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Results for “Intrinsic computation”

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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Cooperative roles of water and metal-support interfaces in the selective hydrogenation of cinnamaldehyde over cobalt boride catalysts

Chemoselectivity in the hydrogenation of carbonyl over alkene bonds is valuable for producing industrial chemicals. In this study, we investigate the role of water in the selective hydrogenation of cinnamaldehyde on cobalt and cobalt boride catalysts by combining rate measurements, in situ characterization, and computational calculations. Ex situ and in situ spectroscopic analyses indicate that the boron species do not significantly modify the intrinsic electronic properties of cobalt. However, during thermal treatments, they exsolve from the bulk phase and become enriched on the surface, forming acidic species that enhance the activity and selectivity of carbonyl bond hydrogenation by three and two times, respectively. A small amount of water is able to promote hydrogenation. When titania, ceria, and zirconia are used as supports, metal-support interactions result in smaller particles that show a drop in selectivity toward the desirable unsaturated alcohol. Overall, the combination of the presence of boron species and strong metal-support interactions protects the catalysts from water attack, resulting in enhanced stability.

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

Origins of enhanced oxygen reduction activity of transition metal nitrides

Transition metal nitride (TMN-) based materials have recently emerged as promising non-precious-metal-containing electrocatalysts for the oxygen reduction reaction (ORR) in alkaline media. However, the lack of fundamental understanding of the oxide surface has limited insights into structure–(re)activity relationships and rational catalyst design. Here, in this work, we demonstrate how a well-defined TMN can dictate/control the as-formed oxide surface and the resulting ORR electrocatalytic activity. Structural characterization of MnN nanocuboids revealed that an electrocatalytically active Mn 3 O 4 shell grew epitaxially on the MnN core, with an expansive strain along the [010] direction to the surface Mn 3 O 4 . The strained Mn 3 O 4 shell on the MnN core exhibited an intrinsic activity that was over 300% higher than that of pure Mn 3 O 4 . A combined electrochemical and computational investigation indicated/suggested that the enhancement probably originates from a more hydroxylated oxide surface resulting from the expansive strain. This work establishes a clear and definitive atomistic picture of the nitride/oxide interface and provides a comprehensive mechanistic understanding of the structure–reactivity relationship in TMNs, critical for other catalytic interfaces for different electrochemical processes.

electrocatalysis↗
DOE OSTI2021

Scalable In Situ Computation of Lagrangian Representations via Local Flow Maps

In situ computation of Lagrangian flow maps to enable post hoc time-varying vector field analysis has recently become an active area of research. However, the current literature is largely limited to theoretical settings and lacks a solution to address scalability of the technique in distributed memory. To improve scalability, we propose and evaluate the benefits and limitations of a simple, yet novel, performance optimization. Our proposed optimization is a communication-free model resulting in local Lagrangian flow maps, requiring no message passing or synchronization between processes, intrinsically improving scalability, and thereby reducing overall execution time and alleviating the encumbrance placed on simulation codes from communication overheads. To evaluate our approach, we computed Lagrangian flow maps for four time-varying simulation vector fields and investigated how execution time and reconstruction accuracy are impacted by the number of GPUs per compute node, the total number of compute nodes, particles per rank, and storage intervals. Our study consisted of experiments computing Lagrangian flow maps with up to 67M particle trajectories over 500 cycles and used as many as 2048 GPUs across 512 compute nodes. In all, our study contributes an evaluation of a communication-free model as well as a scalability study of computing distributed Lagrangian flow maps at scale using in situ infrastructure on a modern supercomputer.

Sane, Sudhanshu↗
DOE OSTI2025

Non-intrusive reduced-order modeling for dynamical systems with spatially localized features

This work presents a non-intrusive reduced-order modeling framework for dynamical systems with spatially localized features characterized by slow singular value decay. The proposed approach builds upon two existing methodologies for reduced and full-order non-intrusive modeling, namely Operator Inference (OpInf) and sparse Full-Order Model (sFOM) inference. We decompose the domain into two complementary subdomains that exhibit fast and slow singular value decay. The dynamics of the subdomain exhibiting slow singular value decay are learned with sFOM while the dynamics with intrinsically low dimensionality on the complementary subdomain are learned with OpInf. The resulting, coupled OpInf-sFOM formulation leverages the computational efficiency of OpInf and the high resolution of sFOM, and thus enables fast non-intrusive predictions for conditions beyond those sampled in the training data set. A novel regularization technique with a closed-form solution based on the Gershgorin disk theorem is introduced to promote stable sFOM and OpInf models. We also provide a data-driven indicator for subdomain selection and ensure solution smoothness over the interface via a post-processing interpolation step. We evaluate the efficiency of the approach in terms of offline and online speedup through a quantitative, parametric computational cost analysis. We demonstrate the coupled OpInf-sFOM formulation for two test cases: a one-dimensional Burgers’ model for which accurate predictions beyond the span of the training snapshots are presented, and a two-dimensional parametric model for the Pine Island Glacier ice thickness dynamics, for which the OpInf-sFOM model achieves an average prediction error on the order of 1% with an online speedup factor of approximately 8$\times$ compared to the numerical simulation.

42 ENGINEERING↗
DOE OSTI2022

Magnetic properties of equiatomic CrMnFeCoNi

Magnetic, specific heat, and structural properties of the equiatomic Cantor alloy system are reported for temperatures between 5 and 300 K, and up to fields of 70 kOe. Here magnetization measurements performed on as-cast, annealed, and cold-worked samples reveal a strong processing history dependence and that high-temperature annealing after cold working does not restore the alloy to a “pristine” state. Measurements on known precipitates show that the two transitions, detected at 43 and 85 K, are intrinsic to the Cantor alloy and not the result of an impurity phase. Experimental and ab initio density functional theory computational results suggest that these transitions are a weak ferrimagnetic transition and a spin-glass-like transition, respectively, and magnetic and specific heat measurements provide evidence of significant Stoner enhancement and electron-electron interactions within the material.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Optical encoder devices and systems

Devices, systems and methods for encoding information using optical components are described. Information associated with a first optical signal (e.g., an optical pump) is encoded onto the phase of a second optical signal (e.g., an optical probe) using cross phase modulation (XPM) in a non-linear optical medium. The optical signals are multiplexed together into the nonlinear optical medium. The probe experiences a modified index of refraction as it propagates through the medium and thus accumulates a phase change proportional to the intensity of the pump. The disclosed devices can be incorporated into larger components and systems for various applications such as scientific diagnostics, radar, remote sensing, wireless communications, and quantum computing that can benefit from encoding and generation of low noise, high resolution signals. Examples of the encoded information includes intrinsic noise from the optical source, or others signals of interest, such as electrical, optical, X-ray, or high-energy particle signals.

Buckley, Brandon Walter↗
DOE OSTI2023

Optical encoder devices and systems

Devices, systems and methods for encoding information using optical components are described. Information associated with a first optical signal (e.g., an optical pump) is encoded onto the phase of a second optical signal (e.g., an optical probe) using cross phase modulation (XPM) in a non-linear optical medium. The optical signals are multiplexed together into the nonlinear optical medium. The probe experiences a modified index of refraction as it propagates through the medium and thus accumulates a phase change proportional to the intensity of the pump. The disclosed devices can be incorporated into larger components and systems for various applications such as scientific diagnostics, radar, remote sensing, wireless communications, and quantum computing that can benefit from encoding and generation of low noise, high resolution signals. Examples of the encoded information includes intrinsic noise from the optical source, or others signals of interest, such as electrical, optical, X-ray, or high-energy particle signals.

Buckley, Brandon Walter↗
DOE OSTI2024

Task-oriented machine learning surrogates for tipping points of agent-based models

We present a machine learning framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale approach, for the construction of different types of effective reduced order models from detailed agent-based simulators and the systematic multiscale numerical analysis of their emergent dynamics. The specific tasks of interest here include the detection of tipping points, and the uncertainty quantification of rare events near them. Our illustrative examples are an event-driven, stochastic financial market model describing the mimetic behavior of traders, and a compartmental stochastic epidemic model on an Erdös-Rényi network. We contrast the pros and cons of the different types of surrogate models and the effort involved in learning them. Importantly, the proposed framework reveals that, around the tipping points, the emergent dynamics of both benchmark examples can be effectively described by a one-dimensional stochastic differential equation, thus revealing the intrinsic dimensionality of the normal form of the specific type of the tipping point. This allows a significant reduction in the computational cost of the tasks of interest.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Noise reduction in X-ray photon correlation spectroscopy with convolutional neural networks encoder–decoder models

Abstract Like other experimental techniques, X-ray photon correlation spectroscopy is subject to various kinds of noise. Random and correlated fluctuations and heterogeneities can be present in a two-time correlation function and obscure the information about the intrinsic dynamics of a sample. Simultaneously addressing the disparate origins of noise in the experimental data is challenging. We propose a computational approach for improving the signal-to-noise ratio in two-time correlation functions that is based on convolutional neural network encoder–decoder (CNN-ED) models. Such models extract features from an image via convolutional layers, project them to a low dimensional space and then reconstruct a clean image from this reduced representation via transposed convolutional layers. Not only are ED models a general tool for random noise removal, but their application to low signal-to-noise data can enhance the data’s quantitative usage since they are able to learn the functional form of the signal. We demonstrate that the CNN-ED models trained on real-world experimental data help to effectively extract equilibrium dynamics’ parameters from two-time correlation functions, containing statistical noise and dynamic heterogeneities. Strategies for optimizing the models’ performance and their applicability limits are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Laser-induced quenching of metastability at the Mott insulator to metal transition

There is growing interest in strongly correlated insulator thin films because the intricate interplay of their intrinsic and extrinsic state variables causes memristive behavior that might be used for biomimetic devices in the emerging field of neuromorphic computing. Here, in this study, we find that laser irradiation tends to drive V 2 ⁢O 3 from supercooled/superheated metastable states toward thermodynamic equilibrium, most likely in a nonthermal way. We study thin films of the prototypical Mott-insulator V2⁢O3, which show spontaneous phase separation into metal-insulator herringbone domains during the Mott transition. Here, we use low-temperature microscopy to investigate how these metal-insulator domains can be modified by scanning a focused laser beam across the thin film surface. We find that the response depends on the thermal history: When the thin film is heated from below the Mott transition temperature, the laser beam predominantly induces metallic domains. On the contrary, when the thin film is cooled from a temperature above the transition, the laser beam predominantly induces insulating domains. Very likely, the V 2 ⁢O 3 thin film is in a superheated or supercooled state, respectively, during the first-order phase transition, and the perturbation by a laser beam drives these metastable states into stable ones. This way, the thermal history is locally erased. Our findings are supported by a phenomenological model with a laser-induced lowering of the energy barrier between the metastable and equilibrium states.

Materials Science↗
DOE OSTI2022

Efficient Probabilistic Computing with Stochastic Perovskite Nickelates

Probabilistic computing has emerged as a viable approach to solve hard optimization problems. Devices with inherent stochasticity can greatly simplify their implementation in electronic hardware. In this report we demonstrate intrinsic stochastic resistance switching controlled via electric fields in perovskite nickelates doped with hydrogen. The ability of hydrogen ions to reside in various metastable configurations in the lattice leads to a distribution of transport gaps. With experimentally characterized p-bits, a shared-synapse p-bit architecture demonstrates highly parallelized and energy-efficient solutions to optimization problems such as integer factorization and Boolean satisfiability. The results introduce perovskite nickelates as scalable potential candidates for probabilistic computing and showcase the potential of light-element dopants in next-generation correlated semiconductors.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

A general framework for quantifying uncertainty at scale

Abstract In many fields of science, comprehensive and realistic computational models are available nowadays. Often, the respective numerical calculations call for the use of powerful supercomputers, and therefore only a limited number of cases can be investigated explicitly. This prevents straightforward approaches to important tasks like uncertainty quantification and sensitivity analysis. This challenge can be overcome via our recently developed sensitivity-driven dimension-adaptive sparse grid interpolation strategy. The method exploits, via adaptivity, the structure of the underlying model (such as lower intrinsic dimensionality and anisotropic coupling of the uncertain inputs) to enable efficient and accurate uncertainty quantification and sensitivity analysis at scale. Here, we demonstrate the efficiency of this adaptive approach in the context of fusion research, in a realistic, computationally expensive scenario of turbulent transport in a magnetic confinement tokamak device with eight uncertain parameters, reducing the effort by at least two orders of magnitude. In addition, we show that this refinement method intrinsically provides an accurate surrogate model that is nine orders of magnitude cheaper than the high-fidelity model.

Farcaş, Ionuţ-Gabriel (ORCID:0000000238841580)↗
DOE OSTI2021

Monte Carlo simulation of the passage of $\gamma$-rays and $\alpha$-particles in CsI

In this work, theoretical and computational methods for simulating the creation of ionization tracks by fast ions in solids were applied to the passage of a-particles in CsI, an inorganic scintillator commonly used for radiation detection. The methods were implemented in a Monte Carlo program to simulate the interaction of $\alpha$-particles, with incident energies of up to 1 MeV, with CsI. The simulations followed the fate of individual electron-hole pairs and thus allowed for a detailed description of the microscopic structure of ionization tracks created by incident radiation. Simulations were also performed with $\gamma$-rays of the same energy to compare and contrast the ionization tracks obtained with both types of particle. Intrinsic properties such as the mean energy per electron-hole pair, Fano factor, maximum theoretical light yield, and spatial distributions of electron-hole pairs were computed for both $\alpha$-particles and $\gamma$-rays. $\alpha$-particles created cylindrical tracks that were initially aligned with the incident direction and with initial radii of a few nanometers, whereas $\gamma$-rays showed significant scattering, resulting in probability distributions with lower intensities and much greater radial extents.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Spatiotemporal Thermal Coupling in VO 2 Device Arrays

Correlated oxides such as VO 2 exhibit an electrically driven insulator–metal transition (IMT) that underlies their promise for neuromorphic and memory devices. Yet the IMT is not a uniform bulk process but a spatiotemporal phenomenon in which local heating nucleates filaments, contracts or dissolves them with the electric field, and couples to the environment. In this work, we directly image the VO 2 IMT dynamics by mid-wave infrared, thermography synchronized with electrical transport, resolving device temperature with micrometer spatial and microsecond temporal resolution. At the single-device level, we capture the full cycle of filament nucleation, contraction, and relaxation during current/voltage-driven resistive switching. At the array level, we show that heat propagates across etched gaps with an effective length scale of ∼131 µm, enabling cooperative behaviors among electrically isolated devices. Short-range distanced devices exhibit mutual filament attraction and sequential dissolution, while long-range distanced devices differentiate into distinct roles: drivers that initiate switching, cooperative responders that undergo assisted self-oscillations, and passive reporters that record the thermal field. Furthermore, these results reframe thermal crosstalk, long regarded as parasitic, as an intrinsic coupling channel and design principle for organizing collective switching behaviors, with direct implications for emergent circuit functionality in neuromorphic and unconventional computing architectures.

coupling↗
DOE OSTI2020

Far-forward neutrinos at the Large Hadron Collider

We present a new calculation of the energy distribution of high-energy neutrinos from the decay of charm and bottom hadrons produced at the Large Hadron Collider (LHC). In the kinematical region of very forward rapidities, heavy-flavor production and decay is a source of tau neutrinos that leads to thousands of charged-current tau neutrino events in a 1 m long, 1 m radius lead neutrino detector at a distance of 480 m from the interaction region. In our computation, next-to-leading order QCD radiative corrections are accounted for in the production cross-sections. Non-perturbative intrinsic-kT effects are approximated by a simple phenomenological model introducing a Gaussian kT-smearing of the parton distribution functions, which might also mimic perturbative effects due to multiple initial-state soft-gluon emissions. The transition from partonic to hadronic states is described by phenomenological fragmentation functions. To study the effect of various input parameters, theoretical predictions for D± s production are compared with LHCb data on double-differential cross-sections in transverse momentum and rapidity. The uncertainties related to the choice of the input parameter values, ultimately affecting the predictions of the tau neutrino event distributions, are discussed. We consider a 3+1 neutrino mixing scenario to illustrate the potential for a neutrino experiment to constrain the 3+1 parameter space using tau neutrinos and antineutrinos. We find large theoretical uncertainties in the predictions of the neutrino fluxes in the far-forward region. Untangling the effects of tau neutrino oscillations into sterile neutrinos and distinguishing a 3+1 scenario from the standard scenario with three active neutrino flavours, will be challenging due to the large theoretical uncertainties from QCD.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Diffusion, atomic transport, and ordering in Al-Zr alloys: FCC and liquid phases

Additive manufacturing of materials with controlled microstructure demands knowledge of atomic scale properties near the solid-liquid transition state. Many of these properties are not affordable by experimental techniques and computer modeling is the possible solution to the problem. In this paper, we present the results of an extended atomistic study of intrinsic atomic transport due to vacancy diffusion in FCC and L12 solid phases and diffusion in the liquid phase of Al-Zr alloys. A deceleration of the overall self-diffusion was observed when Zr was added to Al. The effect was stronger in the solid and weaker in the liquid. Additionally, the effect was strongly temperature dependent in the solid phases, but not in the liquid. Atomic transport was chemically biased: transport of Zr atoms was significantly slower than that of Al atoms, and this bias effect was stronger in the solid phases. The overall diffusion and chemical ordering processes in the liquid state were five to six orders in magnitude faster than in the solid. Chemical short-range order parameters in the liquid saturated at values close to those in the ordered L12 structure of Al3Zr. Chemical and structural ordering in the solid phases was negligible over the modeled microsecond time scale. Here, the results are discussed in view of optimizing additive manufacturing parameters for the controlled formation of metastable L1 2 precipitates.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Electrostatic Asymmetry of Wurtzite Nanocrystals and Resulting Photocatalytic Properties

Efficient light absorption and high energy of charge carriers of zinc cadmium sulfide (ZCS) make this semiconductor attractive for many photocatalytic reactions. Despite marked successes in shape-controlled synthesis of ZCS central to their photocatalytic performance, recombination of charge carriers as they migrate through the nanoscale particles results in losses of excitation energy, markedly reducing the photocatalytic activity of ZCS and other heterogeneous photocatalysts. Here, in this study, we show that the electrostatic asymmetry of nanostructures, previously discovered for nearly spherical nanoparticles, also manifests in wurtzite ZCS with planar geometry. The electrostatic asymmetry assists charge separation and substantially increases the yield of photocatalytic reactions in monocrystalline ZCS. The synthesized ZCS nanorods and nanoplates with identical chemical composition were found to have markedly different photocatalytic activity for evolution of hydrogen in water. Despite much smaller specific surface areas, the ~500 nm wide nanoplates displayed a hydrogen evolution rate 12 times higher than the ~35 nm long nanorods, also outperforming other ZCS photocatalysts. Experimental and computational data indicate that the homo- and heterojunction-free ZCS nanoplates with continuous wurtzite lattice behave essentially as nanoscale dipoles with intrinsic dipole moment as high as 48.39 D per unit cell. Electric-field-directed migration of charge carriers stimulates their localization on opposite parts of the nanoplates. Direct imaging of the intraparticle electrical field using off-axis electron holography confirmed their electrostatic asymmetry. Polarization-enhanced charge separation provides a new pathway to efficient and stable photocatalysts for sustainable energy technologies.

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

Visualization of the HIV-1 Env glycan shield across scales

The dense array of N-linked glycans on the HIV-1 envelope glycoprotein (Env), known as the “glycan shield,” is a key determinant of immunogenicity, yet intrinsic heterogeneity confounds typical structure–function analysis. Here, we present an integrated approach of single-particle electron cryomicroscopy (cryo-EM), computational modeling, and site-specific mass spectrometry (MS) to probe glycan shield structure and behavior at multiple levels. We found that dynamics lead to an extensive network of interglycan interactions that drive the formation of higher-order structure within the glycan shield. This structure defines diffuse boundaries between buried and exposed protein surface and creates a mapping of potentially immunogenic sites on Env. Analysis of Env expressed in different cell lines revealed how cryo-EM can detect subtle changes in glycan occupancy, composition, and dynamics that impact glycan shield structure and epitope accessibility. Importantly, this identified unforeseen changes in the glycan shield of Env obtained from expression in the same cell line used for vaccine production. Finally, by capturing the enzymatic deglycosylation of Env in a time-resolved manner, we found that highly connected glycan clusters are resistant to digestion and help stabilize the prefusion trimer, suggesting the glycan shield may function beyond immune evasion.

60 APPLIED LIFE SCIENCES↗