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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

An efficient computational scheme for electronic excitation spectra of molecules in solution using the symmetry-adapted cluster–configuration interaction method: The accuracy of excitation energies and intuitive charge-transfer indices

Solvent effects on electronic excitation spectra are considerable in many situations; therefore, we propose an efficient and reliable computational scheme that is based on the symmetry-adapted cluster-configuration interaction (SAC-CI) method and the polarizable continuum model (PCM) for describing electronic excitations in solution. The new scheme combines the recently proposed first-order PCM SAC-CI method with the PTE (perturbation theory at the energy level) PCM SAC scheme. This is essentially equivalent to the usual SAC and SAC-CI computations with using the PCM Hartree-Fock orbital and integrals, except for the additional correction terms that represent solute-solvent interactions. The test calculations demonstrate that the present method is a very good approximation of the more costly iterative PCM SAC-CI method for excitation energies of closed-shell molecules in their equilibrium geometry. This method provides very accurate values of electric dipole moments but is insufficient for describing the charge-transfer (CT) indices in polar solvent. The present method accurately reproduces the absorption spectra and their solvatochromism of push-pull type 2,2{sup ′}-bithiophene molecules. Significant solvent and substituent effects on these molecules are intuitively visualized using the CT indices. The present method is the simplest and theoretically consistent extension of SAC-CI method for including PCM environment, and therefore, it is useful for theoretical and computational spectroscopy.

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

Deprotonated sulfamic acid and its homodimers: Does sulfamic acid adopt zwitterion during cluster growth?

Here we present a joint experimental and computational study on the geometric and electronic structures of deprotonated sulfamic acid (SA) clusters [(SA) n –H] – (n = 1, 2) employing negative ion photoelectron spectroscopy and high-level ab initio calculations. The photoelectron spectra provide the vertical/adiabatic detachment energy (VDE/ADE) of the sulfamate anion (SM – ) H 2 N•SO 3 – at 4.85 ± 0.05 and 4.58 ± 0.08 eV, respectively, and the VDE and ADE of the SM – •SA dimer at 6.41 ± 0.05 and 5.87 ± 0.08 eV, respectively. The significantly increased electron binding energies of the dimer confirm the enhanced electronic stability upon the addition of one SA molecule. The CCSD(T)-predicted VDEs/ADEs agree excellently with the experimental data, confirming the identified structures as the most stable ones. Two types of dimer isomers possessing different hydrogen bonding (HB) motifs are identified, corresponding to SM – binding to a zwitterionic SA (SM – •SA z ) and a canonical SA (SM – •SA c ), respectively. Two N–H$\cdots$O HBs and one superior O–H$\cdots$O HB are formed in the lowest-lying SM – •SA c , while SM – •SA z has three moderate N–H$\cdots$O HBs, with the former being 4.71 kcal/mol more stable. Further theoretical analyses reveal that the binding strength advantage of SM – •SA c over SM – •SA z arises from its significant contributions of orbital interactions between fragments, illustrating that sulfamate strongly interacts with its parent SA acid and preferably chooses the canonical SA in the subsequent cluster formations. Given the prominent presence of SA, this study provides the first evidence that the canonical dimer model of sulfamic acid should exist as a superior configuration during cluster growth.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Beyond real: alternative unitary cluster Jastrow models for molecular electronic structure calculations on near-term quantum computers

Near-term quantum devices require wavefunction ansätze that are expressive while also of shallow circuit depth in order to both accurately and efficiently simulate molecular electronic structure. While the unitary coupled cluster ansatz (e.g., UCCSD) has become a standard, the high gate count associated with the implementation of this limits its feasibility on noisy intermediate-scale quantum (NISQ) hardware. k -Fold unitary cluster Jastrow (uCJ) ansätze mitigate this challenge by providing O( kN 2 ) circuit scaling and favorable linear depth circuit implementation. Previous work has focused on the real orbitalrotation (Re-uCJ) variant of uCJ, which allows an exact (Trotter-free) implementation. Here we extend and generalize the k -fold uCJ framework by introducing two new variants, Im-uCJ and g-uCJ, which incorporate imaginary and fully complex orbital rotation operators, respectively. Similar to Re-uCJ, both of the new variants achieve quadratic gate-count scaling. Our results focus on the simplest k = 1 model, and show that the uCJ models frequently maintain energy errors within chemical accuracy (∼1 kcal mol −1 ). Both g-uCJ and Im-uCJ are more expressive in terms of capturing electron correlation and are also more accurate than the earlier Re-uCJ ansatz. We further show that Im-uCJ and g-uCJ circuits can also be implemented exactly, without any Trotter decomposition. Numerical tests using k = 1 on H 2 , H 3 + , Be 2 , C 2 H 4 , C 2 H 6 and C 6 H 6 in various basis sets confirm the practical feasibility of these shallow Jastrow-based ansätze for applications on near-term quantum hardware.

Tkachenko, Nikolay V. [University of California, B↗
DOE OSTI2019

Mass functions, luminosity functions, and completeness measurements from clustering redshifts

Abstract This paper presents stellar mass functions and i-band luminosity functions for Sloan Digital Sky Survey (SDSS) galaxies with i < 21 using clustering redshifts. From these measurements, we also compute targeting completeness measurements for the Baryon Oscillation Spectroscopic Survey (BOSS). Clustering redshifts is a method of obtaining the redshift distribution of a sample of galaxies with only photometric information by measuring the angular cross-correlation with a spectroscopic sample in different redshift bins. We construct a spectroscopic sample containing data from the BOSS + eBOSS surveys, allowing us to recover redshift distributions from photometric data out to z ≃ 2.5. We produce k-corrected i-band luminosity functions and stellar mass functions by applying clustering redshifts to SDSS DR8 galaxies in small bins of colour and magnitude. There is little evolution in the mass function between 0.2 < z < 0.8, implying that the most massive galaxies form most of their mass before z = 0.8. These mass functions are used to produce stellar mass completeness estimates for the BOSS, giving a stellar mass completeness of $80{{\ \rm per\ cent}}$ above M⋆ > 1011.4 between 0.2 < z < 0.7, with completeness falling significantly at redshifts higher than 0.7, and at lower masses. Large photometric data sets will be available in the near future (DECaLS, DES, Euclid), so this and similar techniques will become increasingly useful in order to fully utilize these data.

Bates, Dominic J.↗
DOE OSTI2018

Electronic Detection of Oxygen Adsorption and Size-Specific Doping of Few-Atom Gold Clusters on Graphene

Graphene's sensitivity to adsorbed particles has attracted widespread attention because of its potential sensor applications. Size-selected few-atom clusters are promising candidates as adparticles to graphene. Due to their small size, physicochemical properties are dominated by quantum size effects. In particular, few-atom gold clusters demonstrate a significant catalytic activity in various oxidation reactions. In this joint experimental and computational work, size-selected gold clusters with 3 and 6 atoms adsorbed on graphene field-effect transistors and their interaction with molecular oxygen are investigated. While oxygen adsorbs at both cluster sizes, there is a pronounced cluster size dependence in the corresponding doping, as demonstrated via first-principle calculations and electronic transport measurements. Furthermore, the doping of gold cluster decorated graphene changes sign from n- to p-doping upon oxygen adsorption, directly evidencing electron transfer to the oxygen molecules and hence their activation. These observations pinpoint graphene as a valuable platform to investigate and exploit size-dependent cluster properties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

SMALE: Enhancing Scalability of Machine Learning Algorithms on Extreme-Scale Computing Platforms

Deployment and execution of machine learning tasks on extreme-scale computing platforms face several significant technical challenges: 1) High computing cost incurred by dense networks – The computing workload of deep networks with densely-connected topology increases rapidly with the network size, imposing a non-scalable computing model of extreme-scale computing platforms; 2) Non-optimized workload distribution – Many advanced deep learning algorithms, e.g., sparsification and irregular net-work topology, produce very unbalanced workload distribution on extreme-scale computing platforms. The computation efficiency is greatly hindered by the incurred data and computation redundancies as well as long tails of the node with extensive workload; 3) Constraints in data movement and I/O bottle-neck – Inter-node data movement in extreme-scale computing platforms are associated with high energy and latency costs, and subject to the constraints of I/O bandwidth; and 4) Generalization of algorithm realization and acceleration on computing platforms – The large varieties of machine learning algorithms and structures of extreme-scale computing platforms make the derivation of a generalized algorithm realization and acceleration method very challenging, which, however, is the requirement by domain scientists and interested users. We call the above challenges Smale’s Problems in Machine Learning and Understanding for High-Performance Computing Scientific Discovery. The objective of our three-year research project is to develop a holistic innovation set at structure, assembly, and acceleration layers of machine learning algorithms to address the above challenges in algorithm deployment and execution. Three tasks are particularly performed, including: At the algorithm structure level, we investigate the techniques that can structurally sparsify on the topology of deep networks for computing workload reduction. We also study clustering and pruning techniques that can optimize the workload distributions over the extreme-scale computing platforms; At the algorithm assembly level, we derive a unified learning framework for unsupervised transfer learning and dynamic growing capabilities. Novel training methods are also exploited to enhance the training efficiency of the proposed framework; At the algorithm acceleration level, we will develop a series of techniques that can accelerate the computation of sparse matrix operations, which are one of the core executions in deep learning and optimize memory access of the concerned platforms. Our proposed techniques attack the fundamental problems in machine learning algorithms running on extreme-scale computing platforms by vertically integrating the solutions at three closely entangled layers, paving the long-term scaling path of machine learning applications under DOE context. Three tasks corresponding to the above respective research orientations are performed during the three-year project period with our collaborators at ORNL. The outcome of the proposed project is anticipated to form a holistic solution set of novel algorithms and network topologies, efficient training techniques, and fast acceleration methods to promote the computing scalability of the machine learning applications of particular interest to DOE.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Thermodynamics of Metal Nanoparticles: Energies and Enthalpies of Formation of Magnesium Clusters and Nanoparticles as Large as 1.3 nm

The major obstacle that prevents reliable electronic structure studies of nanoparticles is the rapid increasing computational cost for benchmark calculations using coupled-cluster methods. We show that a CCSD(T) scheme with an MP2/CBS correction can reproduce accurate cohesive energies for magnesium clusters, and this scheme is much less computationally demanding than other reliable methods, so it is applied to Mg n with n up to 19, which enters the realm of nanoparticles. (The diameters of all Mg clusters n ≥ 11 are >1 nm). With the extended benchmark data, we validate exchange–correlation functionals into the nanoparticle regime and use the two best-validated functionals to calculate the enthalpy of formation of Mg 28 , with a diameter of 1.30 nm. We also calculated the enthalpy of formation of all Mg clusters and nanoparticles from Mg 2 to Mg 19 . This kind of reliable thermodynamic data on size-selected metal nanoparticles has been hard to come by, either experimentally or theoretically, but it is badly needed to support applications in catalysis, electrochemistry, and other technologies.

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

Dynamic Ride-Matching for Large-Scale Transportation Systems

Efficient dynamic ride-matching (DRM) in large-scale transportation systems is a key driver in transport simulations to yield answers to challenging problems. Although the DRM problem is simple to solve, it quickly becomes a computationally challenging problem in large-scale transportation system simulations. Therefore, this study thoroughly examines the DRM problem dynamics and proposes an optimization-based solution framework to solve the problem efficiently. To benefit from parallel computing and reduce computational times, the problem’s network is divided into clusters utilizing a commonly used unsupervised machine learning algorithm along with a linear programming model. Then, these sub-problems are solved using another linear program to finalize the ride-matching. At the clustering level, the framework allows users adjusting cluster sizes to balance the trade-off between the computational time savings and the solution quality deviation. A case study in the Chicago Metropolitan Area, U.S., illustrates that the framework can reduce the average computational time by 58% at the cost of increasing the average pick up time by 26% compared with a system optimum, that is, non-clustered, approach. Another case study in a relatively small city, Bloomington, Illinois, U.S., shows that the framework provides quite similar results to the system-optimum approach in approximately 62% less computational time.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Photodissociation and Theory to Investigate Uranium Oxide Cluster Cations

Uranium oxide cluster cations of the form U n O m + are produced by laser vaporization of a depleted uranium rod in a pulsed supersonic expansion. Ions are mass-analyzed and mass-selected with a time-of-flight spectrometer and studied with UV laser multiphoton dissociation. Cations of the stoichiometry UO 2 (UO 3 ) n + were observed as photofragments from all photodissociated cluster cations. (UO 3 ) n + clusters were also observed to result from dissociation of larger (UO 3 ) n + clusters, with UO 3 neutral as a common leaving group. Electronic structure calculations were used to investigate the stability of the prominent uranium oxide cluster cations using density functional theory (DFT) with the hybrid B3LYP exchange-correlation functional and at the CCSD(T) level and cc-pVnZ-PP basis sets (n = D,T), including diffuse orbitals as computational expense and availability permitted. Clustering energies, relative energies and dissociation energies of the cations are reported. The lowest energy neutral (UO 3 ) n clusters up to n = 3 are rings, n = 4 and 5 are chains with very low energy rings, and n = 6 is 3D. Finally, the lowest energy structures for UO 2 (UO 3 ) n + are composed of uranyl-like UO 2 + units bound by bridging oxygens to other UO 2 2+ units for n = 2 and 3, and for n = 4 a more complex 3D structure is predicted.

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

Cosmological constraints from galaxy clusters in the 2500 square-degree SPT-SZ survey

We present cosmological parameter constraints obtained from galaxy clusters identified by their Sunyaev–Zel’dovich effect signature in the 2500 square-degree South Pole Telescope Sunyaev Zel’dovich (SPT-SZ) survey. We consider the 377 cluster candidates identified at z>0.25 with a detection significance greater than five, corresponding to the 95% purity threshold for the survey. We compute constraints on cosmological models using the measured cluster abundance as a function of mass and redshift. We include additional constraints from multi-wavelength observations, including Chandra X-ray data for 82 clusters and a weak lensing-based prior on the normalization of the mass-observable scaling relations. Assuming a spatially flat ΛCDM cosmology, we combine the cluster data with a prior on H {sub 0} and find σ{sub 8}=0.784±0.039 and Ω{sub m}=0.289±0.042, with the parameter combination σ{sub 8}(Ω{sub m}/0.27){sup 0.3}=0.797±0.031. These results are in good agreement with constraints from the cosmic microwave background (CMB) from SPT, WMAP, and Planck, as well as with constraints from other cluster data sets. We also consider several extensions to ΛCDM, including models in which the equation of state of dark energy w, the species-summed neutrino mass, and/or the effective number of relativistic species (N{sub eff}) are free parameters. When combined with constraints from the Planck CMB, H {sub 0}, baryon acoustic oscillation, and SNe, adding the SPT cluster data improves the w constraint by 14%, to w=−1.023±0.042.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Reaction of CO 2 with Groups 4 and 6 Transition Metal Oxide Clusters

Density functional theory (DFT) and coupled cluster theory (CCSD(T)) were used to research the addition of CO 2 to group 4 (MO 2 ) n and group 6 (MO 3 ) n (n = 1, 2, 3) nanoclusters. The structures and energetics arising from Lewis acid–base addition (physisorption) and formation of CO 3 2– (chemisorption) of CO 2 to these clusters were predicted. Physisorption and chemisorption of CO 2 are predicted to be thermodynamically allowed for group 4 (MO 2 ) n clusters, with chemisorption being more favored energetically. Correlations of the ligand binding energies (LBEs) for the group 4 clusters are made with the fluoride affinities and M–O and M=O bond strengths of the clusters. The LBEs for chemisorption on the Zr and Ti clusters are consistent with published experimental and computational studies of bulk solids. Physisorption LBEs for the Ti and Zr clusters are more exothermic than the bulk values, as the cluster models allow for better relaxation at the metal sites. Chemisorption is not predicted to occur with group 6 (MO 3 ) n clusters, as the larger chemisorbed structures were all found to be metastable. Furthermore, CO 2 is predicted to weakly physisorb to (WO 3 ) n with physisorption correlating with the Lewis acidity of the metal site.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

The thermodynamics of proton hydration and the electrochemical surface potential of water

The free energy change for transferring a single ion across the water liquid/vapor interface includes an electrochemical surface potential contribution. Since this potential is not directly accessible to thermodynamic measurement, several extra-thermodynamic approaches have been employed to infer its sign and magnitude, with a resulting wide spread of values. Here, we examine further the thermodynamics of proton hydration and the electrochemical surface potential of water along three directions: (1) a basic relation of interfacial electrostatics and experimental results on ion distributions near a water/organic interface are employed to infer a solvent contribution to the electrochemical surface potential, (2) a re-analysis is performed of the existing bulk and cluster ion hydration data, and (3) extensive computational modeling is conducted to examine the size dependence of hydration enthalpy differences for the NaF ion pair between the small cluster and the converged bulk limits. The computational studies include classical polarizable models and high-level quantum chemical methods. The new theoretical analysis of existing experimental data and the combined classical/quantum modeling lead to results consistent with our previously derived proton hydration quantities.

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

Copacabana: a probabilistic membership assignment method for galaxy clusters

Cosmological analyses using galaxy clusters in optical/near-infrared photometric surveys require robust characterization of their galaxy content. Precisely determining which galaxies belong to a cluster is crucial. In this paper, we present the COlor Probabilistic Assignment of Clusters And BAyesiaN Analysis (Copacabana) algorithm. Copacabana computes membership probabilities for all galaxies within an aperture centred on the cluster using photometric redshifts, colours, and projected radial probability density functions. We use simulations to validate Copacabana and we show that it achieves up to 89 per cent membership accuracy with a mild dependence on photometric redshift uncertainties and choice of aperture size. We find that the precision of the photometric redshifts has the largest impact on the determination of the membership probabilities followed by the choice of the cluster aperture size. We also quantify how much these uncertainties in the membership probabilities affect the stellar mass–cluster mass scaling relation, a relation that directly impacts cosmology. Using the sum of the stellar masses weighted by membership probabilities (⁠μ * ⁠) as the observable, we find that Copacabana can reach an accuracy of 0.06 dex in the measurement of the scaling relation at low redshift for a Legacy Survey of Space and Time type survey. These results indicate the potential of Copacabana and μ * to be used in cosmological analyses of optically selected clusters in the future.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Atomic operations in a large scale distributed computing network

Techniques for executing an atomic command in a distributed computing network are provided. A core cluster, including a plurality of processing cores that do not natively issue atomic commands to the distributed computing network, is coupled to a translation unit. To issue an atomic command, a core requests a location in the translation unit to write an opcode and operands for the atomic command. The translation unit identifies a location (a “window”) that is not in use by another atomic command and indicates the location to the processing core. The processing core writes the opcode and operands into the window and indicates to the translation unit that the atomic command is ready. The translation generates an atomic command and issues the command to the distributed computing network for execution. After execution, the distributed computing network provides a response to the translation unit, which provides that response to the core.

Lackey, Jr., Stanley A.↗
DOE OSTI2015

Cosmic strings and the origin of globular clusters

We hypothesize that cosmic string loops are the seeds about which globular clusters accrete. Fixing the cosmic string tension by demanding that the peak in the distribution of masses of objects accreting onto string loops agrees with the peak in the observed mass distribution of globular clusters in our Milky Way galaxy, we then compute the expected number density and mass function of globular clusters, and compare with observations. Our hypothesis naturally explains why globular clusters are the oldest and most dense objects in a galaxy, and why they are found in the halo of the galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Solving Coupled Cluster Equations by the Newton Krylov Method

We describe using the Newton Krylov method to solve the coupled cluster equation. The method uses a Krylov iterative method to compute the Newton correction to the approximate coupled cluster amplitude. The multiplication of the Jacobian with a vector, which is required in each step of a Krylov iterative method such as the Generalized Minimum Residual (GMRES) method, is carried out through a finite difference approximation, and requires an additional residual evaluation. The overall cost of the method is determined by the sum of the inner Krylov and outer Newton iterations. We discuss the termination criterion used for the inner iteration and show how to apply pre-conditioners to accelerate convergence. We will also examine the use of regularization technique to improve the stability of convergence and compare the method with the widely used direct inversion of iterative subspace (DIIS) methods through numerical examples.

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

Criticality Evaluation of Low Density Product Oxides for Air Transport for the ES-3100 Package

This document presents the criticality safety evaluation of the Y-12 National Security Complex (Y-12) Model ES-3100 package for the addition of lower bulk density limits for HEU product oxides transported by air. The current authorized oxide density range for air transport is 2.0 to 6.54 g/cm 3 . The analysis in this report looks at lower densities, specifically in the range of 1.0 to 2.0 g/cm 3 . For reasons presented in this report, it was decided to present the entire range of oxide densities (1.0 to 6.54 g/cm 3 ) as a series of sub-ranges. The lowest density in each range was evaluated. Thus calculations were performed for the following oxide densities: 5.0 g/cm 3 , 4.0 g/cm 3 , 3.0 g/cm 3 , 2.0 g/cm 3 , 1.5 g/cm 3 and 1.0 g/cm 3 . Calculations presented herein were performed using a third generation computing platform; the Microway Parallel Linux Cluster ENGHPC workstation using the Monte Carlo Criticality Safety Analysis Sequence (CSAS5) criticality code sequence and 238-group ENDF/B-VII cross-section library of Standardized Computer Analysis for Licensing Evaluation (SCALE) 6.1.3 (ORNL/TM-2005/39, SCALE: A Comprehensive Modeling and Simulation Suite for Nuclear Safety Analysis and Design).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network

Abstract Single-cell RNA sequencing (scRNA-seq) permits researchers to study the complex mechanisms of cell heterogeneity and diversity. Unsupervised clustering is of central importance for the analysis of the scRNA-seq data, as it can be used to identify putative cell types. However, due to noise impacts, high dimensionality and pervasive dropout events, clustering analysis of scRNA-seq data remains a computational challenge. Here, we propose a new deep structural clustering method for scRNA-seq data, named scDSC, which integrate the structural information into deep clustering of single cells. The proposed scDSC consists of a Zero-Inflated Negative Binomial (ZINB) model-based autoencoder, a graph neural network (GNN) module and a mutual-supervised module. To learn the data representation from the sparse and zero-inflated scRNA-seq data, we add a ZINB model to the basic autoencoder. The GNN module is introduced to capture the structural information among cells. By joining the ZINB-based autoencoder with the GNN module, the model transfers the data representation learned by autoencoder to the corresponding GNN layer. Furthermore, we adopt a mutual supervised strategy to unify these two different deep neural architectures and to guide the clustering task. Extensive experimental results on six real scRNA-seq datasets demonstrate that scDSC outperforms state-of-the-art methods in terms of clustering accuracy and scalability. Our method scDSC is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/DHUDBlab/scDSC.

Gan, Yanglan↗