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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Green’s function coupled cluster formulations utilizing extended inner excitations

Here, we analyze new approximations of the Green’s function coupled cluster (GFCC) method where locations of poles are improved by extending the excitation level of inner auxiliary operators. These new GFCC approximations can be categorized as the GFCC-i( n, m ) method, where the excitation level of the inner auxiliary operators ( m ) used to describe the ionization potential and electron affinity effects in the N-1 and N+1 particle spaces is higher than the excitation level ( n ) used to correlate the ground-state coupled cluster wave function for the N-electron system. Furthermore, we reveal the so-called “n + 1” rule in this category [or the GFCC-i( n, n + 1) method], which states that in order to maintain size-extensivity of the Green’s function matrix elements, the excitation level of inner auxiliary operators X p (ω) and Y q (ω) cannot exceed n + 1. We also discuss the role of the moments of coupled cluster equations that in a natural way assures these properties. Our implementation in the present study is focused on the first approximation in this GFCC category, i.e., the GFCC-i(2,3) method. As our first practice, we use the GFCC-i(2,3) method to compute the spectral functions for the N 2 and CO molecules in the inner and outer valence regimes. Lastly, in comparison with the Green’s function coupled cluster singles, doubles results, the computed spectral functions from the GFCC-i(2,3) method exhibit better agreement with the experimental results and other theoretical results, particularly in terms of providing higher resolution of satellite peaks and more accurate relative positions of these satellite peaks with respect to the main peak positions.

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

Charting C–C coupling pathways in electrochemical CO 2 reduction on Cu(111) using embedded correlated wavefunction theory

The electrochemical CO 2 reduction reaction (CO 2 RR) powered by excess zero-carbon-emission electricity to produce especially multicarbon (C 2+ ) products could contribute to a carbon-neutral to carbon-negative economy. Foundational to the rational design of efficient, selective CO 2 RR electrocatalysts is mechanistic analysis of the best metal catalyst thus far identified, namely, copper (Cu), via quantum mechanical computations to complement experiments. Here, we apply embedded correlated wavefunction (ECW) theory, which regionally corrects the electron exchange-correlation error in density functional theory (DFT) approximations, to examine multiple C–C coupling steps involving adsorbed CO (*CO) and its hydrogenated derivatives on the most ubiquitous facet, Cu(111). We predict that two adsorbed hydrogenated CO species, either *COH or *CHO, are necessary precursors for C–C bond formation. The three kinetically feasible pathways involving these species yield all three possible products: *COH–CHO, *COH–*COH, and *OCH–*OCH. The most kinetically favorable path forms *COH–CHO. In contrast, standard DFT approximations arrive at qualitatively different conclusions, namely, that only *CO and *COH will prevail on the surface and their C–C coupling paths produce only *COH–*COH and *CO–*CO, with a preference for the first product. This work demonstrates the importance of applying qualitatively and quantitatively accurate quantum mechanical method to simulate electrochemistry in order ultimately to shed light on ways to enhance selectivity toward C 2+ product formation via CO 2 RR electrocatalysts.

30 DIRECT ENERGY CONVERSION↗
NASA NTRS1995

Hubble space telescope observations of young star clusters in NGC-4038/4039, 'the antennae' galaxies

New, high-resolution images of the disks of NGC 4038/4039 obtained with the Wide Field Camera of the Hubble Space Telescope (HST) are presented. NGC 4038/4039, nicknamed The Antennae, is a prototypical example of a pair of colliding galaxies believed to be at an early stage of a merger. Down to the limiting magnitude of V approximately 23 mag, the HST images reveal a population of over 700 blue pointlike objects within the disks. The mean absolute magnitude of these objects is M(sub V) = -11 mag, with the brightest objects reaching M(sub V) approximately -15. Their mean apparent color indices ar U - V = -0.7 mag and V - 1 = 0.8 mag on the Johnson UVI passband system, while their mean indices corrected for internal reddening are (u - v)(sub 0) = -1.0 mag and (V - I(sub 0) = 0.5. Their mean effective radius, determined from slightly resolved images, is 18 pc (for H(sub 0) = 50 km/s /Mpc). Based on their luminosities and resolution, most of these objects cannot be individual stars, but are likely young compact star clusters. The brighter ones are similar to the objects found in NGC 1275 and NGC 7252, which appear to be young globular clusters formed during recent galazy mergers. Based on their U - V and V - I colors, the brightest, bluest clusters of NGC 4038/4039 appear to be less than 10 Myr old. Most of these bright clusters are relatively tightly clustered themselves, with typically a dozen individual clusters belonging to a complex identified as a giant H II region from ground-based observations. The cluster luminosity function (LF) is approximately a power law, phi(L)dL proportional to L(exp -1.78+/-0.05)dL, with no hint of a turnover at fainter magnitudes. This power-law shape agrees with the LF of Magellanic Cloud clusters and Galactic open clusters, but differs from the LF of old globular cluster systems that is typically Gaussian with a Full Width at Half Maximum (FWHM) of approximately 3 mag. Besides the blue clusters, we also find about a dozen extremely red objects with V - I greater than 3.0. The highest number density of these red objects is found in the SE quadrant, where star formation appears to be most recent. We propose that these objects may be very young star clusters still embedded in their placental dust cocoons.

Whitmore, Bradley C.↗
DOE OSTI2024

Weak entanglement approximation for nuclear structure

The interacting shell model, a configuration-interaction method, is a venerable approach for low-lying nuclear structure calculations, but it is hampered by the exponential growth of its basis dimension as one increases the single-particle space and/or the number of active particles. Recent, quantum-information-inspired work has demonstrated that the proton and neutron sectors of a nuclear wave function are weakly entangled. Furthermore, the entanglement is smaller for nuclides away from N = Z, such as heavy, neutron-rich nuclides. Here, in this study, we implement a weak entanglement approximation to bipartite configuration-interaction wave functions, approximating low-lying levels by coupling a relatively small number of many-proton and many-neutron states. This truncation scheme, which we present in the context of past approaches, reduces the basis dimension by many orders of magnitude while preserving essential features of nuclear spectra.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1987

An adaptive pseudo-spectral method for reaction diffusion problems

The spectral interpolation error was considered for both the Chebyshev pseudo-spectral and Galerkin approximations. A family of functionals I sub r (u), with the property that the maximum norm of the error is bounded by I sub r (u)/J sub r, where r is an integer and J is the degree of the polynomial approximation, was developed. These functionals are used in the adaptive procedure whereby the problem is dynamically transformed to minimize I sub r (u). The number of collocation points is then chosen to maintain a prescribed error bound. The method is illustrated by various examples from combustion problems in one and two dimensions.

Bayliss, A.↗
NASA NTRS1989

An adaptive pseudo-spectral method for reaction diffusion problems

The spectral interpolation error was considered for both the Chebyshev pseudo-spectral and Galerkin approximations. A family of functionals I sub r (u), with the property that the maximum norm of the error is bounded by I sub r (u)/J sub r, where r is an integer and J is the degree of the polynomial approximation, was developed. These functionals are used in the adaptive procedure whereby the problem is dynamically transformed to minimize I sub r (u). The number of collocation points is then chosen to maintain a prescribed error bound. The method is illustrated by various examples from combustion problems in one and two dimensions.

Bayliss, A.↗
DOE OSTI2025

Closed-Form Approximation of the Total Variation Proximal Operator

Total variation (TV) is a widely used function for regularizing imaging inverse problems that is particularly appropriate for images whose underlying structure is piecewise constant. TV regularized optimization problems are typically solved using proximal methods, but the way in which they are applied is constrained by the absence of a closed-form expression for the proximal operator of the TV function. A closed-form approximation of the TV proximal operator has previously been proposed, but its accuracy was not theoretically explored in detail. Here, we address this gap by making several new theoretical contributions, proving that the approximation leads to a proximal operator of some convex function, it is equivalent to a gradient descent step on a smoothed version of TV, and that its error can be fully characterized and controlled with its scaling parameter. We experimentally validate our theoretical results on image denoising and sparse-view computed tomography (CT) image reconstruction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Optimization of the Reax force field for the lithium–oxygen system using a high fidelity charge model

Studies using molecular dynamics (MD) have long struggled to simulate the failure modes of materials, predicting unrealistically high ductility and failing to capture brittle fracture. The primary cause of this shortcoming is an inadequate description of bond breaking. While reactive force fields such as ReaxFF show improvements compared to traditional force fields, the charge models used yield unphysical partial charges, especially during dissociation of ionic bonds. This flaw may be remedied by using the atom-condensed Kohn–Sham density functional theory (DFT) approximated to a second order (ACKS2) charge model for determining partial charges. In this work, we present a new ACKS2-enabled Reax force field for fracture simulations of lithium oxide systems, which was obtained by training against an extensive set of DFT, multireference configuration interaction (MRCI), and MRCI+Q reference data using genetic optimization techniques. This new force field significantly improves the bond breaking behavior, but still cannot fully capture the brittle fracture in MD simulations, suggesting more research is needed to improve simulation of brittle fracture.

36 MATERIALS SCIENCE↗
NASA NTRS1983

Spline Classification Methods

The use of spline functions in the development of classification algorithms is discussed. A method is formulated for producing spline approximations to univariate density functions when each density function is described by a histogram of measurements. The resulting approximations are then incorporated into a Bayesian classification procedure for which the probability of misclassification can be readily computed. Some preliminary numerical results are presented to illustrate the method.

Guseman, L. F., Jr.↗
NASA NTRS2006

Initial On-Orbit Spatial Resolution Characterization of OrbView-3 Panchromatic Images

Characterization was conducted under the Memorandum of Understanding among Orbital Sciences Corp., ORBIMAGE, Inc., and NASA Applied Sciences Directorate. Acquired five OrbView-3 panchromatic images of the permanent Stennis Space Center edge targets painted on a concrete surface. Each image is available at two processing levels: Georaw and Basic. Georaw is an intermediate image in which individual pixels are aligned by a nominal shift in the along-scan direction to adjust for the staggered layout of the panchromatic detectors along the focal plane array. Georaw images are engineering data and are not delivered to customers. The Basic product includes a cubic interpolation to align the pixels better along the focal plane and to correct for sensor artifacts, such as smile and attitude smoothing. This product retains satellite geometry - no rectification is performed. Processing of the characterized images did not include image sharpening, which is applied by default to OrbView-3 image products delivered by ORBIMAGE to customers. Edge responses were extracted from images of tilted edges in two directions: along-scan and cross-scan. Each edge response was approximated with a superposition of three sigmoidal functions through a nonlinear least-squares curve-fitting. Line Spread Functions (LSF) were derived by differentiation of the analytical approximation. Modulation Transfer Functions (MTF) were obtained after applying the discrete Fourier transform to the LSF.

Blonski, Slawomir↗
NASA NTRS1998

A Rigorous Framework for Optimization of Expensive Functions by Surrogates

The goal of the research reported here is to develop rigorous optimization algorithms to apply to some engineering design problems for which design application of traditional optimization approaches is not practical. This paper presents and analyzes a framework for generating a sequence of approximations to the objective function and managing the use of these approximations as surrogates for optimization. The result is to obtain convergence to a minimizer of an expensive objective function subject to simple constraints. The approach is widely applicable because it does not require, or even explicitly approximate, derivatives of the objective. Numerical results are presented for a 31-variable helicopter rotor blade design example and for a standard optimization test example.

Booker, Andrew J.↗
DOE OSTI2025

Advances and Challenges of SCAN and r 2 SCAN Density Functionals in Transition‐Metal Compounds

ABSTRACT Transition‐metal compounds (TMCs) with open‐shell d ‐electrons are characterized by a complex interplay of lattice, charge, orbital, and spin degrees of freedom, giving rise to various fascinating applications. Often exhibiting exotic properties, these compounds are commonly classified as correlated systems due to strong inter‐electronic interactions called Hubbard U . This inherent complexity presents significant challenges to Kohn‐Sham density functional theory (KS‐DFT), the most widely used electronic structure method in condensed matter physics and materials science. While KS‐DFT is, in principle, exact for the ground‐state total energy, its exchange‐correlation energy must be approximated in practice. The mean‐field nature of KS implementations, combined with the limitations of current exchange‐correlation density functional approximations, has led to the perception that DFT is inadequate for correlated systems, particularly TMCs. Consequently, a common workaround involves augmenting DFT with an on‐site Hubbard‐like U correction. In recent years, the strongly constrained and appropriately normed (SCAN) density functional, along with its refined variant r 2 SCAN, has achieved remarkable progress in accurately describing the structural, energetic, electronic, magnetic, and vibrational properties of TMCs, challenging the traditional perception of DFT's limitations. This review explores the design principles of SCAN and r 2 SCAN, highlights their key advancements in studying TMCs, explains the mechanisms driving these improvements, and addresses the remaining challenges in this evolving field.

Zhang, Yubo [Minjiang Collaborative Center for The↗
DOE OSTI2023

A unified and constructive framework for the universality of neural networks

Abstract One of the reasons why many neural networks are capable of replicating complicated tasks or functions is their universal approximation property. Though the past few decades have seen tremendous advances in theories of neural networks, a single constructive and elementary framework for neural network universality remains unavailable. This paper is an effort to provide a unified and constructive framework for the universality of a large class of activation functions including most of the existing ones. At the heart of the framework is the concept of neural network approximate identity (nAI). The main result is as follows: any nAI activation function is universal in the space of continuous functions on compacta. It turns out that most of the existing activation functions are nAI, and thus universal. The framework induces several advantages over the contemporary counterparts. First, it is constructive with elementary means from functional analysis, probability theory, and numerical analysis. Second, it is one of the first unified and constructive attempts that is valid for most of the existing activation functions. Third, it provides new proofs for most activation functions. Fourth, for a given activation and error tolerance, the framework provides precisely the architecture of the corresponding one-hidden neural network with a predetermined number of neurons and the values of weights/biases. Fifth, the framework allows us to abstractly present the first universal approximation with a favorable non-asymptotic rate. Sixth, our framework also provides insights into the developments, and hence providing constructive derivations, of some of the existing approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

MCML: Combining physical constraints with experimental data for a multi-purpose meta-generalized gradient approximation

The predictive power of density functional theory for materials properties can be improved without increasing the overall computational complexity by extending the generalized gradient approximation (GGA) for electronic exchange and correlation to density functionals depending on the electronic kinetic energy density in addition to the charge density and its gradient, resulting in a meta-GGA. Here, we propose an empirical meta-GGA model that is based both on physical constraints and on experimental and quantum chemistry reference data. Overall, the resulting optimized meta-GGA MCML yields improved surface and gas phase reaction energetics without sacrificing the accuracy of bulk property predictions of existing meta-GGA approaches.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Search for Higgs boson pair production in the γγ$b\overline{b} $ final state with 13 TeV pp collision data collected by the ATLAS experiment

A search is performed for resonant and non-resonant Higgs boson pair production in the γγ$b\overline{b} $ final state. The data set used corresponds to an integrated luminosity of 36.1 fb -1 of proton-proton collisions at a centre-of-mass energy of 13 TeV recorded by the ATLAS detector at the CERN Large Hadron Collider. No significant excess relative to the Standard Model expectation is observed. The observed limit on the non-resonant Higgs boson pair cross-section is 0.73 pb at 95% confidence level. This observed limit is equivalent to 22 times the predicted Standard Model cross-section. The Higgs boson self-coupling (κ λ = λ HHH /λ HHH SM ) is constrained at 95% confidence level to -8.2 < κ λ < 13.2. For resonant Higgs boson pair production through X→HH→γγ$b\overline{b} $, the limit is presented, using the narrow-width approximation, as a function of mX in the range 260 GeV < m X < 1000 GeV. The observed limits range from 1.1 pb to 0.12 pb over this mass range. Open image in new window

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Assessing entropy for catalytic processes at complex reactive interfaces

When chemical reactions are accelerated by a catalyst, entropy differences between reactants and their transient intermediates can be the driving force behind the promotion or inhibition of desired and parasitic chemical pathways. Understanding and controlling catalytic processes therefore requires both a fundamental and practicable understanding of entropy in addition to enthalpy. In unstructured media such as the vapor phase equilibrated with sparsely covered surfaces, entropy can be adequately accounted for by well-established approaches based on translational, rotational, and harmonic vibrational partition functions. However, these approximations become inadequate in more complex condensed phase environments, e.g., solid liquid interfaces of confined reaction spaces. In this chapter, we provide an overview of the state-of-art in the computational quantification of entropy and its known ramifications on catalysis. The fundamental roles of thermodynamics and kinetics in catalysis are covered in enough detail to appreciate and contextualize the computational methods employed to compute chemically accurate estimates of entropy. These methods are discussed in appropriate detail and range from the ubiquitous harmonic oscillator approximation where entropy unrelated to high frequency oscillations is typically underestimated, to enhanced free energy sampling with molecular dynamics where the desired accuracy must be weighed against the associated computational cost of obtaining it. The rising importance of machine learning and artificial intelligence in accelerating methodological progress in this field is touched upon, as well. Finally, applications, successes, and pitfalls of using these methods are provided to showcase past and present accomplishments while clarifying where improvements in both understanding and methodology are still needed.

Kollias, Loukas↗
DOE OSTI2021

A two-dimensional electron gas suspended above a neutralizing background

Highlights: • Two-dimensional electron gas above a neutralizing jellium background. • Analytic results for the value of the total energy. • Energy contribution originating from separation. • Added energy behaves as an effective kinetic energy term. We consider a two-dimensional electron gas system consisting of a layer of electrons suspended above its jellium neutralizing background counterpart. We investigate the energy of the system in the Hartree–Fock approximation as a function of separation distance between the layer of electrons and the background. We find that the energy of the system is influenced by the separation distance in such a way that the new energy contribution originating from such a separation behaves as an effective kinetic energy term. The increase of the overall energy with increase of the separation distance adversely affects the stability of electron liquid states at high density. The results are derived for a fully spin-polarized (spinless) system of electrons in the thermodynamic limit, but can easily be generalized to a spin unpolarized state.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Uniform-in-phase-space data selection with iterative normalizing flows

Improvements in computational and experimental capabilities are rapidly increasing the amount of scientific data that are routinely generated. In applications that are constrained by memory and computational intensity, excessively large datasets may hinder scientific discovery, making data reduction a critical component of data-driven methods. Datasets are growing in two directions: the number of data points and their dimensionality. Whereas dimension reduction typically aims at describing each data sample on lower-dimensional space, the focus here is on reducing the number of data points. A strategy is proposed to select data points such that they uniformly span the phase-space of the data. The algorithm proposed relies on estimating the probability map of the data and using it to construct an acceptance probability. An iterative method is used to accurately estimate the probability of the rare data points when only a small subset of the dataset is used to construct the probability map. Instead of binning the phase-space to estimate the probability map, its functional form is approximated with a normalizing flow. Therefore, the method naturally extends to high-dimensional datasets. The proposed framework is demonstrated as a viable pathway to enable data-efficient machine learning when abundant data are available.

97 MATHEMATICS AND COMPUTING↗