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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Direct CO 2 Reduction to CO with an Fe 4 S 4 -Based Coordination Polymer

Fe 4 S 4 clusters play essential roles in nature, classically in electron transport but increasingly in newly discovered reactivity or catalysis. These roles have spurred interest in developing synthetic Fe 4 S 4 systems and while several molecular and material systems built from Fe 4 S 4 clusters have been developed, comparatively few examples of synthetic Fe 4 S 4 cluster-based catalysts exist. Herein, we present the use of an Fe 4 S 4 -based coordination polymer as a catalyst for the direct and selective electroreduction of CO 2 to CO. Computational studies suggest that the reaction proceeds through CO 2 binding to a reduced Fe 4 S 4 cluster, followed by a series of protonation, reduction, and H 2 O loss steps to yield a CO-bound cluster that can finally exchange with CO 2 to restart the catalytic cycle. CO bound clusters are predicted to be thermodynamically stable, suggesting that carbonyl species might be off-cycle intermediates. Mechanistic CV studies as well as in situ studies by IR spectroscopy provide evidence for carbonyl-ligated clusters, supporting these compounds as unusual examples of small molecule binding to Fe 4 S 4 clusters. Finally, this work establishes Fe 4 S 4 cluster-based coordination polymers as direct electrocatalysts for CO 2 reduction and provides mechanistic insights into how these species mediate catalytic conversions of small molecules.

cluster chemistry↗
DOE OSTI2023

G 0 W 0 Ionization Potentials of First-Row Transition Metal Aqua Ions

Here, we report computations of the vertical ionization potentials within the GW approximation of the near-complete series of first-row transition metal (V–Cu) aqua ions in their most common oxidation states, i.e., V 3+ , Cr 3+ , Cr 2+ , Mn 2+ , Fe 3+ , Fe 2+ , Co 2+ , Ni 2+ , and Cu 2+ . The d-orbital occupancy of these systems spans a broad range from d 2 to d 9 . All of the structures were first optimized at the density functional theory level using a large cluster of explicit water molecules that are embedded in a continuum solvation model. Vertical ionization potentials were computed with the one-shot G 0 W 0 approach on a range of transition metal ion clusters (6, 18, 40, and 60 explicit water molecules), wherein the convergence with respect to the basis set size was evaluated using the systems with 40 water molecules. We assess the results using three different density functional approximations as starting points for the vertical ionization potential calculations, namely, G 0 W 0 @PBE, G 0 W 0 @PBE0, and G 0 W 0 @r 2 SCAN. While the predicted ground-state structures are similar to all three exchange-correlation functionals, the vertical ionization potentials were in closer agreement with experiment when using the G 0 W 0 @PBE0 and G 0 W 0 @r 2 SCAN approaches, with the r 2 SCAN-based calculations being significantly less expensive. Computed bond distances and vertical ionization potentials for all structures are in good agreement with available experimental data.

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

Elucidating the geometric and electronic structure of a fully sulfided analog of an Anderson polyoxomolybdate cluster

The catalytic activity of transition metal sulfide (TMS) clusters in small molecule activation, redox transformations, and charge transfer has inspired the design of novel TMS-based materials for energy-related catalysis and chemical applications. Polyoxometalates (POMs), known for their structural diversity, can in principle be transformed into TMS clusters; however, fully sulfided analogs are rarely isolated, likely due to the strong tendency of uncapped TMS clusters to agglomerate. Here, we report the geometric and electronic structure of a capping ligand-free fully sulfided analog of heptamolybdate Anderson POM [Mo VI 7 O 24 ] 6− , synthesized through the sulfidation of a nanoconfined POM secured within a porous Zr-metal organic framework (NU-1000). A combined computational and experimental analysis indicates that the sulfided counterpart of the Anderson POM is geometrically and electronically more sophisticated than the parent POM. Comparison of experimental pair distribution function (PDF) data with computational simulations confirms that, unlike the oxygen-only [Mo VI 7 O 24 ] 6− cluster, the [Mo IV 7 (μ 3 -S) 6 (μ 2 -SH) 6 (S 2 ) 6 ] 2− polythiometalate (PTM) exhibits diverse sulfur anions (S 2− , HS − , S 2 2− ). DFT calculations indicate that H 2 S acts as a reducing agent, and together with terminal disulfide (S 2 2− ) ligands in the PTM structure, facilitates the complete reduction of all seven Mo VI centers in the parent POM to Mo IV . These findings are supported by X-ray photoelectron spectroscopy (XPS), which confirms exclusive Mo IV , and elemental analysis, which shows quantitative sulfur incorporation. Difference envelope density (DED) mapping further reveals that the PTM clusters are spatially confined within the MOF pores, preventing agglomeration and preserving molecular integrity.

Rabbani, S. M. Gulam [The Ohio State University, C↗
DOE OSTI2022

Avoiding excess computation in asynchronous evolutionary algorithms

Abstract Asynchronous evolutionary algorithms are becoming increasingly popular as a means of making full use of many processors while solving computationally expensive search and optimization problems. These algorithms excel at keeping large clusters fully utilized, but may sometimes inefficiently sample an excess of fast‐evaluating solutions at the expense of higher‐quality, slow‐evaluating ones. We have previously introduced a steady‐state parent selection strategy, SWEET (“Selection whilE EvaluaTing”), that sometimes selects individuals that are still being evaluated and allows them to reproduce early. We perform a takeover‐time analysis that confirms that this strategy gives slow‐evaluating individuals that have higher fitnesses an increased ability to multiply in the population. We also find that SWEET appears effective at improving optimization performance on problems in which solution quality is positively correlated with evaluation time. We evaluate our approach on six simulated real‐valued optimization problems and three real‐world applications: an autonomous vehicle controller problem that involves tuning a spiking neural network and two adversarial EA problems. We further evaluate SWEET versus a basic asynchronous process in a simulated setting. We present evidence that SWEET outperforms basic asynchronous processes in a use‐case in which performance is positively correlated with evaluation time, and performs comparably (and often better) than basic asynchronous processes in several use‐cases where performance is negatively correlated with evaluation time. That said, in the cases where performance and evaluation time are negatively correlated the variance of outcomes for SWEET is notably high.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1976

Implicit finite-difference procedures for the computation of vortex wakes

Implicit finite-difference procedures for the primitive form of the incompressible Navier-Stokes and the compressible Euler equations are used to compute vortex wake flows. The partial differential equations in strong conservation-law form are transformed to cluster grid points in regions with large changes in vorticity. In addition to clustering, fourth-order accurate, spatial difference operators are used to help resolve the flow-field gradients. The use of implicit time-differencing permits large time steps to be taken since temporal variations are typically small. Computational efficiency is achieved by approximate factorization. Both two-dimensional and preliminary three-dimensional calculations are described and qualitatively compared with existing experimental data.

Steger, J. L.↗
DOE OSTI2025

Spatiotemporal pattern detection, generation, and computation with circuits

Abstract Implementations of neurons, delays, and synapse circuits are presented with simulations. These neural elements are used to create two small spiking neural networks, the Rate-Window and Order-Biased clusters, which are capable of detecting simple two-spike spatiotemporal patterns. A simple pattern detecting network (SPDN) is created by combining the Rate-Window and Order-Biased clusters, where clusters are small spiking neural networks, and its simple pattern detection ability is demonstrated in simulation. The SPDN is used to implement a complex pattern detecting network (CPDN) and its complex pattern detection ability is demonstrated in simulation. Methods for generating arbitrary spatiotemporal patterns are presented. The CPDN and spatiotemporal pattern generation methods are then used to implement a novel spatiotemporal computing paradigm based on detecting and responding to spatiotemporal symbols. A simulation of a spatiotemporal half adder is presented to demonstrate the computing paradigm.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Tailoring composition and deformation modes at the microstructural level for next generation low-cost high-strength austenitic stainless steels

The objective of this project is to enable deliberate development of cost-effective, hydrogen resistant alloys by establishing detailed relationships specific to the effects of alloy composition, short-range order (SRO), and microsegregation in the presence of hydrogen on the transition between homogeneous deformation and localized plasticity in shear bands. In collaboration with the International Institute for Carbon-Neutral Energy Research, I2CNER, at Kyushu University in Japan, we conceptualized, designed, and manufactured four austenitic alloys that maintain corrosion resistance and ensure lower cost relative to baseline commercial alloys. The mechanical properties and deformation modes of the novel alloys (KU alloys) were assessed in the presence of hydrogen (H). Correlations between composition and performance revealed that two of the KU alloys are suitable replacements for 316 steel, while another is a viable replacement for 304 steel at room temperature. We found that, in the presence of other austenite stabilizing elements namely Mn and N, replacing Ni with Cu does not lead to martensite formation as has been previously reported.1–3 Furthermore, we found that the addition of Cu leads to an earlier onset of multiple slip resulting in an relative earlier onset of a higher work hardening rate (WHR). Greater understanding of the relationships between alloy composition and SRO required the development of a novel advanced electron diffraction methodology to characterize SRO in complex FCC alloys. This innovative approach, which combines fluctuation and correlation analyses of diffuse-scattering signals, successfully differentiated between SRO and long-range ordering (LRO). Further investigations into annealed austenitic stainless steels could provide insights into manipulating SRO and its effects on material properties. Atomistic simulations provided understanding of SRO behavior that was difficult to capture experimentally. This project created the first spin cluster expansion model that is able to capture and describe SRO effects in Fe-Ni-Cr FCC alloys, accounting for the non-negligible effects of magnetism. An automated computational workflow was established to provide reliable predictions of SRO in Fe-Ni-Cr austenitic alloys, both with and without the presence of H atoms. Analysis of the propensity for SRO in Fe-Ni-Cr alloys revealed that H tends to cluster with specific, well-defined SRO domains. The computational framework is general purpose and can be extended to realistic stainless steels across diverse composition ranges. With confidence that SRO is possible in austenitic stainless steels, we developed a discrete dislocation finite element code to understand the interaction of dislocations with SRO in the presence of H. By incorporating H effects on the dislocation emission and SRO stress field we show that the critical stress for the dislocation pileup to breakthrough the SRO domain decreases in the presence of H, which directly contributes localized deformation at the macroscale. Through the simulation of a uniaxial tension test, we demonstrated that H-induced weakening of SRO stress field and H-enhanced dislocation emission can lead to the onset of shear localization at lower macroscopic strains. As a whole, this project identified three novel alloys that show improvements in performance and cost efficiency for H-facing applications by studying correlations between alloy chemistry and deformation behavior. We also made significant advancements to experimental and computational methodologies necessary to study the chemistry and distribution of SRO across a range of alloys, which in turn allowed us to demonstrate how deformation mechanisms change due to the contributions of SRO in austenitic alloys in the presence of H. The combined advancements in fundamental understanding with novel alloy development in this project has increased the viability of next generation H-technologies for the broader public through accessible low-cost alloys and accelerated development towards future H-infrastructure.

08 HYDROGEN↗
DOE OSTI2020

Parallel performance of algebraic multigrid domain decomposition

Algebraic multigrid (AMG) is a widely used scalable solver and preconditioner for large-scale linear systems resulting from the discretization of a wide class of elliptic PDEs. While AMG has optimal computational complexity, the cost of communication has become a significant bottleneck that limits its scalability as processor counts continue to grow on modern machines. This article examines the design, implementation, and parallel performance of a novel algorithm, algebraic multigrid domain decomposition (AMG-DD), designed specifically to limit communication. The goal of AMG-DD is to provide a low-communication alternative to standard AMG V-cycles by trading some additional computational overhead for a significant reduction in communication cost. Numerical results show that AMG-DD achieves superior accuracy per communication cost compared with AMG, and speedup over AMG is demonstrated on a large GPU cluster.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Molecular structure determination: Equilibrium structure of pyrimidine (m-C 4 H 4 N 2 ) from rotational spectroscopy (reSE) and high-level ab initio calculation (re) agree within the uncertainty of experimental measurement

The pure rotational spectrum of pyrimidine (m-C 4 H 4 N 2 ), the meta-substituted dinitrogen analog of benzene, has been studied in the millimeter-wave region from 235 GHz to 360 GHz. The rotational spectrum of the ground vibrational state has been assigned and fit to yield accurate rotational and distortion constants. Over 1700 distinct transitions were identified for the normal isotopologue in its ground vibrational state and least-squares fit to a partial sextic S-reduced Hamiltonian. Transitions for all four singly substituted 13 C and 15 N isotopologues were observed at natural abundance and were likewise fit. Deuterium-enriched samples of pyrimidine were synthesized, giving access to all eleven possible deuterium-substituted isotopologues, ten of which were previously unreported. Experimental values of rotational constants and computed values of vibration–rotation interaction constants and electron-mass corrections were used to determine semi-experimental equilibrium structures (r e SE ) of pyrimidine. The r e SE structure obtained using coupled-cluster with single, double, and perturbative triple excitations [CCSD(T)] corrections shows exceptional agreement with the re structure computed at the CCSD(T)/cc-pCV5Z level (≤0.0002 Å in bond distance and ≤0.03° in bond angle). Of the various computational methods examined, CCSD(T)/cc-pCV5Z is the only method for which the computed value of each geometric parameter lies within the statistical experimental uncertainty (2σ) of the corresponding semi-experimental coordinate. The exceptionally high accuracy and precision of the structure determination is a consequence of the large number of isotopologues measured, the precision and extent of the experimental frequency measurements, and the sophisticated theoretical treatment of the effects of vibration–rotation coupling and electron mass. Taken together, these demanding experimental and computational studies establish the capabilities of modern structural analysis for a prototypical monocyclic aromatic compound.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1987

Small Al clusters. I - The effect of basis set and correlation on the geometry of small Al clusters

A detailed study is presented of the basis set requirements and effects of correlation on the geometry and structure of small Al(n) clusters n = 2, 4, and 13. An effective core potential (ECP) is developed from the Huzinaga basis which accurately reproduces the all-electron results. It is found that depolarization functions are very important in computing the bond length, and that the bond contraction obtained is about as large for a 13-atom cluster as for the Al(4) rhombus structure. With d functions on each center in Al(13), a bond distance shorter than the bulk is obtained, as expected. The inclusion of correlation in Al(4) is found to be less important than the addition of polarization functions for determining the bond length, but increases substantially the calculated atomization energy. These calibration calculations allow an accurate treatment for Al(n) clusters to be designed.

Bauschlicher, Charles W., Jr.↗
DOE OSTI2023

Latent code-based fusion: A Volterra neural network approach

We propose a deep structure encoder using Volterra Neural Networks (VNNs) to seek a latent representation of multi-modal data whose features are jointly captured by a union of subspaces. The so-called self-representation embedding of the latent codes leads to a simplified fusion which is driven by a similarly constructed decoding. The Volterra Filter architecture achieved reduction in parameter complexity is primarily due to controlled non-linearities being introduced by the higher-order convolutions in lieu of generalized activation functions. Experimental results on two different datasets have shown a significant improvement in the clustering performance for VNNs auto-encoder over conventional Convolutional Neural Networks (CNNs) auto-encoder. In addition, we also show that the proposed approach demonstrates a much-improved sample complexity over CNN-based auto-encoder with a robust classification performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Stability and mobility of tungsten clusters on tungsten (110) surface: Ab initio and atomistic simulations

Quantifying the surface transport properties of tungsten (W) is of prime importance to understand the formation of nano-fuzz in fusion plasma-facing conditions. The stability and mobility of W adatom clusters (W n , n = 2-9) on the W(110) surface has been investigated by computer simulations, including ab initio calculations using density functional theory (DFT) and molecular statics (MS) simulations with multiple W interatomic potentials. The DFT results demonstrate that the sequential binding energy generally increases with number of W adatoms, except for the 5 th and 7 th W adatoms. The most common elemental migration steps of W n (n>2) clusters are observed to consist of monomer and dimer hops, while larger W n clusters can also diffuse by dissociation and recombination of smaller clusters. The threshold migration energy of W 9 is the highest, then followed by W 8 , W 4 , and W 6 , while W 3 , W 5 , and W 7 have similar migration energies. Compared to DFT, each interatomic potential evaluated overestimates the binding energies of W n clusters. Additionally, the embedded-atom potential developed by Juslin and Wirth adequately predicts the threshold migration energy of W n (n>2) clusters, although it predicts different underlying migration mechanisms. The results show that interaction mechanism between W adatoms controls the stability and mobility of W n clusters on the W(110) surface.

36 MATERIALS SCIENCE↗
NASA NTRS2000

Parallel and Distributed Computational Fluid Dynamics: Experimental Results and Challenges

This paper describes several results of parallel and distributed computing using a large scale production flow solver program. A coarse grained parallelization based on clustering of discretization grids combined with partitioning of large grids for load balancing is presented. An assessment is given of its performance on distributed and distributed-shared memory platforms using large scale scientific problems. An experiment with this solver, adapted to a Wide Area Network execution environment is presented. We also give a comparative performance assessment of computation and communication times on both the tightly and loosely-coupled machines.

Djomehri, Mohammad Jahed↗
DOE OSTI2021

Software for the frontiers of quantum chemistry: An overview of developments in the Q-Chem 5 package

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange-correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear-electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an "open teamware" model and an increasingly modular design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2004

Scalable Multiprocessor for High-Speed Computing in Space

A report discusses the continuing development of a scalable multiprocessor computing system for hard real-time applications aboard a spacecraft. "Hard realtime applications" signifies applications, like real-time radar signal processing, in which the data to be processed are generated at "hundreds" of pulses per second, each pulse "requiring" millions of arithmetic operations. In these applications, the digital processors must be tightly integrated with analog instrumentation (e.g., radar equipment), and data input/output must be synchronized with analog instrumentation, controlled to within fractions of a microsecond. The scalable multiprocessor is a cluster of identical commercial-off-the-shelf generic DSP (digital-signal-processing) computers plus generic interface circuits, including analog-to-digital converters, all controlled by software. The processors are computers interconnected by high-speed serial links. Performance can be increased by adding hardware modules and correspondingly modifying the software. Work is distributed among the processors in a parallel or pipeline fashion by means of a flexible master/slave control and timing scheme. Each processor operates under its own local clock; synchronization is achieved by broadcasting master time signals to all the processors, which compute offsets between the master clock and their local clocks.

Lux, James↗
DOE OSTI2021

Avoiding Excess Computation in Asynchronous Evolutionary Algorithms

Asynchronous evolutionary algorithms are becoming increasingly popular as a means of making full use of many processors while solving computationally expensive search and optimization problems. These algorithms excel at keeping large clusters fully utilized, but may sometimes inefficiently sample an excess of fast-evaluating solutions at the expense of higher-quality, slow-evaluating ones. We introduce a steady-state parent selection strategy, SWEET (“Selection whilE EvaluaTing”), that sometimes selects individuals that are still being evaluated and allows them to reproduce early. This gives slow-evaluating individuals that have higher fitnesses an increased ability to multiply in the population. We find that SWEET appears effective in simulated take-over time analysis, but that its benefit is confined mostly to early in the run, and our preliminary study on an autonomous vehicle controller problem that involves tuning a spiking neural network proves inconclusive.

Scott, Eric↗
DOE OSTI2023

SHAPER: can you hear the shape of a jet?

The identification of interesting substructures within jets is an important tool for searching for new physics and probing the Standard Model at colliders. Many of these substructure tools have previously been shown to take the form of optimal transport problems, in particular the Energy Mover’s Distance (EMD). In this work, we show that the EMD is in fact the natural structure for comparing collider events, which accounts for its recent success in understanding event and jet substructure. We then present a Shape Hunting Algorithm using Parameterized Energy Reconstruction (SHAPER), which is a general framework for defining and computing shape-based observables. SHAPER generalizes N-jettiness from point clusters to any extended, parametrizable shape. This is accomplished by efficiently minimizing the EMD between events and parameterized manifolds of energy flows representing idealized shapes, implemented using the dual-potential Sinkhorn approximation of the Wasserstein metric. We show how the geometric language of observables as manifolds can be used to define novel observables with built-in infrared-and-collinear safety. We demonstrate the efficacy of the SHAPER framework by performing empirical jet substructure studies using several examples of new shape-based observables.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Many-body entanglement in solid-state emitters

The preparation and control of quantum states lie at the heart of quantum information science. Recent advances in solid-state quantum emitters (QEs) and nanophotonics have transformed the landscape of quantum photonic technologies, enabling scalable generation of quantum states of light and matter. A new frontier in solid-state quantum photonics is the engineering of many-body interactions between QEs and photons to achieve robust coherence and controllable many-body entanglement. These entangled states, including photonic graph and cluster states, superradiant emission and emergent quantum phases, are promising for quantum computation, sensing and simulation. However, intrinsic inhomogeneities and decoherence in solid-state platforms pose considerable challenges in realizing such complex entangled states. This Review provides an overview of fundamental many-body interactions and dynamics at the light–matter interfaces of solid-state QEs and discusses recent advances in mitigating decoherence and harnessing robust many-body coherence.

36 MATERIALS SCIENCE↗