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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules

We introduce QM7-X, a comprehensive dataset of 42 physicochemical properties for ≈4.2 million equilibrium and non-equilibrium structures of small organic molecules with up to seven non-hydrogen (C, N, O, S, Cl) atoms. To span this fundamentally important region of chemical compound space (CCS), QM7-X includes an exhaustive sampling of (meta-)stable equilibrium structures—comprised of constitutional/structural isomers and stereoisomers, e.g., enantiomers and diastereomers (including cis-/trans- and conformational isomers)—as well as 100 non-equilibrium structural variations thereof to reach a total of ≈4.2 million molecular structures. Computed at the tightly converged quantum-mechanical PBE0+MBD level of theory, QM7-X contains global (molecular) and local (atom-in-a-molecule) properties ranging from ground state quantities (such as atomization energies and dipole moments) to response quantities (such as polarizability tensors and dispersion coefficients). By providing a systematic, extensive, and tightly-converged dataset of quantum-mechanically computed physicochemical properties, we expect that QM7-X will play a critical role in the development of next-generation machine-learning based models for exploring greater swaths of CCS and performing in silico design of molecules with targeted properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Hydropower expansion in eco-sensitive river basins under global energy-economic change

Vast hydropower resources remain untapped globally, the deployment of which could provide energy-economic benefits but impact riverine ecosystems. Across eco-sensitive river basins, it is unclear how drivers of hydropower expansion, such as rapid economic growth and a low-carbon energy transition, could interact with countervailing forces, such as increasingly cost-competitive variable renewable energy (VRE). Using an integrated energy-water-economy model, we explore the effects of these forces on long-term hydropower expansion in the world’s 20 most eco-sensitive basins, which have high ecological richness and untapped hydropower potential. Here, we find that a low-carbon transition exerts the strongest development pressure, causing deployment exceeding 80% of exploitable potential in more than 72% of eco-sensitive basins by 2050, most of which have limited deployment today. Rapid economic growth induces such extensive deployment in only 44% of eco-sensitive basins. Enhanced integration of VRE reduces deployment, alleviating the impacts of rapid economic growth but not the low-carbon transition.

13 HYDRO ENERGY↗
DOE OSTI2021

Emergence of Lanes and Turbulent-like Motion in Active Spinner Fluid

Assemblies of self-rotating particles are gaining interest as a novel realization of active matter with unique collective behaviors such as edge currents and non-trivial dynamic states. Here, we develop a continuum model for a system of fluid-embedded spinners by coarse-graining the equations of motion of the discrete particles. We apply the model to explore mixtures of clockwise and counterclockwise rotating spinners. We find that the dynamics is sensitive to fluid inertia: in the inertialess system, after transient turbulent-like motion the spinners segregate and form steady traffic lanes. At small but finite Reynolds number instead, the turbulent-like motion persists and the system exhibits a chirality breaking transition leading to a single rotation sense state. Our results shed light on the dynamic behavior of non-equilibrium materials exemplified by active spinners.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials

Natural porous materials such as nanoporous clays are used as green and low-cost adsorbents and catalysts. The key factors determining their performance in these applications are the pore morphology and surface activity, which are typically represented by properties such as specific surface area, pore volume, micropore content and pH. The latter may be modified and tuned to specific applications through material processing and/or chemical treatment. Characterization of the material, raw or processed, is typically performed experimentally, which can become costly especially in the context of tuning of the properties towards specific application requirements and needing numerous experiments. In this work, we present an application of tree-based machine learning methods trained on experimental datasets to accelerate the characterization of natural porous materials. The resulting models allow reliable prediction of the outcomes of experimental characterization of processed materials (R2 from 0.78 to 0.99) as well as identification of key factors contributing to those properties through feature importance analysis. Furthermore, the high throughput of the models enables exploration of processing parameter–property correlations and multiobjective optimization of prototype materials towards specific applications. We have applied these methodologies to pinpoint and rationalize optimal processing conditions for clays exploitable in acid catalysis. One of such identified materials was synthesized and tested revealing appreciable acid character improvement with respect to the pristine material. Specifically, it achieved 79% removal of chlorophyll-a in acid catalyzed degradation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Frequency-dependent sensitivity of AC elastocaloric effect measurements explored through analytical and numerical models

We present a comprehensive study of the frequency-dependent sensitivity for measurements of the AC elastocaloric effect by applying both exactly soluble models and numerical methods to the oscillating heat flow problem. These models reproduce the finer details of the thermal transfer functions observed in experiments, considering here representative data for single-crystal Ba(Fe1−xCox)2As2. Based on our results, we propose a set of practical guidelines for experimentalists using this technique. This work establishes a baseline against which the frequency response of the AC elastocaloric technique can be compared and provides intuitive explanations of the detailed structure observed in experiments.

Straquadine, J. A. W. (ORCID:0000000165309541)↗
DOE OSTI2023

Evaporation and scattering of neon, methane, and water from a dodecane flat liquid jet

The evaporation and scattering of Ne, CD 4 , and D 2 O from a dodecane flat liquid jet are investigated in a molecular beam apparatus. The experiment yields translational energy distributions as a function of scattering angle by means of a rotatable mass spectrometer. In the evaporation experiments, one observes a Maxwell–Boltzmann distribution with a cos $θ$ angular distribution superimposed on a weak, isotropic background. The scattering experiments show contributions from impulsive scattering and thermal desorption. At select incident angles for the three systems, angular distributions show super-specular scattering for the impulsive scattering channel, an effect attributed to anisotropic momentum transfer to the liquid surface. The impulsive scattering channel is analyzed with a soft-sphere model to explore energy transfer between the scatterer and liquid as a function of deflection angle. In conclusion, compared to Ne scattering, the polyatomic gases exhibit more thermal desorption and, in the impulsive scattering channel, a higher degree of internal excitation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Correlation functions from tensor network influence functionals: The case of the spin-boson model

We investigate the application of matrix product state (MPS) representations of the influence functionals (IFs) for the calculation of real-time equilibrium correlation functions in open quantum systems. Focusing specifically on the unbiased spin-boson model, we explore the use of IF-MPSs for complex time propagation, as well as IF-MPSs for constructing correlation functions in the steady state. We examine three different IF approaches: one based on the Kadanoff–Baym contour targeting correlation functions at all times, one based on a complex contour targeting the correlation function at a single time, and a steady state formulation, which avoids imaginary or complex times, while providing access to correlation functions at all times. We show that within the IF language, the steady state formulation provides a powerful approach to evaluate equilibrium correlation functions.

Chemistry↗
DOE OSTI2026

Attention-based explainability for structure–property relationships

Machine learning methods are emerging as a universal paradigm for constructing correlative structure–property relationships in materials science based on multimodal characterization. However, this necessitates the development of methods for the physical interpretability of the resulting correlative models. Here, we demonstrate the potential of attention-based neural networks for revealing structure–property relationships and the underlying physical mechanisms, using the ferroelectric properties of PbTiO3 thin films as a case study. Through the analysis of attention scores, we disentangle the influence of distinct domain patterns on the polarization switching process. The attention-based Transformer model is explored both as a direct interpretability tool and as a surrogate for explaining representations learned via unsupervised machine learning, enabling the identification of physically grounded correlations. We compare attention-derived interpretability scores with classical SHapley Additive exPlanations analysis and show that, in contrast to applications in natural language processing, attention mechanisms in materials science exhibit high efficiency in highlighting meaningful structural features.

Slautin, Boris [Independent Researcher]↗
DOE OSTI2025

Mechanistic Multiscale Uncertainty Propagation in Support of Accelerated Fuel Qualification

Taking a nuclear fuel concept through the research, development, and qualification stages has historically taken on the order of 20 to 25 years because of extensive irradiation tests required for a variety of conditions. The concept of accelerated fuel qualification (AFQ) has been proposed to increase the innovation pace for nuclear fuels. The goal of AFQ is not to replace the traditional qualification approach but rather to reduce the total number of experiments required to ensure approval from the regulatory authority. Of the many AFQ approaches being explored, advanced modeling—and, in particular, mechanistic modeling—is in a uniquely cross-cutting position to reduce the number of required integral tests through the inclusion of separate-effects testing, while helping to extrapolate reactor performance during rare events. We make the case that propagation of uncertainty through various computational length scales helps contextualize mechanistic modeling. We will utilize UO 2 fission gas diffusion predictions from the atomistically informed cluster dynamics code Centipede to inform fuel performance rodlet simulations using the BISON finite element code as the metric for showing how multiscale mechanistic uncertainty quantification can help reduce uncertainty in fuel performance. In conclusion, by quantifying uncertainty and its reduction through multiscale modeling, the qualification process may be accelerated through the reduction of costly irradiation experiments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Plasma-catalyst modeling for materials selection: challenges and opportunities in nitrogen oxidation

Reliable kinetic models are essential to rationalize observations and to guide the selection of appropriate plasma-catalyst combinations. Kinetic models coupling plasma- and heterogeneous-catalytic-chemistries, however, are primitive. Here, we combine reduced plasma chemistries and density functional theory parameterized surface reactions to model NO formation from N2 oxidation, an alternative nitrogen fixation process. We first show plasma excited species can enhance turnover frequencies of both Pt and Au. The enhancing potential is a function of plasma species and their densities. We then compare NO production between plasma/Pt and plasma/Au with an integral reactor model and explore the dependence of NO production as a function of plasma conditions, reactor configurations and chemical compositions. Plasma/Pt generates more NO than plasma/Au under most thermal and plasma parametric conditions. Plasma/Au outperforms plasma/Pt under high plasma excitations at low temperatures. Furthermore, these results highlight that selection of plasma-catalyst combinations should consider the properties of plasmas, materials and the coupling of the two.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Signatures of self-interacting dark matter on cluster density profile and subhalo distributions

Non-gravitational interactions between dark matter particles with strong scattering, but relatively small annihilation and dissipation, has been proposed to match various observables on cluster and group scales. In this paper, we present the results from large cosmological simulations which include the effects of different self-interaction scenarios. In particular we explore a model with the differential cross section that can depend on both the relative velocity of the interacting particles and the angle of scattering. We focus on how quantities, such as the stacked density profiles, subhalo counts and the splashback radius change as a function of different forms of self-interaction. We find that self-interactions not only affect the central region of the cluster, the effect well known from previous studies, but also significantly alter the distribution of subhalos and the density of particles out to the splashback radius. Therefore, our results suggest that current weak lensing data can already put constraints on the self-interaction cross-section that are only slightly weaker than the Bullet Cluster constraints (σ/m ≲ 2 cm 2 /g), and future lensing surveys should be able to tighten them even further making halo profiles on cluster scales a competitive probe for DM physics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Supermassive primordial black holes from inflation

There is controversy surrounding the origin and evolution of our universe's largest supermassive black holes (SMBHs). In this study, we consider the possibility that some of these black holes formed from the direct collapse of primordial density perturbations. Since the mass of a primordial black hole is limited by the size of the cosmological horizon at the time of collapse, these SMBHs must form rather late, and are naively in conflict with constraints from CMB spectral distortions. These limits can be avoided, however, if the distribution of primordial curvature perturbations is highly non-Gaussian. After quantifying the departure from Gaussianity needed to evade these bounds, we explore a model of multi-field inflation — a non-minimal, self-interacting curvaton model — which has all the necessary ingredients to yield such dramatic non-Gaussianities. We leave the detailed model building and numerics to a future study, however, as our goal is to highlight the challenges associated with forming SMBHs from direct collapse and to identify features that a successful model would need to have. This study is particularly timely in light of recent observations of high-redshift massive galaxy candidates by the James Webb Space Telescope as well as evidence from the NANOGrav experiment for a stochastic gravitational wave background consistent with SMBH mergers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

A storyline analysis of Hurricane Irma’s precipitation under various levels of climate warming

Abstract Understanding how extreme weather, such as tropical cyclones, will change with future climate warming is an interesting computational challenge. Here, the hindcast approach is used to create different storylines of a particular tropical cyclone, Hurricane Irma (2017). Using the community atmosphere model, we explore how Irma’s precipitation would change under various levels of climate warming. Analysis is focused on a 48 h period where the simulated hurricane tracks reasonably represent Irma’s observed track. Under future scenarios of 2 K, 3 K, and 4 K global average surface temperature increase above pre-industrial levels, the mean 3-hourly rainfall rates in the simulated storms increase by 3–7% K −1 compared to present. This change increases in magnitude for the 95th and 99th percentile 3-hourly rates, which intensify by 10–13% K −1 and 17–21% K −1 , respectively. Over Florida, the simulated mean rainfall accumulations increase by 16–26% K −1 , with local maxima increasing by 18–43% K −1 . All percent changes increase monotonically with warming level.

Environmental Sciences & Ecology↗
DOE OSTI2022

Quantum error mitigation by hidden inverses protocol in superconducting quantum devices

We present a method to improve the convergence of variational algorithms based on hidden inverses (HIs) to mitigate coherent errors. In the context of error mitigation, this means replacing the hardware implementation of certain Hermitian gates with their inverses. Doing so results in noise cancellation and a more resilient quantum circuit. This approach improves performance in a variety of two-qubit error models where the noise operator also inverts with the gate inversion. We apply the mitigation scheme on superconducting quantum processors running the variational quantum eigensolver (VQE) algorithm to find the H 2 ground-state energy. When implemented on superconducting hardware we find that the mitigation scheme effectively reduces the energy fluctuations in the parameter learning path in VQE, reducing the number of iterations for a converged value. We also provide a detailed numerical simulation of VQE performance under different noise models and explore how HIs and randomized compiling affect the underlying loss landscape of the learning problem. These simulations help explain our experimental hardware outcomes, helping to connect lower-level gate performance to application-specific behavior in contrast to metrics like fidelity which often do not provide an intuitive insight into observed high level performance.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Reinforcement learning pulses for transmon qubit entangling gates

The utility of a quantum computer is highly dependent on the ability to reliably perform accurate quantum logic operations. For finding optimal control solutions, it is of particular interest to explore model-free approaches, since their quality is not constrained by the limited accuracy of theoretical models for the quantum processor—in contrast to many established gate implementation strategies. In this work, we utilize a continuous control reinforcement learning algorithm to design entangling two-qubit gates for superconducting qubits; specifically, our agent constructs cross-resonance and CNOT gates without any prior information about the physical system. Using a simulated environment of fixed-frequency fixed-coupling transmon qubits, we demonstrate the capability to generate novel pulse sequences that outperform the standard cross-resonance gates in both fidelity and gate duration, while maintaining a comparable susceptibility to stochastic unitary noise. We further showcase an augmentation in training and input information that allows our agent to adapt its pulse design abilities to drifting hardware characteristics, importantly, with little to no additional optimization. Our results exhibit clearly the advantages of unbiased adaptive-feedback learning-based optimization methods for transmon gate design.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

The relation between optical and γ-ray emission in BL Lac sources

ABSTRACT The relativistic jets produced by some Active Galactic Nuclei (AGNs) are among the most efficient persistent sources of non-thermal radiation and represent an ideal laboratory for studying high-energy interactions. In particular, when the relativistic jet propagates along the observer’s line of sight, the beaming effect produces dominant signatures in the observed spectral energy distribution (SED), from the radio domain up to the highest energies, with the further possibility of resulting in radiation-particle multimessenger associations. In this work, we investigate the relationships between the emission of γ rays and the optical spectra of a sample of AGN, selected from BL Lac sources detected by the Fermi Large Area Telescope (Fermi-LAT). We find that there is a close relationship between the optical and γ-ray spectral indices. Despite all the limitations due to the non-simultaneity of the data, this observation strongly supports a substantial role of Synchrotron-Self Compton (SSC) radiation in a single zone leptonic scenario for most sources. This result simplifies the application of theoretical models to explore the physical parameters of the jets in this type of sources.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Assessing the impact of uniform rotation on the structure of neutron stars

Driven by recent laboratory experiments and astronomical observations, significant advances have deepened our understanding of neutron-star physics. NICER's Pulse Profile Modeling has refined our knowledge of neutron star masses and radii, while gravitational-wave detections have revealed key insights into the structure of neutron stars. Particularly relevant is the extraction of the tidal deformability by the LIGO-Virgo collaboration and the most recent determination of stellar radii by NICER, both suggesting a relatively soft equation of state (EOS) at intermediate densities. Additionally, measurements from the PREX collaboration and from pulsar timing suggest instead that the EOS is stiff in the vicinity of saturation density and at the highest densities accessible to date. But how stiff can the EOS be at these very high densities? Recent events featuring compact objects near the “lower mass gap” have raised questions about the existence of very massive neutron stars. Motivated by this finding and in light of new refinements to theoretical models, we explore the possibility that these massive objects may indeed be rapidly rotating neutron stars. Here, we explore how rotation affects both the maximum neutron star mass and their associated radii, and discuss the implications they may have on the equation of state.

Equations of state of nuclear matter↗
DOE OSTI2023

Superconducting diode effect in quasi-one-dimensional systems

The recent observations of the superconducting diode effect pose the challenge to fully understand the necessary ingredients for nonreciprocal phenomena in superconductors. In this theoretical work, we focus on the nonreciprocity of the critical current in a quasi-one-dimensional superconductor. We define the critical current as the value of the supercurrent at which the quasiparticle excitation gap closes (depairing). Once the critical current is exceeded, the quasiparticles can exchange energy with the superconducting condensate, giving rise to dissipation. Our minimal model can be microscopically derived as a low-energy limit of a Rashba spin-orbit coupled superconductor in a Zeeman field. Within the proposed model, we explore the nature of the nonreciprocal effects of the critical current both analytically and numerically. Further, our results quantify how system parameters such as spin-orbit coupling and quantum confinement affect the strength of the superconducting diode effect. Our theory provides a complementary description to Ginzburg-Landau theories of the effect.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗