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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Characterizing the performance of node-aware strategies for irregular point-to-point communication on heterogeneous architectures

Supercomputer architectures are trending toward higher computational throughput due to the inclusion of heterogeneous compute nodes. These multi-GPU nodes increase on-node computational efficiency, while also increasing the amount of data to be communicated and the number of potential data flow paths. In this work, we characterize the performance of irregular point-to-point communication with MPI on heterogeneous compute environments through performance modeling, demonstrating the limitations of standard communication strategies for both device-aware and staging-through-host communication techniques. Presented models suggest staging communicated data through host processes then using node-aware communication strategies for high inter-node message counts. Notably, the models also predict that node-aware communication utilizing all available CPU cores to communicate inter-node data leads to the most performant strategy when communicating with a high number of nodes. Furthermore, model validation is provided via a case study of irregular point-to-point communication patterns in distributed sparse matrix–vector products. Importantly, we include a discussion on the implications model predictions have on communication strategy design for emerging supercomputer architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Rahasak—Scalable blockchain architecture for enterprise applications

Blockchain-based decentralized infrastructure has been adapted in various industries to handle the sensitive data in a privacy-preserving manner without trusting third parties. However, integrating state-of-the-art blockchain platforms with the scalable, enterprise-level applications result in several challenges. Current blockchain platforms do not support high transaction throughput, lack high scalability, and cannot provide real-time transaction processing and back-pressure operation handling in high transaction throughput applications(e.g Big data, IoT). In this paper, we propose a novel permissioned blockchain platform “Rahasak” for highly scalable, enterprise applications. Rahasak blockchain adopts the Apache Kafka-based consensus on top of a “Validate-Execute-Group” blockchain architecture to handle realtime transaction execution on the blockchain. The architecture is equipped with a functional programming and actor-based smart contract platform that enables concurrent execution of transactions in the blockchain. Rahasak supports high transaction throughput, high scalability, concurrent transaction execution, data analytics features. Finally, with Rahasak, we make blockchain more scalable, secure, structured and meaningful for further data analytics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Ultraviolet Superradiance from Mega-Networks of Tryptophan in Biological Architectures

Networks of tryptophan (Trp)–an aromatic amino acid with strong fluorescence response–are ubiquitous in biological systems, forming diverse architectures in transmembrane proteins, cytoskeletal filaments, subneuronal elements, photoreceptor complexes, virion capsids, and other cellular structures. We analyze the cooperative effects induced by ultraviolet (UV) excitation of several biologically relevant Trp mega-networks, thus giving insights into novel mechanisms for cellular signaling and control. Our theoretical analysis in the single-excitation manifold predicts the formation of strongly superradiant states due to collective interactions among organized arrangements of up to >10 5 Trp UV-excited transition dipoles in microtubule architectures, which leads to an enhancement of the fluorescence quantum yield (QY) that is confirmed by our experiments. We demonstrate the observed consequences of this superradiant behavior in the fluorescence QY for hierarchically organized tubulin structures, which increases in different geometric regimes at thermal equilibrium before saturation, highlighting the effect’s persistence in the presence of disorder. Our work thus showcases the many orders of magnitude across which the brightest (hundreds of femtoseconds) and darkest (tens of seconds) states can coexist in these Trp lattices.

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

Role of Molecular Architecture on Ion Transport in Ethylene oxide-Based Polymer Electrolytes

This work aims to develop a detailed mechanistic understanding of the role of a graft polymer architecture on lithium ion (Li + ) transport in poly(ethylene oxide)-based polymer electrolytes. Specifically, we compare Li + transport in poly-(ethylene oxide) (PEO) versus poly(oligo oxyethylene methacrylate) (POEM) polymers doped with lithium bis( trifluoromethanesulfonyl) (LiTFSI) salts, using both experimental electrochemical characterization and molecular dynamics (MD) simulations. Furthermore, our results indicate that POEM exhibits a range of relaxation processes that cannot be interpreted solely in terms of glass-transition temperature (T g ) effects. Due to its side-chain architecture, the segmental relaxation of POEM is nonuniform across ether oxygens (EOs) and shows a more pronounced sensitivity to temperature above T g compared to PEO. Moreover, POEM also exhibits a nonuniform Li + coordination behavior, in which Li + is primarily solvated by two different chains in POEM, compared to a single chain in PEO. Li + transport in POEM occurs via two events with distinct characteristic times: a fast intrachain hopping along side chains and a slow interchain hopping between side chains. Taken together, the relaxation processes and ion transport mechanisms identified in POEM provide useful insights into design of more effective solid polymer electrolytes.

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

Nanolatticed Architecture Mitigates Damage in Shark Egg Cases

Structural versatility and multifunctionality of biological materials have resulted in countless bioinspired strategies seeking to emulate the properties of nature. The nanostructured egg case of swell sharks is one of the toughest permeable membranes known and, thus, presents itself as a model system for materials where the conflicting properties, strength and porosity, are desirable. The egg case possesses an intricately ordered structure that is designed to protect delicate embryos from the external environment while enabling respiratory and metabolic exchange, achieving a tactical balance between conflicting properties. Herein, structural analyses revealed an enabling nanolattice architecture that constitutes a Bouligand-like nanoribbon hierarchical assembly. Three distinct hierarchical architectural adaptations enhance egg case survival: Bouligand-like organization for in-plane isotropic reinforcement, noncylindrical nanoribbons maximizing interfacial stress distribution, and highly ordered nanolattices enabling permeability and lattice-governed toughening mechanisms. Furthermore, these discoveries provide fundamental insights for the improvement of multifunctional membranes, fiber-reinforced soft composites, and mechanical metamaterials.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

The Cascade Effectiveness of 3-Terminal Tandem Photocathode Architectures as Applied to CO 2 Reduction

Cascade catalysis for photoelectrochemical CO 2 reduction (CO 2 R) decouples the overall reaction into sequential steps occurring on separately optimized catalysts (for example, Ag and Cu) between which an intermediate species such as CO is transferred. A 3-terminal tandem (3TT) photovoltaic architecture advantageously holds two different catalytic regions at different potentials under a single illumination source, but its overall efficiency is low. Using a stochastic reaction-diffusion model, we have examined 3TT photocathode design principles, focusing on the coupling of surface chemistry and transport of CO both inside the boundary layer and outward, into the bulk electrolyte. We find that ensuring that the lateral diffusion distance within the boundary layer is short compared to the boundary layer thickness and controlling bulk flow are key, with interdigitated designs showing an overall conversion efficiency improvement by 2 orders of magnitude compared to the side-by-side case without flow. These findings can be adapted for other cascaded architectures.

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

Polymer Architecture-Induced Trade-off between Conductivities and Transference Numbers in Salt-Doped Polymeric Ionic Liquids

Recent experiments have demonstrated that polymeric ionic liquids that share the same cation and anion but possess different architectures can exhibit markedly different conductivity and transference number characteristics when doped with lithium salt. In this study, we used atomistic molecular simulations on polymer chemistries inspired by the experiments to probe the mechanistic origins underlying the competition between conductivity and transference numbers. Our results indicate that the architecture of the polycationic ionic liquid plays a subtle but crucial role in modulating the anion–cation interactions, especially their dynamical coordination characteristics. Chemistries leading to longer-lived anion–cation coordinations relative to lithium–anion coordinations lead to lower conductivities and higher transference numbers. Furthermore, our results suggest that higher conductivities are accompanied by lower transference numbers and vice versa, revealing that alternative approaches may need to be considered to break this trade-off in salt-doped polyILs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Heteroepitaxial control of thickness, strain, and domain architecture in few-layer ferroelectric tin monochalcogenides

Thin-film epitaxy and epitaxial strain have been widely exploited to tune domain configurations, switching behavior, and ferroic properties in conventional three-dimensional ferroelectric thin films; however, its application to controlling the properties of two-dimensional (2D) ferroelectrics has remained largely unexplored. Here, using SnX (X = Se, S) as a model system, we demonstrate heteroepitaxial control of thickness, strain state, and domain architecture in few-layer ferroelectric SnX via growth on monolayer MoS2 van der Waals (vdW) templates. Compared with conventional growth, MoS2-templated heteroepitaxy promotes epitaxial alignment, yielding ultrathin SnSe films with improved crystalline quality, full areal coverage, and enlarged lateral dimensions. Strong interfacial epitaxial coupling induces pronounced in-plane strain and stabilizes a hierarchical ferroelastic domain architecture, in which long-range 90° stripe domains are further subdivided into nanoscale rotational variants, as revealed by scanning transmission electron microscopy and synchrotron X-ray microscopy. Piezoresponse force microscopy and second-harmonic polarimetry confirm robust in-plane polarization, while ferroelectricity in SnSe is established through polarization-electric field hysteresis and nonvolatile ferroelectric resistive switching with on/off ratios approaching 1000. A nonvolatile, switchable ferroelectric diode effect further evidences direct coupling between polarization and charge transport. Notably, ferroelectric switchability exhibits a strong thickness dependence and is preserved only below ∼10 layers. This vdW heteroepitaxial strategy is further extended to ferroelectric SnS. The seamless heteroepitaxial integration of 2D ferroelectrics with CMOS-compatible, wafer-scale MoS2 templates provides a general and scalable route for strain-enabled structural and ferroelectric engineering in emerging memory and low-power optoelectronic applications.

Wang, Yueyin↗
DOE OSTI2022

Influence of Fault Architecture on Induced Earthquake Sequence Evolution Revealed by High-Resolution Focal Mechanism Solutions

The increasing seismicity and improved seismic observation network in recent years provide an opportunity to explore factors that influence the triggering processes, spatiotemporal evolution, and maximum magnitude of induced sequences. We map the fault architecture and stress state of four induced sequences in Oklahoma to determine their influence on the seismicity. We systematically relocate the earthquakes and compute hundreds of focal mechanisms of small to medium events (1.0 < M < 5.1) using various techniques, including machine learning, for the Guthrie, Woodward, Cushing, and Fairview sequences. The detailed fault geometry and spatiotemporal evolution of seismicity and stress states reveal different dominant driving forces for each sequence. In Cushing and Fairview (largest event ≥M5.0), the main fault structures are near-vertical narrow strike-slip faults, with most of the small earthquake fault planes optimally oriented. The two sequences exhibit discontinuous temporal migration but strong earthquake self-driven rupture growth. In Guthrie and Woodward (largest event <M5.0), the two sequences show more complex diffuse fault structures with varying dipping angles along depth. The inverted focal mechanisms show a mix of strike-slip faulting and normal faulting in both sequences, and the normal faulting events are less optimally oriented than strike-slip events. The two sequences are dominated by continuous diffusive migration in time driven by pore pressure propagation. The above results suggest that fault architecture and stress state influence sequence evolution, major driving forces, and possibly maximum magnitude.

58 GEOSCIENCES↗
DOE OSTI2021

Correlator convolutional neural networks as an interpretable architecture for image-like quantum matter data

Image-like data from quantum systems promises to offer greater insight into the physics of correlated quantum matter. However, the traditional framework of condensed matter physics lacks principled approaches for analyzing such data. Machine learning models are a powerful theoretical tool for analyzing image-like data including many-body snapshots from quantum simulators. Recently, they have successfully distinguished between simulated snapshots that are indistinguishable from one and two point correlation functions. Thus far, the complexity of these models has inhibited new physical insights from such approaches. Here, we develop a set of nonlinearities for use in a neural network architecture that discovers features in the data which are directly interpretable in terms of physical observables. Applied to simulated snapshots produced by two candidate theories approximating the doped Fermi-Hubbard model, we uncover that the key distinguishing features are fourth-order spin-charge correlators. Our approach lends itself well to the construction of simple, versatile, end-to-end interpretable architectures, thus paving the way for new physical insights from machine learning studies of experimental and numerical data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Tailoring thermal insulation architectures from additive manufacturing

Abstract Tailoring thermal transport by structural parameters could result in mechanically fragile and brittle networks. An indispensable goal is to design hierarchical architecture materials that combine thermal and mechanical properties in a continuous and cohesive network. A promising strategy to create such a hierarchical network targets additive manufacturing of hybrid porous voxels at nanoscale. Here we describe the convergence of agile additive manufacturing of porous hybrid voxels to tailor hierarchically and mechanically tunable objects. In one strategy, the uniformly distributed porous silica voxels, which form the basis for the control of thermal transport, are non-covalently interfaced with polymeric networks, yielding hierarchic super-elastic architectures with thermal insulation properties. Another additive strategy for achieving mechanical strength involves the versatile orthogonal surface hybridization of porous silica voxels retains its low thermal conductivity of 19.1 mW m −1 K −1 , flexible compressive recovery strain (85%), and tailored mechanical strength from 71.6 kPa to 1.5 MPa. The printed lightweight high-fidelity objects promise thermal aging mitigation for lithium-ion batteries, providing a thermal management pathway using 3D printed silica objects.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Polymers with advanced architectures as emulsifiers for multi-functional emulsions

Emulsions have wide applications in the food, cosmetics, and pharmaceutical industries. Emulsions stabilized with polymers having different architectures, such as linear diblock and triblock copolymers, branched polymers, star polymers, graft polymers, and densely grafted bottlebrush polymers, have been used for encapsulation, delivery, and as templates for polymerization reactions. Furthermore, stimuli-responsive polymers have been used for on-demand demulsification and emulsion engineering. In this review, we highlight the developments in the past decade that used polymers with advanced architectures as multi-functional emulsifiers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Autonomous elemental characterization enabled by a low cost robotic platform built upon a generalized software architecture

Despite the rapidly growing applications of robots in industry, the use of robots to automate tasks in scientific laboratories is less prolific due to the lack of generalized methodologies and the high cost of hardware. This paper focuses on the automation of characterization tasks necessary for reducing cost while maintaining generalization and proposes a software architecture for building robotic systems in scientific laboratory environments. A dual-layer (Socket.IO and ROS) action server design is the basic building block, which facilitates the implementation of a web-based front end for user-friendly operation and the use of ROS Behavior Trees for convenient task planning and execution. A robotic platform for automating mineral and material sample characterization is built upon the architecture, with an open-source, low-cost three-axis computer numerical control gantry system serving as the main robot. A handheld laser induced breakdown spectroscopy (LIBS) analyzer is integrated with a 3D printed adapter, enabling (1) automated 2D chemical mapping and (2) autonomous sample measurement (with the support of an RGB-Depth camera). We demonstrate the utility of automated chemical mapping by scanning the surface of a spodumene-bearing pegmatite core sample with a 1071-point dense hyperspectral map acquired at a rate of 1520 bits per second. Furthermore, we showcase the autonomy of the platform in terms of perception, dynamic decision-making, and execution, through a case study of LIBS measurement of multiple mineral samples. The platform enables controlled and autonomous chemical quantification in the laboratory that complements field-based measurements acquired with the same handheld device, linking resource exploration and processing steps in the supply chain for lithium-based battery materials.

Cao, Xuan [Lawrence Berkeley National Laboratory (↗
DOE OSTI2026

Solid-state prealkylation of electrode architectures to tune solid electrolyte interphase composition

Efficient electrochemical cycling of certain Si anodes is limited by irreversible Li consumption to form and continually reform the solid electrolyte interface (SEI) due to Si expansion/contraction and fracture. Prelithiation can compensate for these losses; however, the starting open circuit potential (VOC) becomes highly reducing and, therefore, the electrolyte reduction chemistry that influences the SEI composition can change. Herein, we compare SEI formation for electrodes prelithiated using Solid State Prealkylation of Electrode Architectures (SPEAR) versus traditional electrochemically lithiated architectures (ECLAR), focusing on SEI compositional changes as a function of stoichiometry (0.28 ≤ x ≤ 1.38 in LixSi). Increasing SPEAR prelithiation decreased the initial VOC of Si anodes vs. Li/Li+ from ∼3 V (Li0.28Si) to < 0.5 V for Li1.38Si, enabling simultaneous competitive reduction of EC, EMC, and LiPF6 at low potentials. Ex situ7Li and 29Si cross-polarization NMR and XPS reveal that SPEAR drives thicker SEI formation with substantially increased P/F contributions and a predominantly inorganic insoluble SEI (71.4% inorganic for Li1.38Si), consistent with accelerated LiPF6-derived POx/LiPFx/LiF formation relative to ECLAR analogs which exhibit carbonate-rich organic SEI compositions. Symmetric-cell EIS further indicates SPEAR-specific impedance features consistent with pore reduction (filling) during LixSi formation. In full cells, SPEAR prelithiation increases the initial coulombic efficiency (ICE) and accelerates SEI formation and stabilization with Li1.38Si reaching 99.4% coulombic efficiency (CE) by cycle 2.

Musgrove, Amanda [ORNL] (ORCID:0009000220910389)↗
DOE OSTI2022

Distributed deep learning training using silicon photonic switched architectures

The scaling trends of deep learning models and distributed training workloads are challenging network capacities in today’s datacenters and high-performance computing (HPC) systems. We propose a system architecture that leverages silicon photonic (SiP) switch-enabled server regrouping using bandwidth steering to tackle the challenges and accelerate distributed deep learning training. In addition, our proposed system architecture utilizes a highly integrated operating system-based SiP switch control scheme to reduce implementation complexity. To demonstrate the feasibility of our proposal, we built an experimental testbed with a SiP switch-enabled reconfigurable fat tree topology and evaluated the network performance of distributed ring all-reduce and parameter server workloads. The experimental results show up to 3.6× improvements over the static non-reconfigurable fat tree. Our large-scale simulation results show that server regrouping can deliver up to 2.3× flow throughput improvement for a 2× tapered fat tree and a further 11% improvement when higher-layer bandwidth steering is employed. The collective results show the potential of integrating SiP switches into datacenters and HPC systems to accelerate distributed deep learning training.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Subspace recursive Fermi-operator expansion strategies for large-scale DFT eigenvalue problems on HPC architectures

Quantum mechanical calculations for material modeling using Kohn–Sham density functional theory (DFT) involve the solution of a nonlinear eigenvalue problem for N smallest eigenvector-eigenvalue pairs, with N proportional to the number of electrons in the material system. Here, these calculations are computationally demanding and have asymptotic cubic scaling complexity with the number of electrons. Large-scale matrix eigenvalue problems arising from the discretization of the Kohn–Sham DFT equations employing a systematically convergent basis traditionally rely on iterative orthogonal projection methods, which are shown to be computationally efficient and scalable on massively parallel computing architectures. However, as the size of the material system increases, these methods are known to incur dominant computational costs through the Rayleigh–Ritz projection step of the discretized Kohn–Sham Hamiltonian matrix and the subsequent subspace diagonalization of the projected matrix. This work explores the potential of polynomial expansion approaches based on recursive Fermi-operator expansion as an alternative to the subspace diagonalization of the projected Hamiltonian matrix to reduce the computational cost. Subsequently, we perform a detailed comparison of various recursive polynomial expansion approaches to the traditional approach of explicit diagonalization on both multi-node central processing unit and graphics processing unit architectures and assess their relative performance in terms of accuracy, computational efficiency, scaling behavior, and energy efficiency.

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

A hybrid neural architecture: Online attosecond x-ray characterization

The emergence of high-repetition-rate x-ray free-electron lasers (XFELs), such as SLAC’s LCLS-II, serves as our canonical example for autonomous controls that necessitate high-throughput diagnostics paired with streaming computational pipelines capable of single-shot analysis with extremely low latency. We present the deterministic characterization with an integrated parallelizable hybrid resolver architecture, a hybrid machine learning framework designed for fast, accurate analysis of XFEL diagnostics using angular streaking-based sinogram images. This architecture integrates convolutional neural networks and bidirectional long short-term memory models to denoise input, identify x-ray sub-spike features, and extract sub-spike relative delays with sub-30 attosecond temporal resolution. Deployed on low-latency hardware, it achieves over 10 kHz throughput with 168.3 μs inference latency, indicating scalability to 14 kHz with field-programmable gate array integration. By transforming regression tasks into classification problems and leveraging optimized error encoding, we achieve high precision with low-latency performance that is critical for real-time streaming event selection and experimental control feedback signals. This represents a key development in real-time control pipelines for next-generation autonomous science, generally, and high repetition-rate x-ray experiments in particular.

Accelerator Physics (physics.acc-ph)↗