regarded as one of the most direct approaches to achieving the net zero carbon target. Although CO 2 storage in deep saline aquifers and depleted gas reservoirs has been studied extensively, most studies use commercial simulators that model faults/fractures by simply modifying the transmissibility in the direction perpendicular to the fault surfaces. Here, this work shows that this simplistic approach ignores the accelerated flow in the directions parallel to the fault plane, leading to significantly higher leakage along the fault surface. To accurately model the flow of CO 2 in faulted reservoirs, we present the first transient embedded discrete fracture model for corner-point grids (tEDFM-CPG). By comparing the results of the tEDFM-CPG to high-resolution reference solutions, we show that this approach is accurate and efficient at predicting CO 2 flow in faulted/fractured reservoirs. Finally, this work presents the use of mixed reality (MR) to efficiently observe CO 2 gas migration in the interior of these corner-point grid systems.

25 ENERGY STORAGE↗
DOE OSTI2026

One-Step Relativistic Driven Similarity Renormalization Group Multireference Perturbation Theory

We present an efficient implementation of a one-step relativistic second-order multireference perturbation theory based on the multireference driven similarity renormalization group (MR-DSRG) using the exact two-component (X2C) Hamiltonian, which we denote X2C-DSRG-MRPT2. We show that the X2C-DSRG-MRPT2 method can accurately capture spin–orbit coupling (SOC) effects in the electronic structure of strongly correlated systems containing elements across the periodic table. We further demonstrate that the X2C-DSRG-MRPT2 method, through its variational treatment of SOC effects, can yield spin–orbit splittings with mean absolute percentage errors consistently below 7% with respect to experimental values for systems containing up to sixth row elements. With its modest computational scaling (fourth power in system size for the perturbative step) and high accuracy, X2C-DSRG-MRPT2 provides a promising avenue for the routine treatment of relativistic effects in strongly correlated molecular systems.

Hamiltonians↗
DOE OSTI2025

Coupling flux balance analysis with reactive transport modeling through machine learning for rapid and stable simulation of microbial metabolic switching

Integrating genome-scale metabolic networks with reactive transport models (RTMs) provides a detailed description of the dynamic changes in microbial growth and metabolism. Despite promising demonstrations in the past, computational inefficiency has been pointed out as a critical issue to overcome because it requires repeated application of linear programming (LP) to obtain flux balance analysis (FBA) solutions in every time step and spatial grid. To address this challenge, we propose a new simulation method where we train and validate artificial neural networks (ANNs) using randomly sampled FBA solutions and incorporate the resulting surrogate FBA model (represented as algebraic equations) into RTMs as source/sink terms. We demonstrate the efficiency of our method via a case study of Shewanella oneidensis MR-1. During aerobic growth on lactate, S. oneidensis produces metabolic byproducts (such as pyruvate and acetate), which are subsequently consumed as alternative carbon sources when the preferred nutrients are depleted. To effectively simulate these complex dynamics, we used a cybernetic approach that models metabolic switches as the outcome of dynamic competition among multiple growth options. In both zero-dimensional batch and one-dimensional column configurations, the ANN-based surrogate models achieved substantial reduction of computational time by several orders of magnitude compared to the original LP-based FBA models. Moreover, the ANN models produced robust solutions without any special measures to prevent numerical instability. These developments significantly promote our ability to utilize genome-scale networks in complex, multi-physics, and multi-dimensional ecosystem modeling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Colossal magnetoresistance from spin-polarized polarons in an Ising system

Recent experiments suggest a new paradigm toward novel colossal magnetoresistance (CMR) in a family of materials EuM 2 X 2 (M = Cd, In, Zn; X = P, As), distinct from the traditional avenues involving Kondo–Ruderman–Kittel–Kasuya–Yosida crossovers, magnetic phase transitions with structural distortions, or topological phase transitions. Here, we use angle-resolved photoemission spectroscopy and density functional theory calculations to explore their origin, particularly focusing on EuCd 2 P 2 . While the low-energy spectral weight royally tracks that of the resistivity anomaly near the temperature with maximum magnetoresistance ( T MR ) as expected from transport-spectroscopy correspondence, the spectra are completely incoherent and strongly suppressed with no hint of a Landau quasiparticle. Using systematic material and temperature dependence investigation complemented by theory, we attribute this nonquasiparticle caricature to the strong presence of entangled magnetic and lattice interactions, a characteristic enabled by the p - f mixing. Given the known presence of ferromagnetic clusters, this naturally points to the origin of CMR being the scattering of spin-polarized polarons at the boundaries of ferromagnetic clusters. These results are not only illuminating to investigate the strong correlations and topology in EuCd 2 X 2 family, but, in a broader view, exemplify how multiple cooperative interactions can give rise to extraordinary behaviors in condensed matter systems.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Host galaxies of ultra-strong Mg ii absorbers at z ∼ 0.7

ABSTRACT We report spectroscopic identification of the host galaxies of 18 ultra-strong Mg ii systems (USMg ii) at 0.6 ≤ z ≤ 0.8. We created the largest sample by merging these with 20 host galaxies from our previous survey within 0.4 ≤ z ≤ 0.6. Using this sample, we confirm that the measured impact parameters ($\rm 6.3\leqslant D[kpc] \leqslant 120$ with a median of 19 kpc) are much larger than expected, and the USMg ii host galaxies do not follow the canonical $\rm {\it W}_{2796}-{\it D}$ anticorrelation. We show that the presence and significance of this anticorrelation may depend on the sample selection. The $\rm {\it W}_{2796}-{\it D}$ anticorrelation seen for the general Mg ii absorbers show a mild evolution at low $\rm W_{2796}$ end over the redshift range 0.4 ≤ z ≤ 1.5 with an increase of the impact parameters. Compared to the host galaxies of normal Mg ii absorbers, USMg ii host galaxies are brighter and more massive for a given impact parameter. While the USMg ii systems preferentially pick star-forming galaxies, they exhibit slightly lower ongoing star-forming rates compared to main sequence galaxies with the same stellar mass, suggesting a transition from star-forming to quiescent states. For a limiting magnitude of mr < 23.6, at least 29 per cent of the USMg ii host galaxies are isolated, and the width of the Mg ii absorption in these cases may originate from gas flows (infall/outflow) in isolated haloes of massive star forming but not starbursting galaxies. We associate more than one galaxy with the absorber in $\ge 21~{{\ \rm per\ cent}}$ cases, where interactions may cause wide velocity spread.

Astronomy & Astrophysics↗
DOE OSTI2026

Downscaled Daily 1 km Climate Data (NEX-GDDP-CMIP6) for Southeast Texas. Full ensemble of downscaled CMIP6 climate projections at 1 km daily resolution.

For the SETx-UIFL, the daily NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) dataset climate projections were downscaled from approximately 27 km to 1 km. The SETx dataset provides very high-resolution climate data for the historical period (1950–2014) and future scenarios derived from CMIP6 global models under the four Tier 1 Shared Socioeconomic Pathways (SSPs 1.26, 2.45, 3.70, and 5.85), developed for the IPCC Sixth Assessment Report. A subset of ten NEX-GDDP-CMIP6 models was selected to represent a balance of model families, climate sensitivities, and availability across scenarios, ensuring a diverse and reliable ensemble for regional analysis. Selected models: BCC-CSM2-MR, CESM2, CMCC-ESM2, CNRM-ESM2-1, EC-Earth3, FGOALS-g3, GFDL-CM4, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. Daily variables downscaled include tasmax, tasmin, tas, pr, hurs, huss, rsds, rlds, and sfcWind.

Persad, Geeta↗
DOE OSTI2025

Modeling Co2 Flow Through Faulted/Fractured Reservoirs Using Tedfm in Corner-Point Grids

Interest in underground CO2 storage has increased significantly over the last decade, driven by growing concern about global warming and rising levels of greenhouse gases in the atmosphere. Given that CO2 accounts for 80% of these greenhouse gases, carbon capture, utilization, and storage (CCUS) is considered one of the most direct approaches to achieving the net-zero carbon target. Although CO2 storage in deep saline aquifers and depleted gas reservoirs has been studied extensively, most studies use commercial simulators that model faults/fractures by simply modifying the transmissibility in the direction perpendicular to the fault surfaces. This work shows that this simplistic approach ignores the accelerated flow in the directions parallel to the fault plane, leading to significantly higher leakage along the fault surface. To accurately model CO2 flow in faulted reservoirs, we present the first transient embedded discrete-fracture model for corner-point grids (tEDFM-CPG). By comparing the tEDFM-CPG results with high-resolution reference solutions, we show that this approach is accurate and efficient at predicting CO2 flow in faulted/fractured reservoirs. In conclusion, this work presents the use of mixed reality (MR) to efficiently observe CO2 gas migration in the interior of these corner-point grid systems.

02 PETROLEUM↗