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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

On the Links Between Ice Nucleation, Cloud Phase, and Climate Sensitivity in CESM2

Ice nucleation in mixed-phase clouds has been identified as a critical factor in projections of future climate. Here we explore how this process influences climate sensitivity using the Community Earth System Model 2 (CESM2). We find that ice nucleation affects simulated cloud feedbacks over most regions and levels of the troposphere, not just extratropical low clouds. However, with present-day global mean cloud phase adjusted to replicate satellite retrievals, similar total cloud feedback is attained whether ice nucleation is simulated as aerosol-sensitive, insensitive, or absent. These model experiments all result in a strongly positive total cloud feedback, as in the default CESM2. A microphysics update from CESM1 to CESM2 had substantially weakened ice nucleation, due partly to a model issue. Our findings indicate that this update reduced global cloud phase bias, with CESM2's high climate sensitivity reflecting improved mixed-phase cloud representation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Optimal choice of multiple line-of-sight measurements determining plasma hotspot velocity at the National Ignition Facility

The measurement of plasma hotspot velocity provides an important diagnostic of implosion performance for inertial confinement fusion experiments at the National Ignition Facility. The shift of the fusion product neutron mean kinetic energy as measured along multiple line-of-sight time-of-flight spectrometers provides velocity vector components from which the hotspot velocity is inferred. Multiple measurements improve the hotspot velocity inference; however, practical considerations of available space, operational overhead, and instrumentation costs limit the number of possible line-of-sight measurements. Here, we propose a solution to this classical “experiment design” problem that optimizes the precision of the velocity inference for a limited number of measurements.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Gut microbiome changes with micronutrient supplementation in children with attention–deficit/hyperactivity disorder: the MADDY study

Micronutrients have demonstrated promise in managing inattention and emotional dysregulation in children with attention-deficit/hyperactivity disorder (ADHD). The biological mechanism by which micronutrients improve these symptoms remains unclear. One plausible pathway is through the gut-brain axis, the bi-directional communication network that links the gastrointestinal tract with the brain. This study examines changes in gut microbiome composition and diversity after micronutrients supplementation in children with ADHD (N=44) and sheds light on potential mechanisms responsible for the response to micronutrients as measured by clinician-rated global impression. Participants from this investigation represent a sub-group of the Micronutrients for ADHD in Youth (MADDY) study, a double blind randomized controlled study in which participants received either micronutrients or a placebo for 8 weeks, followed by an 8-week open label extension with micronutrients for all participants. Stool samples collected at baseline, week 8, and week 16 were analyzed using 16S rRNA amplicon sequencing targeting the V4 hypervariable region. Pairwise compositional analyses served as the primary means for investigating changes in gut microbiome composition between micronutrients versus placebo groups and responders versus non-responders. A significant change in microbial evenness, as measured by alpha diversity, was observed following micronutrients, and the phylum Actinobacteriota decreased in the micronutrients group compared to placebo. Additionally, two bacterial families: Rikenellaceae and Oscillospiraceae, exhibited a significant increase in change of gut microbiome composition following micronutrients between responders and non-responders. These findings suggest that micronutrients modulated the composition of the gut microbiome and point towards specific bacterial changes associated with response to micronutrients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Compton scattering from He 4 at the TUNL HI γ S facility

Differential cross sections for elastic Compton scattering from 4 He have been measured with high statistical precision at the High Intensity γ-ray Source at laboratory scattering angles of 55°, 90°, and 125° using a quasi-monoenergetic photon beam with a weighted mean energy value of 81.3 MeV. The results are compared to previous measurements and similar fore-aft asymmetry in the angular distribution of the differential cross sections is observed. Lastly, this experimental work is expected to strongly motivate the development of effective-field-theory calculations of Compton scattering from 4 He to fully interpret the data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Trends in spatial correlations, two-phase coexistence, and criticality in a class of type-2 Schloegl models for autocatalysis

A class of type-2 Schloegl models is considered for particles on a square lattice with variable-range cooperativity. These models involve: (i) spontaneous particle annihilation at rate p; (ii) autocatalytic particle creation at unoccupied sites (i, j) with n ⩾ 2 particles within a specified neighborhood, Ω 𝑁 (i, j), of sites at rate $k_n$ = $\frac{^{(^n_2)}}{_{(^N_2)}}$ = $\frac{n{(n-1)}}{_{N(N-1)}}$; and (iii) possible spontaneous particle creation at unoccupied sites at “small” rate ɛ ⩾ 0. In some cases, Ω 𝑁 just includes all symmetry-equivalent sites at a single specific distance 𝑑 (in units of lattice constants) from the unoccupied site, e.g., 𝑑 = 1 (nearest-neighbor sites) where 𝑁 = 4, or 𝑑 = √5 (or √13 or…) where 𝑁 = 8. In other cases, Ω 𝑁 includes sites multiple distances from the unoccupied site, e.g., 𝑑 = {1,√2}, where 𝑁 = 8. Kinetic Monte Carlo (KMC) simulation reveals that these models exhibit a nonequilibrium discontinuous phase transition between high- and low-density states below a critical point, ɛ < ɛ c , with generic two-phase coexistence (2PC) at least for smaller 𝑁. With some exceptions, there is an approach toward mean-field behavior with increasing 𝑁 (so the regime of generic 2PC shrinks, and ɛ c approaches the mean-field value of 1/27). Additional insight into trends is provided by analysis of the exact master equations for the models via hierarchical truncation. These truncations utilize suitably tailored pair approximations which reflect the dominant nonequilibrium spatial correlations. These correlations in turn are shown to reflect the details of the autocatalytic particle creation process. For spatially heterogeneous states, the truncations produce coupled sets of lattice differential equations (LDE) which can describe orientation-dependent propagation of an interface between high- and low-density steady states for ɛ < ɛ c . Pair approximation values of 𝑝 = 𝑝 eq where the interface is stationary, and its orientation-dependence, are in semiquantitative agreement with KMC results. In conclusion, this comparison accounts for propagation failure in the LDE which complicates interpretation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Deep Learning Approaches for Predicting the Surface Tension of Ionic Liquids

Ionic liquids (ILs) are a novel class of solvents that have attracted significant attention due to their unique and tunable properties. Among their physiochemical characteristics, surface tension plays a critical role in various industrial applications including electrolytes, heat transfer fluids, and separation processes. However, because of the exploratory nature of IL design and the vast combinatorial space of possible anion–cation pairs, the experimental determination of these properties is often impractical, being both time-consuming and costly. To overcome these challenges, computational approaches are increasingly employed to develop accurate predictive models that can accelerate IL discovery and design. In this study, we present two deep learning (DL) models for predicting the surface tension of ILs across a broad temperature range at a constant pressure. The models use simplified molecular input line entry system, SMILES, representations of ILs to extract molecular features as inputs. Both DL models demonstrate excellent agreement with experimental data, achieving an R 2 value of 0.990 and a root-mean-square error of 0.792 mN/m. In conclusion, these results offer valuable insights for the rapid screening and rational design of ILs with tailored surface tension values.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Protein–Ligand Binding Free-Energy Calculations with ARROW–A Purely First-Principles Parameterized Polarizable Force Field

Protein–ligand binding free-energy calculations using molecular dynamics (MD) simulations have emerged as a powerful tool for in silico drug design. Here, we present results obtained with the ARROW force field (FF)–a multipolar polarizable and physics-based model with all parameters fitted entirely to high-level ab initio quantum mechanical (QM) calculations. ARROW has already proven its ability to determine solvation free energy of arbitrary neutral compounds with unprecedented accuracy. The ARROW FF parameterization is now extended to include coverage of all amino acids including charged groups, allowing molecular simulations of a series of protein–ligand systems and prediction of their relative binding free energies. We ensure adequate sampling by applying a novel technique that is based on coupling the Hamiltonian Replica exchange (HREX) with a conformation reservoir generated via potential softening and nonequilibrium MD. ARROW provides predictions with near chemical accuracy (mean absolute error of ~0.5 kcal/mol) for two of the three protein systems studied here (MCL1 and Thrombin). The third protein system (CDK2) reveals the difficulty in accurately describing dimer interaction energies involving polar and charged species. Overall, for all of the three protein systems studied here, ARROW FF predicts relative binding free energies of ligands with a similar accuracy level as leading nonpolarizable force fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets

Quantifying charge-state transition energy levels of impurities in semiconductors is critical to understanding and engineering their optoelectronic properties for applications ranging from solar photovoltaics to infrared lasers. While these transition levels can be measured and calculated accurately, such efforts are time-consuming and more rapid prediction methods would be beneficial. Here, we significantly reduce the time typically required to predict impurity transition levels using multi-fidelity datasets and a machine learning approach employing features based on elemental properties and impurity positions. We use transition levels obtained from low-fidelity (i.e., local-density approximation or generalized gradient approximation) density functional theory (DFT) calculations, corrected using a recently proposed modified band alignment scheme, which well-approximates transition levels from high-fidelity DFT (i.e., hybrid HSE06). Further, the model fit to the large multi-fidelity database shows improved accuracy compared to the models trained on the more limited high-fidelity values. Crucially, in our approach, when using the multi-fidelity data, high-fidelity values are not required for model training, significantly reducing the computational cost required for training the model. Our machine learning model of transition levels has a root mean squared (mean absolute) error of 0.36 (0.27) eV vs high-fidelity hybrid functional values when averaged over 14 semiconductor systems from the II–VI and III–V families. As a guide for use on other systems, we assessed the model on simulated data to show the expected accuracy level as a function of bandgap for new materials of interest. Finally, we use the model to predict a complete space of impurity charge-state transition levels in all zinc blende III–V and II–VI systems.

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

Exploring the parameter space of MagLIF implosions using similarity scaling. I. Theoretical framework

Magneto-inertial fusion concepts, such as the magnetized liner inertial fusion (MagLIF) platform, constitute an alternative path for achieving ignition and significant fusion yields in the laboratory. The space of experimental input parameters defining a MagLIF load is highly multi-dimensional, and the implosion itself is a complex event involving many physical processes. In the first paper of this series, we develop a simplified analytical model that identifies the main physical processes at play during a MagLIF implosion. Using non-dimensional analysis, we determine the most important dimensionless parameters characterizing MagLIF implosions and provide estimates of such parameters using typical fielded or experimentally observed quantities for MagLIF. Here, we then show that MagLIF loads can be “incompletely” similarity scaled, meaning that the experimental input parameters of MagLIF can be varied such that many (but not all) of the dimensionless quantities are conserved. Based on similarity-scaling arguments, we can explore the parameter space of MagLIF loads and estimate the performance of the scaled loads. Then, in the follow-up papers of this series, we test the similarity-scaling theory for MagLIF loads against simulations for two different scaling “vectors,” which include current scaling and rise-time scaling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: exploring the halo occupation distribution model for emission line galaxies

We study the modelling of the halo occupation distribution (HOD) for the eBOSS DR16 emission line galaxies (ELGs). Motivated by previous theoretical and observational studies, we consider different physical effects that can change how ELGs populate haloes. We explore the shape of the average HOD, the fraction of satellite galaxies, their probability distribution function (PDF), and their density and velocity profiles. Our baseline HOD shape was fitted to a semi-analytical model of galaxy formation and evolution, with a decaying occupation of central ELGs at high halo masses. We consider Poisson and sub/super-Poissonian PDFs for satellite assignment. We model both Navarro–Frenk–White and particle profiles for satellite positions, also allowing for decreased concentrations. We model velocities with the virial theorem and particle velocity distributions. Additionally, we introduce a velocity bias and a net infall velocity. We study how these choices impact the clustering statistics while keeping the number density and bias fixed to that from eBOSS ELGs. The projected correlation function, w p , captures most of the effects from the PDF and satellites profile. The quadrupole, ξ 2 , captures most of the effects coming from the velocity profile. We find that the impact of the mean HOD shape is subdominant relative to the rest of choices. Here, we fit the clustering of the eBOSS DR16 ELG data under different combinations of the above assumptions. The catalogues presented here have been analysed in companion papers, showing that eBOSS RSD+BAO measurements are insensitive to the details of galaxy physics considered here. These catalogues are made publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Isotopic equilibrium constants for very low-density and low-temperature nuclear matter

Yields of equatorially emitted light isotopes, 1 ≤ Z ≤ 14, observed in ternary fission in the reaction 241 Pu(n th, f) are employed to determine apparent chemical equilibrium constants for low-temperature and low-density nuclear matter. The degree of equilibration and role of medium modifications are probed through a comparison of experimentally derived reaction quotients with equilibrium constants calculated using a relativistic mean-field model employing a universal medium modification correction for the attractive σ meson coupling. The results of these comparisons indicate that equilibrium is achieved for the lighter ternary fission isotopes. For the heavier isotopes experimental reaction quotients are well below calculated equilibrium constants. This is attributed to a dynamical limitation reflecting insufficient time for full equilibrium to develop. The role of medium effects leading to yield reductions is discussed as is the apparent enhancement of yields for 8 He and other very neutron rich exotic nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics

A general agreement in psycholinguistics claims that syntax and meaning are unified precisely and very quickly during online sentence processing. Although several theories have advanced arguments regarding the neurocomputational bases of this phenomenon, we argue that these theories could potentially benefit by including neurophysiological data concerning cortical dynamics constraints in brain tissue. In addition, some theories promote the integration of complex optimization methods in neural tissue. In this paper we attempt to fill these gaps introducing a computational model inspired in the dynamics of cortical tissue. In our modeling approach, proximal afferent dendrites produce stochastic cellular activations, while distal dendritic branches–on the other hand–contribute independently to somatic depolarization by means of dendritic spikes, and finally, prediction failures produce massive firing events preventing formation of sparse distributed representations. The model presented in this paper combines semantic and coarse-grained syntactic constraints for each word in a sentence context until grammatically related word function discrimination emerges spontaneously by the sole correlation of lexical information from different sources without applying complex optimization methods. By means of support vector machine techniques, we show that the sparse activation features returned by our approach are well suited—bootstrapping from the features returned by Word Embedding mechanisms—to accomplish grammatical function classification of individual words in a sentence. In this way we develop a biologically guided computational explanation for linguistically relevant unification processes in cortex which connects psycholinguistics to neurobiological accounts of language. We also claim that the computational hypotheses established in this research could foster future work on biologically-inspired learning algorithms for natural language processing applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Scaling Field-Theoretic Simulation for Multicomponent Mixtures with Neural Operators

Multicomponent polymer mixtures are ubiquitous in biological self-organization but are notoriously difficult to study computationally. Plagued by both slow single molecule relaxation times and slow equilibration within dense mixtures, molecular dynamics simulations are typically infeasible at the spatial scales required to study the stability of mesophase structure. Polymer field theories offer an attractive alternative, but analytical calculations are only tractable for mean-field theories and nearby perturbations, constraints that become especially problematic for fluctuation-induced effects such as coacervation. Here, we show that a recently developed technique for obtaining numerical solutions to partial differential equations based on operator learning, neural operators, lends itself to a highly scalable training strategy by parallelizing per-species operator maps. We illustrate the efficacy of our approach on six-component mixtures with randomly selected compositions and that it significantly outperforms the state-of-the-art pseudospectral integrators for field-theoretic simulations, especially as polymer lengths become long.

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

Cropland expansion in the United States produces marginal yields at high costs to wildlife

Recent expansion of croplands in the United States has caused widespread conversion of grasslands and other ecosystems with largely unknown consequences for agricultural production and the environment. Here we assess annual land use change 2008–16 and its impacts on crop yields and wildlife habitat. We find that croplands have expanded at a rate of over one million acres per year, and that 69.5% of new cropland areas produced yields below the national average, with a mean yield deficit of 6.5%. Observed conversion infringed upon high-quality habitat that, relative to unconverted land, had provided over three times higher milkweed stem densities in the Monarch butterfly Midwest summer breeding range and 37% more nesting opportunities per acre for waterfowl in the Prairie Pothole Region of the Northern Great Plains. Our findings demonstrate a pervasive pattern of encroachment into areas that are increasingly marginal for production, but highly significant for wildlife, and suggest that such tradeoffs may be further amplified by future cropland expansion.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Assessing the Impact of a Forest Canopy on Near-Surface Wind Statistics

Representing the forest canopy in atmospheric numerical models should improve simulated winds within and above the canopy up to a few hundred meters above the ground. Here, in this study, we implement a forest canopy parameterization into the Weather Research and Forecasting (WRF) Model in a large-eddy simulation (LES) mode by applying drag forces across multiple layers within the canopy height. We use unique observations from the Lidar Experiments for Assessing Flow over Forests (LEAFF) field campaign at the Wind River Experimental Forest (WREF) in the U.S. Pacific Northwest to evaluate model performance. In a 2-day case study, the canopy parameterization improved wind predictions both within and above the canopy, particularly during the daytime and at finer grid resolution. Without it, winds were frequently overpredicted above the canopy. Similarly, derived quantities such as the wind shear index also yielded estimates closer to observations with the canopy parameterization implemented. These findings suggest that representing the canopy using drag forces alone can improve simulated mean winds up to 200 m above the surface. Furthermore, second-order statistical moments of wind were more sensitive to canopy density than first-order moments, especially during the daytime. This increased sensitivity and the improved daytime performance in wind speed—evidenced by the lowest bias from observations (3% compared to 20% over diurnal cycle)—imply that winds above the canopy layer are strongly influenced by how well turbulence above the canopy is modeled. The results of this study can serve as a foundation for parameterizing forest canopy effects in coarser weather forecast models.

Energy - Wind↗
DOE OSTI2020

Method and apparatus for evaluating superconducting tunnel junction detector noise versus bias voltage

A technique for characterizing the noise behavior of a superconducting tunnel junction (STJ) detector as a function of its applied bias voltage Vb by stepping the STJ's bias voltage across a predetermined range and, at each applied bias, making multiple measurements of the detector's current, calculating their mean and their standard deviation from their mean, and using this standard deviation as a measure of the STJ detector's noise at that applied bias. Because the method is readily executed under computer control, it is particularly useful when large numbers of STJ detectors require biasing, as in STJ detector arrays In a preferred implementation, the STJ is measured under computer control by attaching it to a digital spectrometer comprising a digital x-ray processor (DXP) coupled to a preamplifier that can set the STJ's bias voltage Vb using a digital-to-analog converter (DAC) controlled by the DXP.

Warburton, William K.↗
DOE OSTI2023

Loop-current charge density wave driven by long-range Coulomb repulsion on the kagomé lattice

Recent experiments on vanadium-based nonmagnetic kagomé metals A⁢V 3 ⁢Sb 5 ⁢(A=K,Rb,Cs) revealed evidence for possible spontaneous time-reversal symmetry (TRS) breaking in the charge density wave (CDW) ordered state. The long-sought-after quantum order of loop currents has been suggested as a candidate for the TRS breaking state. However, a microscopic model for the emergence of the loop-current CDW due to electronic correlations is still lacking. Here, in this work, we calculate the susceptibility of the real and imaginary bond orders on the kagomé lattice near van Hove filling, and reveal the importance of next-nearest-neighbor Coulomb repulsion V 2 in triggering the instability toward imaginary bond ordered CDW. The concrete effective single-orbital t–V 1 –V 2 model on the kagomé lattice is then studied, where t and V 1 are the hopping and Coulomb repulsion on the nearest-neighbor bonds. We obtain the mean-field ground states, analyze their properties, and determine the phase diagram in the plane spanned by V 1 and V 2 at van Hove filling. The region dominated by V 1 is occupied by a 2⁢a 0 ×2⁢a 0 real CDW insulator with the inverse of Star-of-David (ISD) bond configuration. Increasing V 2 indeed drives a first-order transition from ISD to stabilized loop-current insulators that exhibit four possible current patterns of different topological properties, leading to orbital Chern insulators. We then extend these results away from van Hove filling and show that electron doping helps the stabilization of loop currents, and gives rise to doped orbital Chern insulators with emergent Chern Fermi pockets carrying large Berry curvature and orbital magnetic moment. Our findings provide a concrete model realization of the loop-current Chern metal at the mean-field level for the TRS breaking normal state of the kagomé superconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Phase Identification in Synchrotron X-ray Diffraction Patterns of Ti–6Al–4V Using Computer Vision and Deep Learning

X-ray diffraction patterns contain information about the atomistic structure and microstructure (defect population) of materials, extracting detailed information from diffraction patterns is complex, demanding and relies on prior knowledge. Here, we hypothesize that deep-learning techniques can help to perform an effective and accurate analysis with high throughput rates. To demonstrate this concept, we applied a novel deep learning framework to determine the evolution of the β-phase volume fraction in a Ti–6Al–4V alloy during heat-treatment from video sequences of 2D diffraction patterns recorded in transmission and with highly monochromatic radiation in a synchrotron beamline. In particular, we studied the impact of network design on prediction reliability and computational performance. Networks of different architectures were trained using 3008 experimental 2D patterns. A well-tuned model was found to reproduce the phase fractions of another experimental data set, consisting of 1100 diffraction patterns, with a mean-square error as small as 2.6 x 10 -4 . The average prediction error of β-phase volume fraction was within 1.6 x 10 -2 (in each diffraction pattern) of the values obtained by conventional methods. Our work demonstrates that convolutional neural networks can evaluate high energy X-ray diffraction patterns with a remarkable level of reliability. Furthermore, it demonstrates the significance of network design on the reliability of predictions and computational performance. The most complex models do not necessarily result in highest accuracy and may even fail to learn from the data.

36 MATERIALS SCIENCE↗