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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Meta-Generalized Gradient Approximation for the Cavity-Dependent Exchange-Correlation Interaction in Strongly Coupled Light–Matter Systems

Strong light–matter coupling in optical cavities enables the manipulation of chemical and physical properties without altering molecular composition. Theoretical modeling of such phenomena requires exchange-correlation (XC) functionals that account for both electron–electron and electron–photon (ep) interactions within quantum electrodynamical density functional theory (QEDFT). In this work, we develop a meta-generalized gradient approximation (meta-GGA) specifically targeting the cavity-dependent XC interaction in strongly coupled light–matter systems. This novel approximation is built upon a new semilocal polarizability approximation, which draws from the jellium-with-a-gap model, and can be extended to a “global hybrid” variant that goes beyond the isotropic model from previous approximations. The polarizability model yields significantly improved dispersion coefficients and benchmark calculations with the cavity-dependent XC functional demonstrate improved agreement with QED Hartree–Fock (QED-HF) reference energies. Application to the regioselectivity of brominated nitrobenzene intermediates reveals the functional’s capacity to capture cavity-induced energetic shifts. In conclusion, our results advance the Jacob’s ladder of functionals for QEDFT and provide a practical tool for modeling polaritonic chemistry.

Approximation↗
DOE OSTI2026

Controlled Acidity Gradients Enable CO 2 Reduction to Formic Acid (Not Formate) by Molecular Electrocatalysts

Neutral or basic conditions are commonly required for the selective electrochemical reduction of CO 2 , leading to the accumulation of carbonate salts and the generation of formate rather than formic acid. A generalizable strategy for obtaining formic acid (not formate) in the electroreduction of CO 2 with molecular catalysts is introduced, based on controlling acidity gradients using a dual-electrolyte cell with a proton-exchange membrane. This approach uses anodic water oxidation as the source of protons and electrons for CO 2 reduction to formic acid, while mitigating H 2 evolution near the cathode and avoiding carbonate formation. Mechanistic studies, including systems modeling, provide insight into the origin of the formic acid selectivity and guide the broader implementation of this strategy in molecular electrocatalysis for CO 2 utilization.

Alcohols↗
DOE OSTI2026

Capturing Secondary Kinetic Instabilities in Three‐Dimensional Dayside Reconnection Using an Improved Gradient‐Based Closure

Magnetic reconnection is a highly dynamic process that excites a wide variety of kinetic waves and instabilities. Transverse current sheet instabilities such as the lower-hybrid drift and secondary drift-kink instabilities in particular have been shown by kinetic simulations to modify the reconnection and introduce significant turbulence and mixing to the reconnection layer. Past studies using the ten-moment fluid model to capture important kinetic physics such as the electron inertia and full representation of the pressure tensor proved advantageous to a two-fluid representation of reconnection, but the model struggled when using a local relaxation closure for the heat flux to replicate the current sheet instabilities and subsequent mixing seen in kinetic simulations. This work uses the Gkeyll software framework to perform simulations of asymmetric reconnection based on the 16 October 2015 MMS crossing of a diffusion region, the Burch event. An improved gradient-based heat flux closure is implemented, showing significant improvement in secondary kinetic instabilities that grow in the current sheet. These instabilities generate turbulence which leads to growth of secondary magnetic islands and flux ropes.

Bradshaw, K. [Princeton University, NJ (United Sta↗
DOE OSTI2025

Population ecology and biogeochemical implications of ssDNA and dsDNA viruses along a permafrost thaw gradient

Anthropogenic-driven climate change is accelerating permafrost thaw, threatening to release vast carbon stores through increased microbial activity. While microbial roles are increasingly studied, the contributions of viruses remain largely unexplored, in part due to soil-associated technical challenges that have hindered their detection and characterization. Here, we applied an optimized virion enrichment workflow along a permafrost thaw gradient, identifying 9,963 viral populations (vOTUs), including single- and double-stranded DNA viruses, with 99.9% novelty compared to other soils. Hosts were predicted for 38% of vOTUs, spanning nine archaeal, and 36 bacterial phyla, 22% of which were linked to metagenome-assembled genomes, including key carbon-cycling taxa. Genomic analyses revealed 811 putative auxiliary metabolic genes (AMGs) from 658 vOTUs, nearly half involved in carbon processing. These included 59 glycoside hydrolases (GH) across nine GH families, 45 for monosaccharide degradation, and seven involved in short-chain fatty acid and C1 metabolism, linking viruses to both early and late stages of carbon turnover. Additionally, six vOTUs carried racD, which may stabilize microbial necromass and promote long-term carbon storage. Viral and AMG functional diversity increased with thaw stage, indicating that viruses might participate in a broadening range of microbial metabolic processes as permafrost thaws. These findings expand our understanding of virus contributions in microbial carbon processing and suggest their important role in deciphering soil carbon fate under changing climate conditions.

Biological and medical sciences↗
DOE OSTI2025

Machine learning of 27Al NMR electric field gradient tensors for crystalline structures from DFT

NMR crystallography has emerged as a promising technique for the determination and refinement of atomic coordinates in crystal structures. The crystal structure of compounds containing quadrupolar nuclei, such as 27Al, can be improved by directly comparing solid-state NMR measurements to DFT computations of the electric field gradient (EFG) tensor. The non-negligible computational cost of these first-principles calculations limits the applicability of this method to all but the most well-defined structures. We developed a fast, low-cost machine learning model to predict EFG parameters based on local structural motifs and elemental parameters. We computed 8081 EFG tensors from 1681 27Al crystalline solids using DFT and benchmarked them against 105 experimentally measured 27Al sites. Surprisingly, simple local geometric features dominate the predictive performance of the resulting random-forest model, yielding an R2 value of 0.98 and an RMSE of 0.61 MHz for CQ, the quadrupolar coupling constant. This model accuracy should enable pre-refining future structural assignments before finally validating with first-principles calculations. Such a catalogue of 27Al NMR tensors can serve as a tool for researchers assigning complex NMR spectra influenced by the nuclear electric quadrupole interaction.

Sun, He↗
DOE OSTI2024

Iron transformation mediates phosphate retention across a permafrost thaw gradient

Phosphorus limits primary productivity in many (Sub-)Arctic ecosystems and may constrain biological carbon sequestration. Iron (III) oxides strongly bind phosphate in soils but can dissolve under flooded, reducing conditions induced by permafrost thaw and ground collapse. The ability for iron to regulate phosphate storage and solubility in thawing permafrost landscapes remains unclear. Here, iron-rich sediments containing iron oxides and organic-bound iron were incubated with or without added phosphate in soils along a permafrost thaw gradient to evaluate how iron-phosphate associations respond to thaw-induced redox shifts. Iron oxides partially dissolved and released sorbed phosphate when incubated in soils underlain by degraded permafrost. Iron complexed by organic matter remained stable but provided no phosphate binding capacity. Phosphate addition enhanced iron oxide dissolution and phosphorus concentrations in associated microbial biomass. Our study demonstrates that the capacity for iron oxides to immobilize and retain phosphate in permafrost peatlands decreases with permafrost thaw.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Gradient-based optimization of complex nanoparticle heterostructures enabled by deep learning on heterogeneous graphs

Applications of deep learning (DL) to design nanomaterials are hampered by a lack of suitable data representations and training data. Here, in this study, we report efforts to overcome these limitations and leverage DL to optimize the nonlinear optical properties of core–shell upconverting nanoparticles (UCNPs). UCNPs, which have applications in fields such as biosensing, super-resolution microscopy and three-dimensional printing, can emit visible and ultraviolet light from near-infrared excitations. We report a large-scale dataset of UCNP emission spectra based on accurate but expensive kinetic Monte Carlo simulations (N > 6,000) and use these data to train a heterogeneous graph neural network using a physically motivated representation of UCNP nanostructure. Applying gradient-based optimization on the trained graph neural network, we identify structures with 6.5× higher predicted emission under 800-nm illumination than any UCNP in our training set. Our work reveals design principles for UCNP heterostructures and presents a roadmap for DL-based inverse design of nanomaterials.

Sivonxay, Eric [Lawrence Berkeley National Laborat↗
DOE OSTI2024

Directly driven magnetized fast-ignition targets with steep density gradients for inertial fusion energy

The development of advanced targets capable of achieving ignition with improved energy gain at lower driver energies is one of four key technical challenges to be solved in order to realize economical inertial fusion energy. We report the minimum energy necessary for a small hemispherical mass of fast-ignited high-density deuterium–tritium fuel to explosively ignite a significantly larger hemispherical mass of assembled cold fuel with much lower mass density, both with and without a flux-compressed magnetic field connecting the two regions. With the magnetic field, the burn rate improves, and lower energy states become more effective. The imploded fuel reservoir available in the lower-density, larger-mass region of the steep density gradient determines whether the fusion yield is several hundred MJ or up to a few GJ. We report a case wherein the cold reservoir ignited and produced high gain with the assistance of only ~700 kJ of hotspot yield, an amount that has already been demonstrated as feasible in laboratory experiments using indirect-drive targets.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Limit cycle oscillations in the zonal-flow-catalyzed interactions of ion-temperature-gradient turbulence

Limit-cycle oscillations are studied for ion temperature gradient turbulence, which, in the absence of large diamagnetic (mean) shear flows, saturates through energy transfer from unstable modes to large-scale stable modes via zonal-flow intermediary modes. Oscillations of zonal flow and turbulence levels are strongly constrained by the reactive, largely non-dissipative character of the zonal flows. Since existing predator–prey models for observed oscillations in experiments do not include energy transfer through zonal flows to stable modes, low-order fluid models with this physics are constructed and investigated. A simple three-wave truncation produces low-amplitude zonal flows that slowly oscillate around a zero mean, with turbulence oscillations between coupled wavenumbers that exceed linear frequencies by orders of magnitude. This inconsistency with experimental observations is caused by the weak non-linear drive of zonal flows in three-wave systems and the lack of multiple-wavenumber turbulent interactions. A more comprehensive model that preserves multiple wavenumber interactions within the context of conservative zonal-flow-mediated energy transfer to stable modes accurately reflects observed dynamics when the phase between stable and unstable modes is occasionally randomized.

Li, P. -Y. (ORCID:0000000295254171)↗
DOE OSTI2025

Electron temperature gradient instability and transport analysis in NSTX and NSTX-U plasmas

Extensive linear and nonlinear simulations to study electron temperature gradient (ETG) stability and thermal transport in National Spherical Torus Experiment (NSTX) and NSTX-U plasmas were performed using the fully electromagnetic gyrokinetic code CGYRO. Linear simulations were performed to determine ETG thresholds in different discharges, showing that ETG modes in spherical tokamaks can present different scalings compared to conventional aspect-ratio tokamaks. Nonlinear gyrokinetic simulations were conducted for selected cases to calculate electron thermal transport and compare to experimental values. Results are also compared with those of ETG modes in the multi-mode model and the Trapped-Gyro-Landau-Fluid reduced model codes, to better understand their applicability in spherical tokamaks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Evaluating the gradients of localized diabatic state energies and couplings at minimum cost

We calculate the gradients of Boys diabatic state energies and couplings when the electronic vector space is generated by configuration interaction singles. Our approach follows the Lagrangian approach of Paz and Glover (rather than direct differentiation of the adiabatic-to-diabatic approaches that have been published previously). The result is that we achieve a dramatic increase in savings over previous approaches, and the present approach should be immediately useful to scientists focused on electronic relaxation, especially chemists studying electron transfer who wish to go beyond the Condon approximation. Here, a future extension to time-dependent density functional theory in the Tamm–Dancoff approximation is clear.

Chemical physics↗
DOE OSTI2026

Suppression of ion temperature gradient modes by Alfvén activity above a drive threshold in DIII-D

A recent study demonstrates that the suppression of ion temperature gradient (ITG) modes can occur during the nonlinear evolution of toroidicity-induced Alfvén eigenmodes (TAEs) in the DIII-D tokamak [Du et al., Phys. Rev. Lett. 135, 265101 (2025)]. In that work, ITG was suppressed by the formation of a narrow, TAE-induced shear flow layer, whose shearing rate exceeded the ITG decorrelation rate. Here, the shear flow arises from an imbalance between Reynolds and Maxwell stress forces, as the TAE departs from the conventional shear Alfvén wave polarization. This follow-up paper systematically identifies the plasma conditions required for robust ITG suppression through a series of comparative experiments. The results show that TAEs routinely suppress ITG turbulence in plasmas with higher local safety factor (q), elevated fast-ion beta, and larger populations of fast ions on passing orbits, i.e., conditions consistent with the substantial TAE drive. Database analysis further reveals the existence of a threshold in TAE drive for ITG suppression. That is, once the drive exceeds a critical value within a favorable q window, the system undergoes the nonlinear bifurcation process, characterized by a sharp increase in TAE saturated amplitudes at a nearly fixed fast ion drive, suppression of ITG turbulence, and the formation of an internal thermal transport barrier at the localized radii.

Du, X. D. [General Atomics, San Diego, CA (United ↗
DOE OSTI2024

Sequential Kalman tuning of the t -preconditioned Crank-Nicolson algorithm: efficient, adaptive and gradient-free inference for Bayesian inverse problems

Ensemble Kalman Inversion (EKI) has been proposed as an efficient method for the approximate solution of Bayesian inverse problems with expensive forward models. However, when applied to the Bayesian inverse problem EKI is only exact in the regime of Gaussian target measures and linear forward models. Here, in this work we propose embedding EKI and Flow Annealed Kalman Inversion, its normalizing flow (NF) preconditioned variant, within a Bayesian annealing scheme as part of an adaptive implementation of the t-preconditioned Crank-Nicolson (tpCN) sampler. The tpCN sampler differs from standard pCN in that its proposal is reversible with respect to the multivariate t-distribution. The more flexible tail behaviour allows for better adaptation to sampling from non-Gaussian targets. Within our Sequential Kalman Tuning (SKT) adaptation scheme, EKI is used to initialize and precondition the tpCN sampler for each annealed target. The subsequent tpCN iterations ensure particles are correctly distributed according to each annealed target, avoiding the accumulation of errors that would otherwise impact EKI. We demonstrate the performance of SKT for tpCN on three challenging numerical benchmarks, showing significant improvements in the rate of convergence compared to adaptation within standard SMC with importance weighted resampling at each temperature level, and compared to similar adaptive implementations of standard pCN. The SKT scheme applied to tpCN offers an efficient, practical solution for solving the Bayesian inverse problem when gradients of the forward model are not available. Code implementing the SKT schemes for tpCN is available at https://github.com/RichardGrumitt/KalmanMC.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Global gyro-kinetic ion temperature gradient and trapped electron mode turbulence modelling in X -point geometry in negative and positive triangularity

Comparative modelling of ion temperature gradient (ITG)/trapped electron mode (TEM) turbulence in negative (NT) and positive (PT) triangularity plasma shapes was done using the nonlinear global full-f gyrokinetic particle code JOREK-GK in the realistic X-point tokamak geometry including the Scrape Off Layer (SOL) for TCV and DIII-D parameters. A comparison of JOREK-GK code with the gyrokinetic codes GS2 and GENE was done using NT/PT triangularity TCV L-modes parameters showing good agreement between codes in linear growth rates and clear beneficial effect of NT as compared to PT. Global non-linear modelling of the ITG/TEM saturated turbulence for realistic DIII-D NT pulses was done and compared with numerically constructed PT equilibrium with the same plasma profiles. Existence of longer correlation length of density fluctuations in PT compared to NT was demonstrated. Stronger and more sheared zonal flows are generated via Reynolds stress in NT compared to PT. These factors are stabilizing for TEM/ITG turbulence in NT and lead to smaller heat fluxes and heat conductivities in NT compared to PT configuration. Weak dependence of plasma confinement on collisionality and plasma rotation was found in modelling of DIII-D NT shots similar to the experiment. The confinement scaling with normalized ion gyro-radius ρ* was estimated both for NT and PT. Bohm-like scaling was obtained in both configurations, however with better confinement for NT compared to PT which could be favourable factor for reactor size machines.

ITG↗
DOE OSTI2025

Effects of midplane density gradient in the propagation of high-harmonic fast waves considering high temperature anisotropy in the scrape-off layer of NSTX-U

High-Harmonic Fast Wave (HHFW) heating experiments in NSTX have shown that up to 60% of the injected power can be lost in the Scrape-Off Layer (SOL) when the density is above the fast wave cutoff density in front of the antenna, for which the fast wave is able to propagate into the plasma. This work models HHFW propagation in the SOL plasmas of NSTX-U using a 2D divertor SOL profile derived from the pressure assumption and the finite element temperature solution, which accounts for the high anisotropy of heat conduction in a 2D axisymmetric geometry. In this work, the two-dimensional axisymmetric SOL temperature profile is first evaluated by solving the steady-state non-linear heat conduction equation, in which thermal conductivity depends on temperature, using a finite element approach in the Petra-M workbench. A 2D density profile is then obtained from a prescribed density profile at the outer midplane assuming pressure is uniform along a flux tube in the SOL. This approach results in density and temperature profiles in which the strong asymmetric nature of heat conduction between the parallel and perpendicular background magnetic field is successfully captured. Furthermore, this work focuses on investigating the effect of the SOL plasma density profile on parasitic HHFW propagation in the SOL. The simulation results show that the radial gradient of the density profile affects the wavefield propagation in the SOL. As the density profile broadens, the wavefield intensity and its poloidal extent are reduced in the SOL, and the core coupling increases. Similarly, as shown by collisional power deposition, a proxy for power absorption, the fraction of power deposited in the SOL decreases with a broadening profile.

HHFW↗
DOE OSTI2024

Photon classification with Gradient Boosted Trees at CLAS12

Dihadron semi-inclusive deep inelastic scattering (SIDIS) of 10.6 GeV longitudinally polarized electrons off the proton has been measured using the CLAS12 detector at Jefferson Lab. Two separate channels, π + π 0 and π - π 0 , were analyzed, requiring the reconstruction of diphoton pairs. Here, in this analysis, we addressed the problem of false neutral particles being reconstructed by CLAS12's event builder, polluting the otherwise physical combinatorial background underneath the π 0 peak. A photon classifier using a Gradient Boosted Trees (GBTs) architecture was trained with Monte Carlo simulations to reduce the amount of background π 0 's. We show that the nearest-neighbor features learned by the model lead to a substantial increase in signal vs. background discrimination compared to previous CLAS12 π^0 analyses. The machine learning approach recovers several times more dihadron statistics for the dataset.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Harnessing the power of gradient-based simulations for multi-objective optimization in particle accelerators

Abstract Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-objective optimization (MOO) is particularly challenging due to trade-offs between the objectives. Evolutionary algorithms, such as genetic algorithms (GAs), have been leveraged for many optimization problems, however, they do not apply to complex control problems by design. This paper demonstrates the power of differentiability for solving MOO problems in particle accelerators using a deep differentiable reinforcement learning (DDRL) algorithm. We compare the DDRL algorithm with model-free reinforcement learning (MFRL), GA, and Bayesian optimization (BO) for simultaneous optimization of heat load and trip rates in the continuous electron beam accelerator facility. The underlying problem enforces strict constraints on both individual states and actions as well as cumulative (global) constraints on energy requirements of the beam. Using historical accelerator data, we develop a physics-based surrogate model which is differentiable and allows for back-propagation of gradients. The results are evaluated in the form of a Pareto-front with two objectives. We show that the DDRL outperforms MFRL, BO, and GA on high dimensional problems.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Implementation of Stochastic Gradient Descent in an Automated Glow Peak Identification Software for Multiple Thermoluminescent Dosimeter Types

A glow-curve analysis code was previously developed in C++ to analyze thermoluminescent dosimeter glow curves using automated peak detection while applying a first-order kinetics model. A newer version of this code was implemented to improve the automated peak detection and curve fitting models. The Stochastic Gradient Descent Algorithm was introduced to replace the prior approach of taking first and second-order derivatives for peak detection. Additionally, early stopping mechanisms were invoked to improve the previously used Levenberg-Marquardt Algorithm employed for curve fitting. The two software versions were compared through glow curve analysis of different thermoluminescent dosimeter materials and calculation of the corresponding figures of merit. Altogether improvements were shown, namely an increase in the number of peaks detected and a reduction of the mean figure of merit by approximately 46%.

137Cs↗