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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Uncertainty in the visibility mask of a survey and its effects on the clustering of biased tracers

The forecasted accuracy of upcoming surveys of large-scale structure cannot be achieved without a proper quantification of the error induced by foreground removal (or other systematics like 0-point photometry offset). Because these errors are highly correlated on the sky, their influence is expected to be especially important at very large scales, at and beyond the first Baryonic Acoustic Oscillation (BAO). In this work we quantify how the uncertainty in the visibility mask of a survey, that gives the survey depth in a specific sky area, influences the measured power spectrum of a sample of tracers of the density field and its covariance matrix. We start from a very large set of 10,000 catalogs of dark matter (DM) halos in periodic cosmological boxes, produced with the PINOCCHIO approximate method. To make an analytic approach feasible, we assume luminosity-independent halo bias and an idealized geometry for the visibility mask, that is constant in square tiles of physical length l ; this should be interpreted as the projection, at the observation redshift, of the angular correlation scale of the foreground residuals. We find that the power spectrum of these biased tracers can be expressed as the sum of a cosmological term, a mask term and a term involving their convolution. The mask and convolution terms scale like P ∝ l {sup 2}σ {sub A} {sup 2}, where σ {sub A} {sup 2} is the variance of the uncertainty on the visibility mask. With l = 30−100 Mpc/ h and σ {sub A} = 5−20%, the mask term can be significant at k ∼ 0.01−0.1 h /Mpc, and the convolution term can amount to ∼ 1−10% of the total. The influence of mask uncertainty on power spectrum covariance is more complicated: the coupling of the convolution term with the other two gives rise to several mixed terms, that we quantify by difference using the mock catalogs. These are found to be of the same order of the mask covariance, and to introduce non-diagonal terms at large scales. As a consequence, the power spectrum covariance matrix cannot be expressed as the sum of a cosmological and of a mask term. More realistic settings (realistic foregrounds, luminosity-dependent bias) make the analytical approach not feasible, and the problem requires on the one hand usage of extended sets of mock catalogs, on the other hand detailed knowledge of the correlations among errors in the visibility masks. Our results lie down the theoretical bases to quantify the impact that uncertainties in the mask calibration have on the derivation of cosmological constraints from large spectroscopic surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2000

Equivalent Skin Analysis of Wing Structures Using Neural Networks

An efficient method of modeling trapezoidal built-up wing structures is developed by coupling. in an indirect way, an Equivalent Plate Analysis (EPA) with Neural Networks (NN). Being assumed to behave like a Mindlin-plate, the wing is solved using the Ritz method with Legendre polynomials employed as the trial functions. This analysis method can be made more efficient by avoiding most of the computational effort spent on calculating contributions to the stiffness and mass matrices from each spar and rib. This is accomplished by replacing the wing inner-structure with an "equivalent" material that combines to the skin and whose properties are simulated by neural networks. The constitutive matrix, which relates the stress vector to the strain vector, and the density of the equivalent material are obtained by enforcing mass and stiffness matrix equities with rec,ard to the EPA in a least-square sense. Neural networks for the material properties are trained in terms of the design variables of the wing structure. Examples show that the present method, which can be called an Equivalent Skin Analysis (ESA) of the wing structure, is more efficient than the EPA and still fairly good results can be obtained. The present ESA is very promising to be used at the early stages of wing structure design.

Liu, Youhua↗
NASA NTRS1990

Gaussian windows: A tool for exploring multivariate data

Presented here is a method for interactively exploring a large set of quantitative multivariate data, in order to estimate the shape of the underlying density function. It is assumed that the density function is more or less smooth, but no other specific assumptions are made concerning its structure. The local structure of the data in a given region may be examined by viewing the data through a Gaussian window, whose location and shape are chosen by the user. A Gaussian window is defined by giving each data point a weight based on a multivariate Gaussian function. The weighted sample mean and sample covariance matrix are then computed, using the weights attached to the data points. These quantities are used to compute an estimate of the shape of the density function in the window region. The local structure of the data is described by a method similar to the method of principal components. By taking many such local views of the data, we can form an idea of the structure of the data set. The method is applicable in any number of dimensions. The method can be used to find and describe simple structural features such as peaks, valleys, and saddle points in the density function, and also extended structures in higher dimensions. With some practice, we can apply our geometrical intuition to these structural features in any number of dimensions, so that we can think about and describe the structure of the data. Since the computations involved are relatively simple, the method can easily be implemented on a small computer.

Jaeckel, Louis A.↗
DOE OSTI2025

Exploiting a Shortcoming of Coupled-Cluster Theory: The Extent of Non-Hermiticity as a Diagnostic Indicator of Computational Accuracy

The fundamental non-Hermitian nature of the forms of the coupled-cluster (CC) theory widely used in quantum chemistry has usually been viewed as a negative, but the present paper shows how this can be used to an advantage. Specifically, the non-symmetric nature of the reduced one-particle density matrix (in the molecular orbital basis) is advocated as a diagnostic indicator of computational quality. In the limit of the full coupled-cluster theory [which is equivalent to full configuration interaction (FCI)], the electronic wave function and correlation energy are exact within a given one-particle basis set, and the symmetric character of the exact density matrix is recovered. The extent of the density matrix asymmetry is shown to provide a measure of “how difficult the problem is” (like the well-known T 1 diagnostic), but its variation with the level of theory also gives information about “how well this particular method works”, irrespective of the difficulty of the problem at hand. The proposed diagnostic is described and applied to a select group of small molecules, and an example of its overall utility for the practicing quantum chemist is illustrated through its application to the beryllium dimer (Be 2 ). Future application of this idea to excited states, open-shell systems, and symmetry-breaking problems and an extension of the method to the two-particle density are then proposed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

A new time dependent density functional algorithm for large systems and plasmons in metal clusters

A new algorithm to solve the Time Dependent Density Functional Theory (TDDFT) equations in the space of the density fitting auxiliary basis set has been developed and implemented. The method extracts the spectrum from the imaginary part of the polarizability at any given photon energy, avoiding the bottleneck of Davidson diagonalization. The original idea which made the present scheme very efficient consists in the simplification of the double sum over occupied-virtual pairs in the definition of the dielectric susceptibility, allowing an easy calculation of such matrix as a linear combination of constant matrices with photon energy dependent coefficients. The method has been applied to very different systems in nature and size (from H{sub 2} to [Au{sub 147}]{sup −}). In all cases, the maximum deviations found for the excitation energies with respect to the Amsterdam density functional code are below 0.2 eV. The new algorithm has the merit not only to calculate the spectrum at whichever photon energy but also to allow a deep analysis of the results, in terms of transition contribution maps, Jacob plasmon scaling factor, and induced density analysis, which have been all implemented.

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

Investigation of 3D printed lightweight hybrid composites via theoretical modeling and machine learning

Hybrid composites combine two or more different fillers to achieve multifunctional or advanced material properties, such as lightweight and enhanced mechanical properties. The properties of the composites significantly depend on their microstructures, which can be tailored via advanced 3D printing processes. Understanding the process-structure-property relationships is critical to enable the design and engineering of novel hybrid composites for applications in aerospace, automotive, and protective coatings. Here, for this work, we develop 3D printable and lightweight hybrid composites and leverage the conventional design of experiments, a theoretical hybrid model, and an image-driven machine learning (ML) method to investigate their mechanical behaviors. The hybrid composites are formulated with elastomer matrix, microfillers, and thin-shell particles, enabling a significant degree of design freedom of microstructures with densities and mechanical properties varying up to 70% and 91%, respectively. Our statistical analysis indicates that the 3D printing path direction and the microfibers fraction are dominating process parameters with contribution percentages of 45.3% and 57.7% on the specific stiffness and strength, respectively. A hybrid mechanics model is developed based on a simple Weibull distribution function and classical single-filler models to effectively capture the variations in mechanical properties, however, it overestimates the values due to its statistical constraints and idealization of experimental uncertainty. The image-driven ML model leverages the microscale images directly without losing the structural details, shows more accurate predictions with experimental data, and has 48.6% lower root mean square error than the theoretical model.

3D printing↗
DOE OSTI2021

Bond order predictions using deep neural networks

Machine learning is an extremely powerful tool for the modern theoretical chemist since it provides a method for bypassing costly algorithms for solving the Schrödinger equation. Already, it has proven able to infer molecular and atomic properties such as charges, enthalpies, dipoles, excited state energies, and others. Most of these machine learning algorithms proceed by inferring properties of individual atoms, even breaking down total molecular energy into individual atomic contributions. In this paper, we introduce a modified version of the Hierarchically Interacting Particle Neural Network (HIP-NN) capable of making predictions on the bonds between atoms rather than on the atoms themselves. We train the modified HIP-NN to infer bond orders for a large number of small organic molecules as computed via the Natural Bond Orbital package. We demonstrate that the trained model is extensible to molecules much larger than those in the training set by studying its performance on the COMP6 dataset. This method has applications in cheminformatics and force field parameterization and opens a promising future for machine learning models to predict other quantities that are defined between atoms such as density matrix elements, Hamiltonian parameters, and molecular reactivities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Unraveling multi-spin effects in rotational resonance nuclear magnetic resonance using effective reduced density matrix theory

A quantum-mechanical model integrating the concepts of reduced density matrix and effective Hamiltonians is proposed to explain the multi-spin effects observed in rotational resonance (R{sup 2}) nuclear magnetic resonance (NMR) experiments. Employing this approach, the spin system of interest is described in a reduced subspace inclusive of its coupling to the surroundings. Through suitable model systems, the utility of our theory is demonstrated and verified with simulations emerging from both analytic and numerical methods. The analytic results presented in this article provide an accurate description/interpretation of R{sup 2} experimental results and could serve as a test-bed for distinguishing coherent/incoherent effects in solid-state NMR.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Orbital-selective Mott phases of a one-dimensional three-orbital Hubbard model studied using computational techniques

A recently introduced one-dimensional three-orbital Hubbard model displays orbital-selective Mott phases with exotic spin arrangements such as spin block states [J. Rincón et al., Phys. Rev. Lett. 112, 106405 (2014)]. In this paper we show that the constrained-path quantum Monte Carlo (CPQMC) technique can accurately reproduce the phase diagram of this multiorbital one-dimensional model, paving the way to future CPQMC studies in systems with more challenging geometries, such as ladders and planes. The success of this approach relies on using the Hartree-Fock technique to prepare the trial states needed in CPQMC. In addition, we study a simplified version of the model where the pair-hopping term is neglected and the Hund coupling is restricted to its Ising component. The corresponding phase diagrams are shown to be only mildly affected by the absence of these technically difficult-to-implement terms. This is confirmed by additional density matrix renormalization group and determinant quantum Monte Carlo calculations carried out for the same simplified model, with the latter displaying only mild fermion sign problems. Lastly, we conclude that these methods are able to capture quantitatively the rich physics of the several orbital-selective Mott phases (OSMP) displayed by this model, thus enabling computational studies of the OSMP regime in higher dimensions, beyond static or dynamic mean-field approximations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Ground-state-based model reduction with unitary circuits

Here, we present a method to numerically obtain low-energy effective models based on a unitary transformation of the ground state. The algorithm finds a unitary circuit that transforms the ground state of the original model to a projected wavefunction with only the low-energy degrees of freedom. The effective model can then be derived using the unitary transformation encoded in the circuit. We test our method on the one-dimensional and two-dimensional square-lattice Hubbard model at half-filling, and obtain more accurate effective spin models than the standard perturbative approach.

Hubbard model↗
DOE OSTI2021

General synthesis of single-atom catalysts with high metal loading using graphene quantum dots

Transition-metal single-atom catalysts present extraordinary activity per metal atomic site, but suffer from low metal-atom densities (typically less than 5 wt% or 1 at.%), which limits their overall catalytic performance. Here we report a general method for the synthesis of single-atom catalysts with high transition-metal-atom loadings of up to 40 wt% or 3.8 at.%, representing several-fold improvements compared to benchmarks in the literature. Graphene quantum dots, later interweaved into a carbon matrix, were used as a support, providing numerous anchoring sites and thus facilitating the generation of high densities of transition-metal atoms with sufficient spacing between the metal atoms to avoid aggregation. A significant increase in activity in electrochemical CO 2 reduction (used as a representative reaction) was demonstrated on a Ni single-atom catalyst with increased Ni loading.

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

A multisite decomposition of the tensor network path integrals

Tensor network decompositions of path integrals for simulating open quantum systems have recently been proven to be useful. However, these methods scale exponentially with the system size. This makes it challenging to simulate the non-equilibrium dynamics of extended quantum systems coupled with local dissipative environments. In this work, we extend the tensor network path integral (TNPI) framework to efficiently simulate such extended systems. The Feynman–Vernon influence functional is a popular approach used to account for the effect of environments on the dynamics of the system. In order to facilitate the incorporation of the influence functional into a multisite framework (MS-TNPI), we combine a matrix product state (MPS) decomposition of the reduced density tensor of the system along the sites with a corresponding tensor network representation of the time axis to construct an efficient 2D tensor network. The 2D MS-TNPI network, when contracted, yields the time-dependent reduced density tensor of the extended system as an MPS. The algorithm presented is independent of the system Hamiltonian. We outline an iteration scheme to take the simulation beyond the non-Markovian memory introduced by solvents. Applications to spin chains coupled to local harmonic baths are presented; we consider the Ising, XXZ, and Heisenberg models, demonstrating that the presence of local environments can often dissipate the entanglement between the sites. We discuss three factors causing the system to transition from a coherent oscillatory dynamics to a fully incoherent dynamics. The MS-TNPI method is useful for studying a variety of extended quantum systems coupled with solvents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Phase diagram of the anisotropic triangular lattice Hubbard model

In a recent study [Phys. Rev. X 10, 021042 (2020)], we showed using large-scale density matrix renormalization group (DMRG) simulations on infinite cylinders that the triangular lattice Hubbard model has a chiral spin liquid phase. In this work, we introduce hopping anisotropy in the model, making one of the three distinct bonds on the lattice stronger or weaker compared with the other two. We implement the anisotropy in two inequivalent ways, one which respects the mirror symmetry of the cylinder and one which breaks this symmetry. In the full range of anisotropy, from the square lattice to weakly coupled one-dimensional chains, we find a variety of phases. Near the isotropic limit we find the three phases identified in our previous work: metal, chiral spin liquid, and 120$^\circ$ spiral order; we note that a recent paper suggests the apparently metallic phase may actually be a Luther-Emery liquid, which would also be in agreement with our results. Furthermore, when one bond is weakened by a relatively small amount, the ground state quickly becomes the square lattice N\'{e}el order. When one bond is strengthened, the story is much less clear, with the phases that we find depending on the orientation of the anisotropy and on the cylinder circumference. While our work is to our knowledge the first DMRG study of the anisotropic triangular lattice Hubbard model, the overall phase diagram we find is broadly consistent with that found previously using other methods, such as variational Monte Carlo and dynamical mean field theory.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Probing the Mechanism of Cadmium Sulfide Cluster Nucleation and Growth via Sequential Infiltration Synthesis

Few-atom metal chalcogenide clusters may be realized through the sequential infiltration of metal–organic precursors in polymer films. However, the underlying nucleation and growth mechanisms that allow for cluster synthesis with near atomic-scale precision are not fully resolved. The kinetics of the sequential infiltration synthesis (SIS) method that control the nucleation and growth mechanisms of primarily Cd 4 S 4 -core clusters within a poly­(4-vinylpyridine) (P4VP) matrix are probed with in situ UV–visible absorbance spectroscopy. Density functional theory (DFT) calculations that allow simulation of the optical properties of cluster fragments further reveal the thermodynamics that guide cluster growth within the P4VP matrix. Here, we conclude that a reactive capture mechanism for cluster nucleation and growth is dominant, although transient dimethyl cadmium adduction to the polymer backbone may contribute to cluster nucleation under shorter metal–organic purge process conditions. Grazing incidence X-ray diffraction (GI-XRD) and X-ray absorption spectroscopy (XAS) analyses further corroborate the cluster size and atom connectivity throughout the stepwise synthesis.

Jayaweera, Nuwanthaka P. [Argonne National Laborat↗
DOE OSTI2020

Spin–Orbit Matrix Elements for a Combined Spin-Flip and IP/EA approach

We introduce a practical approach for computing the Breit–Pauli spin–orbit matrix elements of multiconfigurational systems with both spin and spatial degeneracies based on our recently developed RAS-nSF-IP/EA method (Houck, S. E.; et al. J. Chem. Theory Comput. 2019, 15, 2278). The spin–orbit matrix elements over all the multiplet components are computed using a single one-particle reduced density matrix as a result of the Wigner–Eckart theorem. A mean field spin–orbit approximation was used to account for the two-electron contributions. Basis set dependence as well as the effect of including additional excitations is presented. The effect of correlating the core and semicore orbitals is also examined. Surprisingly accurate results are obtained for spin–orbit coupling constants, despite the fact that the efficient wave function approximations we research neglect the bulk of dynamical correlation.

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

Quantification of electron correlation for approximate quantum calculations

State-of-the-art many-body wave function techniques rely on heuristics to achieve high accuracy at an attainable computational cost to solve the many-body Schrödinger equation. By far, the most common property used to assess accuracy has been the total energy; however, total energies do not give a complete picture of electron correlation. In this work, we assess the von Neumann entropy of the one-particle reduced density matrix (1-RDM) to compare selected configuration interaction (CI), coupled cluster, variational Monte Carlo, and fixed-node diffusion Monte Carlo for benchmark hydrogen chains. A new algorithm, the circle reject method, is presented, which improves the efficiency of evaluating the von Neumann entropy using quantum Monte Carlo by several orders of magnitude. The von Neumann entropy of the 1-RDM and the eigenvalues of the 1-RDM are shown to distinguish between the dynamic correlation introduced by the Jastrow and the static correlation introduced by determinants with large weights, confirming some of the lore in the field concerning the difference between the selected CI and Slater–Jastrow wave functions.

Chemistry↗
DOE OSTI2017

Calibrations for charged particle tracking and measurements of w photoproduction with the GlueX detector

The GlueX experiment is a new experimental facility at Jefferson Lab in Newport News, VA. The experiment aims to map out the spectrum of hybrid mesons in the light quark sector. Measurements of the spin-density matrix elements in omega photoproduction are performed with a linear polarized photon beam on an unpolarized proton target, and presented in bins of Mandelstam t for beam energies of 8.4-9.0 GeV. The spin-density matrix elements are exclusively measured through two decays of the omega meson: omega -> pi^+ pi^- pi^0 and omega ->pi^0 gamma. A description of the experimental apparatus is presented. Several methods used in the calibration of the charged particle tracking system are described. These measurements greatly improve the world statistics in this energy range. These are the first results measured through the omega ->pi^0 gamma decay at this energy. Results are generally consistent with a theoretical model based on diffractive production with Pomeron and pseudoscalar exchange in the t-channel.

Staib, Michael↗
NASA NTRS2016

Influence of Constituents on Creep Properties of SiC/SiC Composites

SiC-SiC composites are being considered as potential candidate materials for next generation turbine components such as combustor liners, nozzle vanes and blades because of their low density, high temperature capability, and tailorable mechanical properties. These composites are essentially fabricated by infiltrating matrix into a stacked array of fibers or fiber preform by one or a combination of manufacturing methods such as, Melt Infiltration (MI) of molten silicon metal, Chemical Vapor Infiltration (CVI), Polymer Infiltration and Pyrolysis (PIP). To understand the influence of constituents, the SiC-SiC composites fabricated by MI, CVI, and PIP methods were creep tested in air between 12000 and 14500 degrees Centigrade for up to 500 hours. The failed specimens were analyzed under a scanning electron microscope to assess damage mechanisms. Also, knowing the creep deformation parameters of the fiber and the matrix under the testing conditions, the creep behavior of the composites was modeled and compared with the measured data. The implications of the results on the long term durability of these composites will be discussed.

modeling↗