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Results for “FINGERPRINT”
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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Fingerprinting Brownian Motions of Polymers under Flow
Here, we present a quantitative approach to the self-dynamics of polymers under steady flow by employing a set of complementary reference frames and extending the spherical harmonic expansion technique to dynamic density correlations. Application of this method to nonequilibrium molecular dynamics simulations of polymer melts reveals a number of universal features. For both unentangled and entangled melts, the center-of-mass motions in the flow frame are described by superdiffusive, anisotropic Gaussian distributions, whereas the isotropic component of monomer self-dynamics in the center-of-mass frame is strongly suppressed. Spatial correlation analysis shows that the heterogeneity of monomer self-dynamics increases significantly under flow.
Identifying the fingerprints of topological states by tuning magnetoresistance in a semimetal: The case of topological half-Heusler Pt 1 - x Au x LuSb
We report that topological materials often exhibit remarkably linear nonsaturating magnetoresistance (LMR), which is both of scientific and technological importance. However, the role of topologically nontrivial states in the emergence of such a behavior has eluded clear demonstration in experiments. Here, by reducing the coupling between the topological surface states (TSS) and the bulk carriers, we controllably tune the LMR behavior in Pt 1-x Au x LuSb into distinct plateaus in Hall resistance, which we show arise from a quantum Hall phase. This allowed us to reveal how smearing of the Landau levels, which otherwise gives rise to a quantum Hall phase, results in an LMR behavior due to strong interaction between the TSS with a positive g factor and the bulk carriers. We establish that controlling the coupling strength between the surface and the bulk carriers in topological materials can bring about dramatic changes in their magnetotransport behavior. In addition, our work outlines a strategy to reveal macroscopic physical observables of TSS in compounds with a semimetallic bulk band structure, as is the case in multifunctional Heusler compounds, thereby opening up opportunities for their utilization in hybrid quantum structures.
Origins versus fingerprints of the Jahn-Teller effect in d -electron A B X 3 perovskites
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Spectroscopic fingerprints of gapped quantum spin liquids, both conventional and fractonic
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Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats
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Fingerprinting Anomalous Computation with RNN for GPU-accelerated HPC Machines
This paper presents a workload classification framework that discriminates illicit computation from authorized workloads on GPU-accelerated HPC systems. As such systems become more and more powerful, they are exploited by attackers to run malicious and for-profit programs that typically require extremely high computing ability to be successful. Our classification framework leverages the distinctive signatures between illicit and authorized workloads, and explore machine learning methods to learn the workloads and classify them. The framework uses lightweight, non-intrusive workload profiling to collect model input data, and explores multiple machine learning methods, particularly recurrent neural network (RNN) that is suitable for online anomalous workload detection. Evaluation results on four generations of GPU machines demonstrate that the workload classification framework can tell apart the illicit authorized workloads with a high accuracy of over 95%.
CPS Device-Class Identification via Behavioral Fingerprinting: From Theory to Practice
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Fingerprinting diverse nanoporous materials for optimal hydrogen storage conditions using meta-learning
A meta-learning model predicts the optimal hydrogen storage conditions and selects from a diverse set of nanoporous materials.
Decoding the interstitial/vacancy nature of dislocation loops with their morphological fingerprints in face-centered cubic structure
Dislocation loops are critical defects inducing detrimental effects like embrittlement and swelling in materials under irradiation. Distinguishing their nature (interstitial- or vacancy-type) is a long-standing challenge with great implications for understanding radiation damage. Here, we demonstrate that the morphology of radiation-induced Frank loops can unveil their nature in face-centered cubic (fcc) structure: Circular loops are interstitial-type in all fcc materials, while segmented loops are vacancy-type in high stacking fault energy (SFE) alloys but varied-type in low SFE and high-entropy alloys. The polygonal shape is attributed to the dissociation of an a 0 /3<111> dislocation into an a 0 /6<112> Shockley partial and an a 0 /6<110> stair-rod dislocation. The dissociation of vacancy loops is energetically favorable, whereas interstitial loops require external stimuli to promote dislocation propagation. This “morphology-nature” correlation not only highlights the asymmetry of vacancy/interstitial loops but also offers an efficient way to distinguish loop nature for a wide range of materials.
Fingerprinting Redox Heterogeneity in Electrodes during Extreme Fast Charging
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ALFABET™ and nfp (Neural Fingerprint) [SWR-19-13]
Keras layers for end-to-end learning on molecular structure. Based on Keras, Tensorflow, and RDKit. Source code used in the study Message-passing neural networks for high-throughput polymer screening. SEE ALSO https://github.com/NREL/alfabet
Enhancing internet of things security using entropy-informed RF-DNA fingerprint learning from Gabor-based images
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Annual Report X-GAMMA: Revealing the Nuclear Fingerprint with Cryogenic Detectors
Technical work in the area of gamma-ray spectroscopy based on ultra-high resolution microcalorimeter spectrometers has focused on three areas: (1) In depth study of the sources of systematic uncertainty, (2) hardware upgrades to increase the stability and speed of spectrometer operation, and (3) measuring plutonium-containing SNM samples to test the hardware system and to inform further development of our spectral data analysis tools and methods.
Climate Signatures Fingerprints of a Dynamic Planet
Summary of Los Alamos National Laboratories' research goals relating to climate science.
Dr. Ian Hutcheon Award Spectroscopic Fingerprinting of Vapor-Phase Uranium Species
The Dr. Ian Hutcheon Post-Doctoral Fellowship provides resources to fund a TNFapplicable research project as well as promote mentorship, career development, and relevant training for the recipient. The two-year proposal incorporated a research plan to investigate uranium chemistry relevant to post-detonation nuclear forensics and a mentorship plan to address career development for the recipient in TNF-relevant areas.
Nuclear forensics: The fingerprints of mass destruction [PowerPoint]
The presentation focuses on pre- and post-detonation nuclear forensics and the various roles it plays.
Dr. Ian Hutcheon Award: Spectroscopic Fingerprinting of Vapor-Phase Uranium Species
The Dr. Ian Hutcheon Post-Doctoral Fellowship provides resources to fund a TNFapplicable research project as well as promote mentorship, career development, and relevant training for the recipient. The two-year proposal incorporated a research plan to investigate uranium chemistry relevant to post-detonation nuclear forensics and a mentorship plan to address career development for the recipient in TNF-relevant areas.