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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

SBIR Technology Applications to Space Communications and Navigation (SCaN)

This slide presentation reviews the mission of the Space Communications and Navigation (SCaN) Office with particular emphasis on opportunities for technology development with SBIR companies. The SCaN office manages NASA's space communications and navigation networks: the Near Earth Network (NEN), the Space Network (SN), and the Deep Space Network (DSN). The SCaN networks nodes are shown on a world wide map and the networks are described. Two types of technologies are described: Pull technology, and Push technologies. A listing of technology themes is presented, with a discussion on Software defined Radios, Optical Communications Technology, and Lunar Lasercom Space Terminal (LLST). Other technologies that are being investigated are some Game Changing Technologies (GCT) i.e., technologies that offer the potential for improving comm. or nav. performance to the point that radical new mission objectives are possible, such as Superconducting Quantum Interference Filters, Silicon Nanowire Optical Detectors, and Auto-Configuring Cognitive Communications

Liebrecht, Phil↗
DOE OSTI2025

SA-GAT-SR: self-adaptable graph attention networks with symbolic regression for high-fidelity material property prediction

Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as powerful tools for high-throughput prediction of material properties, offering a compelling enhancement and alternative to traditional first-principles calculations. While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy, such approaches often lack physical interpretability and insights into materials behavior. Here, we introduce a novel computational paradigm—Self-Adaptable Graph Attention Networks integrated with Symbolic Regression (SA-GAT-SR)—that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression. Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintaining O(n) computational scaling. The integrated SR module subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships, achieving 23 × acceleration compared to conventional SR implementations that heavily rely on first-principle calculations-derived features as input. This work suggests a new framework in computational materials science, bridging the gap between predictive accuracy and physical interpretability, offering valuable physical insights into material behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Capturing Surface Coverage Effects in Heterogeneous Catalysis

Adsorbate–adsorbate lateral interactions at relevant surface coverages have a significant effect on chemical kinetics, thereby influencing the activity of a heterogeneous catalyst. Coverage-dependent kinetic and thermodynamic parameters therefore must be included in studies of such complex systems to properly predict the turnover frequencies and kinetic trends. Thus, it becomes extremely important to accurately capture the strength of lateral interactions between neighboring species under realistic reaction conditions. In this Perspective, we discuss the various existing computational and experimental methods for determining adspecies coverage and configurational effects. The choice of the tools and methods employed in such studies depends on factors such as time, length scales, computational cost, the presence of solvents, and reaction conditions. The applications of each method and the respective challenges are also discussed here. As a result, we discuss the recent developments and future of the state-of-the-art for inclusion of surface coverage and configuration into a holistic picture for accurate predictions of catalytic behavior.

09 BIOMASS FUELS↗
DOE OSTI2021

Entanglement and many-body effects in collective neutrino oscillations

Collective neutrino oscillations play a crucial role in transporting lepton flavor in astrophysical settings, such as supernovae, where the neutrino density is large. In this regime, neutrino-neutrino interactions are important and simulations in the mean-field approximation show evidence for collective oscillations occurring at timescales much shorter than those associated with vacuum oscillations. In this work, we study the out-of-equilibrium dynamics of a corresponding spin model using matrix product states and show how collective bipolar oscillations can be triggered by many-body correlations if appropriate initial conditions are present. We find entanglement entropies scaling at most logarithmically in the system size suggesting that classical tensor network methods could be efficient in describing collective neutrino dynamics more generally. These observation provide a clear path forward, not only to increase the accuracy of current simulations, but also to elucidate the mechanism behind collective flavor oscillations without resorting to the mean-field approximation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Nonreciprocal Supercurrents in a Field-Free Graphene Josephson Triode

Superconducting diodes are proposed non-reciprocal circuit elements that should exhibit nondissipative transport in one direction while being resistive in the opposite direction. Multiple examples of such devices have emerged in the past couple of years, however their efficiency is typically limited, and most of them require magnetic field to function. Here we present a device achieving efficiencies upwards of 90% while operating at zero field. Our samples consist of a network of three graphene Josephson junctions linked by a common superconducting island, to which we refer as a Josephson triode. The triode is tuned by applying a control current to one of the contacts, thereby breaking the time-reversal symmetry of the current flow. The triode’s utility is demonstrated by rectifying a small (tens of nA amplitude) applied square wave. Finally, we speculate that devices of this type could be realistically employed in the modern quantum circuits.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Refining the thermochemical properties of CF, SiF, and their cations by combining photoelectron spectroscopy, quantum chemical calculations, and the Active Thermochemical Tables approach

Fluorinated species have a pivotal role in semiconductor material chemistry and some of them have been detected beyond the Earth's atmosphere. Achieving good energy accuracy on fluorinated species using quantum chemical calculations has long been a challenge. In addition, obtaining direct experimental thermochemical quantities has also proved difficult. Here, we report the threshold photoelectron and photoion yield spectra of SiF and CF radicals generated with a fluorine reactor. The spectra were analysed with the support of ab initio calculations, resulting in new experimental values for the adiabatic ionisation energies of both CF (9.128 ± 0.006 eV) and SiF (7.379 ± 0.009 eV). Using these values, the underlying thermochemical network of Active Thermochemical Tables was updated, providing further refined enthalpies of formation and dissociation energies of CF, SiF, and their cationic counterparts.

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

Predicting nonequilibrium Green’s function dynamics and photoemission spectra via nonlinear integral operator learning

Understanding the dynamics of nonequilibrium quantum many-body systems is an important research topic in a wide range of fields across condensed matter physics, quantum optics, and high-energy physics. However, numerical studies of large-scale nonequilibrium phenomena in realistic materials face serious challenges due to intrinsic high-dimensionality of quantum many-body problems and the absence of time-invariance. The nonequilibrium properties of many-body systems can be described by the dynamics of the correlator, or the Green's function of the system, whose time evolution is given by a high-dimensional system of integro-differential equations, known as the Kadanoff–Baym equations (KBEs). The time-convolution term in KBEs, which needs to be recalculated at each time step, makes it difficult to perform long-time numerical simulation. In this paper, we develop an operator-learning framework based on recurrent neural networks (RNNs) to address this challenge. We utilize RNNs to learn the nonlinear mapping between Green's functions and convolution integrals in KBEs. By using the learned operators as a surrogate model in the KBE solver, we obtain a general machine-learning scheme for predicting the dynamics of nonequilibrium Green's functions. Besides significant savings per each time step, the new methodology reduces the temporal computational complexity from $O(N_t^3)$ to $O(N_t)$ where N t is the number of steps taken in a simulation, thereby making it possible to study large many-body problems which are currently infeasible with conventional KBE solvers. Through various numerical examples, we demonstrate the effectiveness of the operator-learning based approach in providing accurate predictions of physical observables such as the reduced density matrix and time-resolved photoemission spectra. Moreover, our framework exhibits clear numerical convergence and can be easily parallelized, thereby facilitating many possible further developments and applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Petz reconstruction in random tensor networks

We illustrate the ideas of bulk reconstruction in the context of random tensor network toy models of holography. Specifically, we demonstrate how the Petz reconstruction map works to obtain bulk operators from the boundary data by exploiting the replica trick. We also take the opportunity to comment on the differences between coarse-graining and random projections.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Identification of high-dielectric constant compounds from statistical design

Abstract The discovery of high-dielectric materials is crucial to increasing the efficiency of electronic devices and batteries. Here, we report three previously unexplored materials with very high dielectric constants (69 < ϵ < 101) and large band gaps (2.9 < E g (eV) < 5.5) obtained by screening materials databases using statistical optimization algorithms aided by artificial neural networks (ANN). Two of these new dielectrics are mixed-anion compounds (Eu 5 SiCl 6 O 4 and HoClO) and are shown to be thermodynamically stable against common semiconductors via phase diagram analysis. We also uncovered four other materials with relatively large dielectric constants (20 < ϵ < 40) and band gaps (2.3 < E g (eV) < 2.7). While the ANN training-data are obtained from the Materials Project, the search-space consists of materials from the Open Quantum Materials Database (OQMD)—demonstrating a successful implementation of cross-database materials design. Overall, we report the dielectric properties of 17 materials calculated using ab initio calculations, that were selected in our design workflow. The dielectric materials with high-dielectric properties predicted in this work open up further experimental research opportunities.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Imaging real-space flat band localization in kagome magnet FeSn

Kagome lattices host flat bands due to their frustrated lattice geometry, which leads to destructive quantum interference of electron wave functions. Here, we report imaging of the kagome flat band localization in real-space using scanning tunneling microscopy. We identify both the Fe 3 Sn kagome lattice layer and the Sn 2 honeycomb layer with atomic resolution in kagome antiferromagnet FeSn. On the Fe 3 Sn lattice, at the flat band energy determined by the angle resolved photoemission spectroscopy, tunneling spectroscopy detects an unusual state localized uniquely at the Fe kagome lattice network. We further show that the vectorial in-plane magnetic field manipulates the spatial anisotropy of the localization state within each kagome unit cell. Our results are consistent with the real-space flat band localization in the magnetic kagome lattice. We further discuss the magnetic tuning of flat band localization under the spin–orbit coupled magnetic kagome lattice model.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Predictive understanding of the surface tension and velocity of sound in ionic liquids using machine learning

Knowledge of the physical properties of ionic liquids (ILs), such as the surface tension and speed of sound, is important for both industrial and research applications. Unfortunately, technical challenges and costs limit exhaustive experimental screening efforts of ILs for these critical properties. Previous work has demonstrated that the use of quantum-mechanics-based thermochemical property prediction tools, such as the conductor-like screening model for real solvents, when combined with machine learning (ML) approaches, may provide an alternative pathway to guide the rapid screening and design of ILs for desired physiochemical properties. However, the question of which machine-learning approaches are most appropriate remains. In the present study, we examine how different ML architectures, ranging from tree-based approaches to feed-forward artificial neural networks, perform in generating nonlinear multivariate quantitative structure–property relationship models for the prediction of the temperature- and pressure-dependent surface tension of and speed of sound in ILs over a wide range of surface tensions (16.9–76.2 mN/m) and speeds of sound (1009.7–1992 m/s). The ML models are further interrogated using the powerful interpretation method, shapley additive explanations. We find that several different ML models provide high accuracy, according to traditional statistical metrics. The decision tree-based approaches appear to be the most accurate and precise, with extreme gradient-boosting trees and gradient-boosting trees being the best performers. However, our results also indicate that the promise of using machine-learning to gain deep insights into the underlying physics driving structure–property relationships in ILs may still be somewhat premature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2002

Novel High Efficient Organic Photovoltaic Materials: Appendix for Summary of Research

There are many different kinds of conjugated polymers that may be useful in photovoltaic devices. So far, the most popular and successful conjugated polymers used in photovoltaic devices include poly(1,4-)phenylenevinylenes (PPV), C60 and their derivatives. The discovery of electro-luminescence in PPV has stimulated a great deal of interest in developing "plastic" solid-state semiconductor devices. The overall synthetic methodology for the preparation of PPV can be divided into three main categories: (1) side chain derivatization, (2) precursor approach, and (3) in-situ polymerization. In this project, the first method was adopted. As discussed in project proposal and literatures, the overall efficiency of photovoltaic devices containing conjugated polymers is determined by the materials ability to generate excitons from incoming radiation, and then to separate the charges at donor/acceptor interfaces, and then to transport charges to respective electrodes. Given that effective exciton diffusion range are typical less then 30 nm, unique morphological structures are needed. This need led to several research groups to the idea that interpenetrating or bi-continuous networks of donor (electron donating) and acceptor (electron withdrawing) polymers should give better results. One approach involved the use of functionalized PPV. The attachment of electron withdrawing cyano groups to a PPV forms the CN-PPV, making it a strong electron acceptor. Underivatized PPV is a generally considered a hole-transporting material. Using blends of MEH-PPV, a soluble donor PPV derivative, as a hole transporter and CN-PPV as an electron transporter, a quantum efficiencies of up to 6% was achieved.

Sun, Sam↗
DOE OSTI2023

Machine learning the spectral function of a hole in a quantum antiferromagnet

Understanding charge motion in a background of interacting quantum spins is a fundamental problem in quantum many-body physics. The most extensively studied model for this problem is the so-called t-t'-t''-J model, where the determination of the parameter t' in the context of cuprate superconductors is challenging. Here we present a theoretical study of the spectral functions of a mobile hole in the t-t'-t''-J model using two machine-learning techniques: K-nearest neighbor regression (KNN) and a feed-forward neural network (FFNN). We employ the self-consistent Born approximation to generate a dataset of about 1.3 x 10 5 spectral functions. Here we show that, for the forward problem, both methods allow for the accurate and efficient prediction of spectral functions, allowing, e.g., rapid searches through parameter space. Furthermore, we find that for the inverse problem (inferring Hamiltonian parameters from spectra), the FFNN can, but the KNN cannot, accurately predict the model parameters using merely the density of states. Our results suggest that it may be possible to use deep-learning methods to predict materials parameters from experimentally measured spectral functions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2009

NASA Tech Briefs, May 2009

Topics covered include: Valve-"Health"-Monitoring System; Microstrip Antenna for Remote Sensing of Soil Moisture and Sea Surface Salinity; Biomedical Wireless Ambulatory Crew Monitor; Wireless Avionics Packet to Support Fault Tolerance for Flight Applications; Aerobot Autonomy Architecture; Submillimeter Confocal Imaging Active Module; Traveling-Wave Maser for 32 GHz; System Synchronizes Recordings from Separated Video Cameras; Piecewise-Planar Parabolic Reflectarray Antenna; Reducing Interference in ATC Voice Communication; EOS MLS Level 1B Data Processing, Version 2.2; Auto-Generated Semantic Processing Services; Geospatial Authentication; Maneuver Automation Software; Event Driven Messaging with Role-Based Subscriptions; Estimating Relative Positions of Outer-Space Structures; Fabricating PFPE Membranes for Capillary Electrophoresis; Linear Actuator Has Long Stroke and High Resolution; Installing a Test Tap on a Metal Battery Case; Fabricating PFPE Membranes for Microfluidic Valves and Pumps; Room-Temperature-Cured Copolymers for Lithium Battery Gel Electrolytes; Catalysts for Efficient Production of Carbon Nanotubes; Amorphous Silk Fibroin Membranes for Separation of CO2; "Zero-Mass" Noninvasive Pressure Transducers; Radial-Electric-Field Piezoelectric Diaphragm Pumps; Ejector-Enhanced, Pulsed, Pressure-Gain Combustor; Suppressing Ghost Diffraction in E-Beam-Written Gratings; Target-Tracking Camera for a Metrology System; Polarimetric Imaging using Two Photoelastic Modulators; Miniature Wide-Angle Lens for Small-Pixel Electronic Camera; Modal Filters for Infrared Interferometry; Mo(3)Sb(7-x)Te(x) for Thermoelectric Power Generation; Two-Dimensional Quantum Model of a Nanotransistor; Scanning Miniature Microscopes without Lenses; Manipulating Neutral Atoms in Chip-Based Magnetic Traps; Expansion Compression Contacts for Thermoelectric Legs; Processing Electromyographic Signals to Recognize Words; Physical Principle for Generation of Randomness; DSN Beowulf Cluster-Based VLBI Correlator; Hybrid NN/SVM Computational System for Optimizing Designs; Criteria for Modeling in LES of Multicomponent Fuel Flow; Computerized Machine for Cutting Space Shuttle Thermal Tiles; Orbiting Depot and Reusable Lander for Lunar Transportation; FPGA-Based Networked Phasemeter for a Heterodyne Interferometer; Aquarius Digital Processing Unit; Three-Dimensional Optical Coherence Tomography; Benchtop Antigen Detection Technique using Nanofiltration and Fluorescent Dyes; Isolation of Precursor Cells from Waste Solid Fat Tissue; Identification of Bacteria and Determination of Biological Indicators; Further Development of Scaffolds for Regeneration of Nerves; Chemically Assisted Photocatalytic Oxidation System; Use of Atomic Oxygen for Increased Water Contact Angles of Various Polymers for Biomedical Applications; Crashworthy Seats Would Afford Superior Protection; Open-Access, Low-Magnetic-Field MRI System for Lung Research; Microfluidic Mixing Technology for a Universal Health Sensor; Microfluidic Extraction of Biomarkers using Water as Solvent; Microwell Arrays for Studying Many Individual Cells; Droplet-Based Production of Liposomes; and Identifying and Inactivating Bacterial Spores

Source record↗
DOE OSTI2026

Constraining Hamiltonians from chiral effective field theory with neutron-star data

Multi-messenger observations of neutron stars (NSs) and their mergers have placed strong constraints on the dense-matter equation of state (EOS). The EOS, in turn, depends on microscopic nuclear interactions that are described by nuclear Hamiltonians. These Hamiltonians are commonly derived within chiral effective field theory (EFT). Ideally, multi-messenger observations of NSs could be used to directly inform our understanding of EFT interactions, but such a direct inference necessitates millions of model evaluations. This is computationally prohibitive because each evaluation requires us to calculate the EOS from a Hamiltonian by solving the quantum many-body problem with methods such as auxiliary-field diffusion Monte Carlo (AFDMC), which provides very accurate and precise solutions but at a significant computational cost. Additionally, we need to solve the stellar structure equations for each EOS which further slows down each model evaluation by a few seconds. In this work, we combine emulators for AFDMC calculations of neutron matter, built using parametric matrix models, and for the stellar structure equations, built using multilayer perceptron neural networks, with the PyCBC data-analysis framework to enable a direct inference of coupling constants in an EFT Hamiltonian using multi-messenger observations of NSs. We find that astrophysical data can provide informative constraints on two-nucleon couplings despite the high densities probed in NS interiors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Maximizing quantum enhancement in axion dark matter experiments

We provide a comprehensive comparison of linear amplifiers and microwave photon counters in axion dark matter experiments. The study is done assuming a range of realistic operating conditions and detector parameters, over the frequency range between 1 and 30 GHz. As expected, photon counters are found to be advantageous under low background, at high frequencies (𝜈 >5 GHz), if they can be implemented with robust wide-frequency tuning or a very low dark count rate. Additional noteworthy observations emerging from this study include: (1) an expanded applicability of off-resonance photon background reduction, including the single-quadrature state squeezing, for scan rate enhancements; (2) a much broader appeal for operating the haloscope resonators in the overcoupling regime, up to 𝛽 ∼10; (3) the need for a detailed investigation into the cryogenic and electromagnetic conditions inside haloscope cavities to lower the photon temperature for future experiments; (4) the necessity to develop a distributed network of coupling ports in high-volume axion haloscopes to utilize these potential gains in the scan rate.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Advances in coherent coupling between magnons and acoustic phonons

The interaction between magnetic and acoustic excitations have recently inspired many interdisciplinary studies ranging from fundamental physics to circuit implementation. Specifically, the exploration of their coherent interconversion enabled via the magnetoelastic coupling opens a new playground combining straintronics and spintronics, and provides a unique platform for building up on-chip coherent information processing networks with miniaturized magnonic and acoustic devices. In this Perspective, we will focus on the recent progress of magnon-phonon coupled dynamic systems, including materials, circuits, imaging and new physics. In particular, we highlight the unique features such as nonreciprocal acoustic wave propagation and strong coupling between magnons and phonons in magnetic thin-film systems, which provides a unique platform for their coherent manipulation and transduction. We will also review the frontier of surface acoustic wave resonators in coherent quantum transduction and discuss how the novel acoustic circuit design can be applied in microwave spintronics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Point-particle drag, lift, and torque closure models using machine learning: Hierarchical approach and interpretability

Developing deterministic neighborhood-informed point-particle closure models using machine learning has garnered interest recently from the dispersed multiphase flow community. The robustness of neural models for this complex multibody problem is hindered by the availability of particle-resolved data. Here, the present work addresses this unavoidable limitation of data paucity by implementing two strategies: (1) by using a rotation and reflection equivariant neural network and (2) by pursuing a physics-based hierarchical machine learning approach. The resulting machine-learned models are observed to achieve a maximum accuracy of 85% and 96% in the prediction of neighbor-induced force and torque fluctuations, respectively, for a wide range of Reynolds number and volume fraction conditions considered. Furthermore, we pursue force and torque network architectures that provide universal prediction spanning a wide range of Reynolds number (0.25 ≤ Re ≤250) and particle volume fraction (0 ≤ φ ≤0.4). The hierarchical nature of the approach enables improved prediction of quantities such as streamwise torque, by going beyond binary interactions to include trinary interactions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗