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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Defect Complexes in CrSBr Revealed Through Electron Microscopy and Deep Learning

Atomic defects underpin the properties of van der Waals materials, and their understanding is essential for advancing quantum and energy technologies. Scanning transmission electron microscopy is a powerful tool for defect identification in atomically thin materials, and extending it to multilayer and beam-sensitive materials would accelerate their exploration. Here, we establish a comprehensive defect library in a bilayer of the magnetic quasi-1D semiconductor CrSBr by combining atomic-resolution imaging, deep learning, and calculations. We apply a custom-developed machine learning work flow to detect, classify, and average point vacancy defects. This classification enables us to uncover several distinct Cr interstitial defect complexes, combined Cr and Br vacancy defect complexes, and lines of vacancy defects that extend over many unit cells. We show that their occurrence is in agreement with our computed structures and binding energy densities, reflecting the intriguing layer interlocked crystal structure of CrSBr. Our ab initio calculations show that the interstitial defect complexes give rise to highly localized electronic states. These states are of particular interest due to the reduced electronic dimensionality and magnetic properties of CrSBr and are, furthermore, predicted to be optically active. Our results broaden the scope of defect studies in challenging materials and reveal new defect types in bilayer CrSBr that can be extrapolated to the bulk and to over 20 materials belonging to the same FeOCl structural family.

deep learning↗
DOE OSTI2024

Scalable Circuits for Preparing Ground States on Digital Quantum Computers: The Schwinger Model Vacuum on 100 Qubits

The vacuum of the lattice Schwinger model is prepared on up to 100 qubits of IBM’s Eagle-processor quantum computers. A new algorithm to prepare the ground state of a gapped translationally invariant system on a quantum computer is presented, which we call “scalable circuits ADAPT-VQE” (SC-ADAPT-VQE). This algorithm uses the exponential decay of correlations between distant regions of the ground state, together with ADAPT-VQE, to construct quantum circuits for state preparation that can be scaled to arbitrarily large systems. These scalable circuits can be determined with use of classical computers, avoiding the challenging task of optimizing parameterized circuits on a quantum computer. SC-ADAPT-VQE is applied to the Schwinger model, and is shown to be systematically improvable, with an accuracy that converges exponentially with circuit depth. Both the structure of the circuits and the deviations of prepared wave functions are found to become independent of the number of spatial sites, L . This allows a controlled extrapolation of the circuits, determined with use of small or modest-sized systems, to arbitrarily large L . The circuits for the Schwinger model are determined on lattices up to L = 14 (28 qubits) with the Qiskit classical simulator, and are subsequently scaled up to prepare the L = 50 (100 qubits) vacuum on IBM’s 127-superconducting-qubit quantum computers ibm_brisbane and ibm_cusco. After introduction of an improved error-mitigation technique, which we call “operator decoherence renormalization”, the chiral condensate and charge-charge correlators obtained from the quantum computers are found to be in good agreement with classical matrix product state simulations. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Variational Simulation of the Lipkin-Meshkov-Glick Model on a Neutral Atom Quantum Computer

We simulate the Lipkin-Meshkov-Glick model using the variational-quantum-eigensolver algorithm on a neutral atom quantum computer. We test the ground-state energy of spin systems with up to 15 spins. Two different encoding schemes are used: an individual spin encoding where each spin is represented by one qubit, and an efficient Gray code encoding scheme that only requires a number of qubits that scales with the logarithm of the number of spins. This more efficient encoding, together with zero-noise extrapolation techniques, is shown to improve the fidelity of the simulated energies with respect to exact solutions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Quantifying uncertainty in physics-based predictions of rare-isotope production cross sections via Bayesian-inspired model averaging across nuclear mass tables

Accurate prediction of fragmentation cross sections is essential for rare-isotope beam production, planning new-isotope searches, and designing experiments to study the most exotic regions of the nuclear chart. However, existing reaction models and phenomenological cross-section parametrizations often exhibit significant deviations over broad regions of mass and charge. In this work, a Bayesian-inspired model-averaging framework is developed to combine abrasion-ablation (AA) calculations based on multiple nuclear mass tables into a single statistically weighted estimate. For the calibrated systems, the model weights are assigned empirically according to the relative quality of fit to measured cross sections, thereby reducing systematic model bias while preserving the underlying physics content of the AA description. The weights are constrained using proton-rich fragmentation data for the 78 Kr and 124 Xe projectiles. The resulting parameter trends are then propagated to the 92 Mo and 144 Sm systems through a controlled scaling procedure. In the present implementation, the excitation-energy prescription is fixed, while the averaging is performed across nuclear-mass inputs; the framework provides both weighted cross sections and associated uncertainty estimates. Applied to proton-rich fragmentation, the present approach provides a practical basis for interpolation and limited extrapolation in regions relevant to rare-isotope production. The resulting predictions are used to assess the production of very proton-rich nuclei, and candidate new isotopes are discussed.

Bayesian methods↗
DOE OSTI2025

Quantum physics of stars

Stars are slowly developing objects; the lifetimes of the different burning phases are determined by the strength of nuclear reactions, which in turn are defined by the quantum structure of the associated nuclei at the threshold and the respective reaction mechanisms. Stars, from the nuclear physics perspective, are cold environments where only a few of the key nuclear reactions have been measured at the actual stellar plasma temperatures. This is also the case for more dynamic astrophysical phenomena from the big bang to stellar explosions. Most of the nuclear reaction rates are therefore based on theoretical extrapolations. A number of discrepancies between these predictions and the associated stellar signatures have been observed, and many may be due to low-energy or near-threshold quantum effects. These effects need to be understood in order to reliably model nuclear reaction processes, not only for stars but also for low-temperature plasma environments such as controlled magnetic or inertial confinement fusion systems, which operate in similar temperature regimes. This review summarizes the various theoretical techniques presently used for deriving reaction rates and discusses possible quantum effects that may impact the reaction cross section near the reaction threshold. These resemble enhanced single-particle and cluster structures near threshold and associated interference effects. New experimental techniques such as deep-underground accelerators or the study of transfer reactions to mimic the quantum-mechanical transition strength, the so-called Trojan horse method, provide ways to directly or indirectly probe the reaction features that determine the reaction rates at stellar energies. Furthermore, this is demonstrated on a number of key nuclear reactions for different nucleosynthesis environments. Finally, current inconsistencies between experimental predictions and observations are discussed.

Models & methods for nuclear reactions↗
DOE OSTI2025

Lattice QCD constraints on the critical point from an improved precision equation of state

In this paper we employ lattice simulations to search for the critical point of QCD. We search for the onset of a first-order QCD transition on the phase diagram by following contours of constant entropy density from imaginary to real chemical potentials under conditions of strangeness neutrality. We scan the phase diagram and investigate whether these contours meet to determine the probability that the critical point is located in a certain region on the 𝑇−𝜇 𝐵 plane. To achieve this we introduce a new, continuum extrapolated equation of state at zero density with improved precision using lattices with 𝑁 𝜏 =8, 10, 12, 16 time slices, and supplement it with new data at imaginary chemical potential. The current precision allows us to exclude, at the 2⁢𝜎 level, the existence of a critical point at 𝜇 𝐵 <450 MeV.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Charge-trap analysis in a SENSEI skipper-CCD: Understanding low-energy backgrounds in rare-event searches

Skipper charge-coupled devices (CCDs) are ultralow-threshold detectors capable of detecting energy deposits in silicon at the electronvolt scale. Skipper CCDs are increasingly used in rare-event searches, including experiments such as SENSEI, DAMIC-M, Oscura, and CONNIE, where one of the major challenges is mitigating low-energy backgrounds. In this work, we present results on trap characterization in a silicon skipper-CCD produced in the same fabrication run as the SENSEI experiment at SNOLAB. Lattice defects contribute to backgrounds in rare-event searches through single-electron charge trapping. To investigate this, we use the charge-pumping technique at different temperatures to identify dipoles produced by traps in the CCD channel. We fully characterize a fraction of these traps and use this information to extrapolate their contribution to the single-electron background in SENSEI. We find that this subpopulation of traps does not contribute significantly, but more work is needed to assess the impact of the traps that cannot be characterized.

Brusco, Agustin↗
DOE OSTI2026

Isospin dependence of the nuclear EMC effect from a global QCD analysis

We perform a new global QCD analysis of unpolarized parton distribution functions (PDFs) in the nucleon from proton, deuteron, and A = 3 data, including recent measurements of He 3 / D and H 3 / D cross section ratios from the MARATHON experiment at Jefferson Lab. Simultaneously inferring the PDFs and nucleon off-shell corrections allows both to be determined consistently, without theoretical assumptions about the isospin dependence of nuclear effects. The analysis provides strong evidence for the need of nucleon off-shell corrections to describe the A = 3 data, with large isoscalar and a suggestion of nonzero isovector contributions in A ≤ 3 nuclei. We find that the extracted EMC ratios of nuclear to nucleon structure functions for A = 2 and 3 differ from those naively extrapolated from heavy nuclei down to low A .

Cocuzza, C. [William & Mary] (ORCID:00000003492292↗
DOE OSTI2025

Dark Matter Velocity Distributions for Direct Detection: Astrophysical Uncertainties Are Smaller Than They Appear

The sensitivity of direct detection experiments depends on the phase-space distribution of dark matter near the Sun, which can be modeled theoretically using cosmological hydrodynamical simulations of Milky Way–like galaxies. However, capturing the halo-to-halo variation in the local dark matter speeds—a necessary step for quantifying the astrophysical uncertainties that feed into experimental results—requires a sufficiently large sample of simulated galaxies, which has been a challenge. In this Letter, we quantify this variation with nearly 100 Milky Way–like galaxies from the tng50 simulation, the largest sample to date at this resolution. Moreover, we introduce a novel phase-space scaling procedure that endows every system with a reference frame that accurately reproduces the local standard-of-rest speed of our Galaxy, providing a principled way of extrapolating the simulation results to real-world data. The ensemble of predicted speed distributions is well characterized by the standard halo model, a Maxwell-Boltzmann distribution truncated at the escape speed, though the individual distributions can deviate from it, especially at high speeds. The dark matter–nucleon cross section limits placed by these speed distributions vary by ∼ 60% about the median. This places the 1⁢𝜎 astrophysical uncertainty at or below the level of the systematic uncertainty of current ton-scale detectors, even down to the energy threshold. The predicted uncertainty remains unchanged when subselecting on those TNG 50 galaxies with merger histories similar to the Milky Way. Tabulated speed distributions, as well as Maxwell-Boltzmann fits, are provided for use in computing direct detection bounds or projecting sensitivities.

Milky Way↗
DOE OSTI2026

Large-scale white noise and cosmology

The generation of white noise on large scales is a generic property of the dynamics of physical systems described by local nonlinear partial differential equations. Nonlinearities prevent the small-scale dynamics from being erased by smoothing. Unresolved small-scale dynamics act as an uncorrelated (white or Poissonian) noise (seemingly stochastic but actually deterministic) contribution to large-scale dynamics. This white noise exists even when the dynamics is very nearly linear. In cases where the power spectrum is sub-Poissonian on large scales, this noise will dominate on the largest scale power no matter the amplitude of the inhomogeneities. Such is the case in the standard model of cosmology, where the primordial density power spectrum is expected to have an almost Harrison-Zel’dovich, P [ k ] ∼ k , spectrum on a much broader range of scales than can be observed. Even though linear gravitational evolution dominates nonlinear corrections by a factor of ∼ 10 5 , the nonobservation of white noise on the Hubble scale precludes the extrapolation of this power law below the comoving 1 pc scale. More generally, observation or nonobservation of large-scale white noise provides a powerful probe of the Universe on very small scales in the early Universe. Gravitational radiation, phase transitions, vorticity, and running of the spectral index are all phenomena that can be probed with large-scale white noise. Large-scale white noise is a nonoptional feature of all cosmological models but one which has not heretofore been appreciated.

Barenboim, Gabriela [Valencia U., IFIC; Valencia U↗
DOE OSTI2026

Carbon-nitrogen complex as an alternative to the 𝑇 center in Si

A first-principles study of a carbon-nitrogen (CN) impurity complex in silicon is presented as an isoelectronic alternative to the T center [(CCH) Si ]. The latter has been pursued for applications in quantum information science, yet its sensitivity to the presence of hydrogen is still problematic. Our proposed complex has no hydrogen, thereby eliminating this issue. First, we show that the CN complex is stable against decomposition into substitutional and interstitial defects. Next, we show that due to being isoelectronic to the T center, the CN complex has a similar electronic structure, and therefore could be used in similar applications. We assess several low-energy configurations of the CN complex, finding (CN) Si to be stable and have the largest Debye-Waller factor. We predict a zero-phonon line (ZPL) of 828 meV (in the telecom S-band) and a radiative lifetime of 4.2 μs, comparable to the T center. Due to the presence of a bound exciton, choice of the exchange-correlation functional and also supercell-size scaling of the ZPL and transition dipole moment require special scrutiny; we rigorously justify our extrapolation schemes that allow computing values in the dilute limit.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Resonant neutron scattering lengths

Unlike most of the periodic table, many rare-earth elements display considerable resonant scattering for thermal neutrons. Although this property is accompanied by strong neutron absorption, modern high-intensity neutron sources make diffraction experiments possible with these elements. Computation of scattering intensities is accomplished by fitting the variation in resonant scattering lengths ( b 0 , b ′ and b ′′) to a semi-empirical Breit–Wigner formalism, which can be evaluated over the range of neutron energies useful for diffraction, typically E = 10–600 meV; λ = 0.4–2.8 Å (with good extrapolation to longer wavelengths).

Von Dreele, Robert B. (ORCID:0000000253390634)↗
DOE OSTI2026

Analytical desmearing of Bonse–Hart ultra-small-angle neutron scattering data via truncated Abel inversion

A non-iterative analytical framework based on the truncated Abel inversion is developed for desmearing Bonse–Hart ultra-small-angle neutron scattering (USANS) data. The method directly inverts the slit-averaged intensity without empirical extrapolation or iterative regularization, establishing a closed-form relationship between the measured and intrinsic scattering profiles. Numerical benchmarks on representative models, including a rigid-line form factor, a Lorentzian function and a fractal structural model, demonstrate quantitative recovery of the ground-truth intensity across the full Q range. Application to a deuterated polystyrene/poly(2-vinylpyridine) blend further confirms that the approach yields smooth continuous profiles consistent with companion small-angle neutron scattering data. The truncated Abel inversion thus provides a stable, model-independent and physically transparent route for accurate desmearing of Bonse–Hart USANS measurements.

Huang, Guan-Rong [National Tsing Hua University, T↗
DOE OSTI2025

Adaptive Computing for Scale-Up Problems

Adaptive Computing is an application-agnostic outer loop framework to strategically deploy simulations and experiments to guide decision making for scale-up analysis. Resources are allocated over successive batches, which makes the allocation adaptive to some objective such as optimization or model training. The framework enables the characterization and management of uncertainties associated with predictive models of complex systems when scale-up questions lead to significant model extrapolation. A key advancement of this framework is its integration of multi-fidelity surrogate modeling, uncertainty management, and automated orchestration of various computing and experimentation resources into a single integrated software package. This enables efficient multi-fidelity modeling across multiple computing resources by incorporating real-world constraints such as relative queue times and throughput on individual machines into the multi-fidelity sampling decision. We discuss applications of this framework to problems in the renewable energy space, including biofuels production, material synthesis, perovskite crystal growth, and building electrical loads.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Data-Driven Analysis of Multipactor Dynamics via Dynamic Mode Decomposition

Multipactor effect is a performance-limiting kinetic plasma effect that can occur in high-power microwave and radio frequency (RF) devices. Multipactor effect is of special concern in vacuum or near-vacuum conditions such as those in particle accelerators and spaceborne devices. In this work, we present a data-driven reduced-order model (ROM) based on dynamic mode decomposition (DMD) for modeling of multipactor effects. We study multipactor effects and the resulting nonlinear harmonic generation by processing high-fidelity data generated from electromagnetic particle-in-cell (EMPIC) simulations using the DMD algorithm. We also investigate time-delay embedding extensions of DMD with improved generalizability and accuracy for modeling the electron plasma current density behavior. Here, the results show that DMD provides valuable insights into multipactor phenomena by extracting relevant modal spatiotemporal patterns and frequencies. In addition, DMD offers the potential to time extrapolate EMPIC simulations at a minimal cost, thereby reducing overall simulation time.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Storms Are an Important Driver of Change in Tropical Forests

Tropical forest dynamics and composition have changed over recent decades, but the proximate drivers of these changes remain unclear. Investigations into these trends have focused on increasing drought stress, CO 2 , temperature, and fires, whereas convective storms are generally overlooked. We argue that existing literature provides clear support for the importance of storms as drivers of forest change. We reanalyze the largest plot-based study of tropical forest carbon dynamics to show that lightning frequency—an indicator of storm activity—strongly predicts forest carbon storage and residence time, and its inclusion improves model fit and weakens evidence for the effects of high temperatures. Convective storm activity has increased 5%–25% per decade over the past half century. Extrapolating from historic trends, we estimate that storms likely contribute ca. 50% of the reported increases in biomass mortality across Amazonia, with all realistic combinations of assumptions indicating a possible range of 12%–118%. Spatial variation in storm activity shows weak relationships with drought, demonstrating that forests can experience high drought stress, high storm activity, or both. Accordingly, we hypothesise that convective storms are among the most important drivers of tropical forest change, and as such, they require significant research investment to avoid misguiding science, policy, and management.

biomass carbon↗
DOE OSTI2026

Multi‐Decadal Dynamics of Wetland Methane Emissions Revealed by Knowledge‐Guided Machine Learning

Measurement of methane fluxes (FCH 4 ) from natural systems, such as wetlands, has lagged far behind carbon dioxide fluxes. Short and fragmented wetland FCH 4 data limit our ability to assess its long-term dynamics and potential climate feedbacks. Extrapolating short-term FCH 4 records to recent decades remains challenging for both process-based models and data-driven machine learning (ML) approaches. Here, we develop a knowledge-guided ML framework that integrates eddy covariance (EC) FCH 4 observations, field warming experiments, and biogeochemical knowledge to reconstruct the long-term FCH 4 budgets and trends. Focusing on the 11 longest EC monitoring sites in the AmeriFlux network, we found considerable variability in multi-decadal trends of wetland FCH 4 , with increases up to 14% per decade from 2000 to 2024. We also found that the strength of these increasing trends declines from high to low latitudes, highlighting the vulnerability of northern wetlands. This work presents novel and robust reconstructions of long-term wetland FCH 4 , offering critical benchmark datasets for bottom-up ecosystem models and advancing fundamental understanding of wetland biogeochemistry.

AmeriFlux site↗
DOE OSTI2025

A statistical representation of bond coating oxidation under environmental barrier coatings

Environmental barrier coatings (EBCs) protect SiC-based ceramic matrix composites (CMCs) in turbine hot sections from high-temperature volatilization in combustion gases. The formation of a SiO 2 thermally grown oxide (TGO) is expected under the EBC after long-term operation. The oxidation resistance of the EBC is understood as a life-limiting factor for the CMC, and this work predicts long-term oxidation behavior under EBCs through a simple statistical approach. Specimens were exposed to 1350°C isothermal conditions for 100-h thermal cycles in flowing steam for up to 1000 h. The EBC morphology, SiO 2 thickness, and SiO 2 cracking behavior were assessed. Using thousands of SiO 2 thickness measurements across many millimeters of the interface, a realistic representation of the entire TGO was captured via a lognormal distribution. The lognormal fit parameters were extrapolated out to 25 000 h to assess the degree of SiO 2 growth, the spread of SiO 2 thicknesses related to the rough oxidizing interface, and percentages of the intermediate bond coating consumed. In conclusion, local interfacial defects from the coating deposition process are identified as local failure points for EBC—CMC systems.

SiO 2↗