Search NASA⌕ Search

SEARCH · Search NASA

Results for “correlation screening”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Strength mechanisms and tunability in Al-Ce-Mg ternary alloys enabled by additive manufacturing

Al-Ce-based alloys are promising candidates for additive manufacturing (AM) due to their hot-cracking resistance and because they do not require heat treatment to obtain precipitation strengthening. Rapid solidification rates enabled by AM methods can lead to enhanced mechanical properties; however, the strengthening mechanisms over large composition ranges were unclear. Here, combinatorial synthesis by directed-energy deposition (DED) and hardness measurements were used to rapidly map the composition-dependent strength of the ternary Al-Ce-Mg system. Tensile testing and microstructure characterization of selected compositions were performed to elucidate the compositional dependence of the strengthening mechanisms. Al 11 Ce 3 precipitates were present in all cases, and the maximum hardness (1.25 GPa) was measured for the Al-8Ce-10Mg composition. A combination of (i) Hall-Petch strengthening, based on the FCC-matrix-phase cell size; (ii) particle strengthening, based on Al 11 Ce 3 volume fraction and size; and (iii) solid-solution strengthening, based on Mg composition of the matrix phase, were used to account for the measured strengths. Vickers hardness is shown to correlate well with ultimate tensile strength in these alloys, highlighting the value of surface-based techniques for rapid screening.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Accelerating the Discovery of New, Single Phase High Entropy Ceramics via Active Learning

High-entropy ceramics have garnered interest due to their remarkable hardness, compressive strength, thermal stability, and fracture toughness; yet the discovery of new high-entropy ceramics (out of a tremendous number of possible elemental permutations) still largely requires costly, inefficient, trial-and-error experimental and computational approaches. The entropy forming ability (EFA) factor was recently proposed as a computational descriptor that positively correlates with the likelihood that a 5-metal high-entropy carbide (HECs) will form the desired single phase, homogeneous solid solution; however, discovery of new compositions is computationally expensive. If you consider 8 candidate metals, the HEC EFA approach uses 49 optimizations for each of the 56 unique 5-metal carbides, requiring a total of 2744 costly density functional theory calculations. Here, we describe an orders-of-magnitude more efficient active learning (AL) approach for identifying novel HECs. To begin, we compared numerous methods for generating composition-based feature vectors (e.g., magpie and mat2vec), deployed an ensemble of machine learning (ML) models to generate an average and distribution of predictions, and then utilized the distribution as an uncertainty. Here we then deployed an AL approach to extract new training data points where the ensemble of ML models predicted a high EFA value or was uncertain of the prediction. Our approach has the combined benefit of decreasing the amount of training data required to reach acceptable prediction qualities and biases the predictions toward identifying HECs with the desired high EFA values, which are tentatively correlated with the formation of single phase HECs. Using this approach, we increased the number of 5-metal carbides screened from 56 to 15,504, revealing 4 compositions with record-high EFA values that were previously unreported in the literature. Our AL framework is also generalizable and could be modified to rationally predict optimized candidate materials/combinations with a wide range of desired properties (e.g., mechanical stability, thermal conductivity).

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine Learning-Guided Identification of PET Hydrolases from Natural Diversity

The enzymatic depolymerization of poly(ethylene terephthalate) (PET) is emerging as a leading chemical recycling technology for waste polyester. As part of this endeavor, new candidate enzymes identified from natural diversity can serve as useful starting points for enzyme evolution and engineering. In this study, we improved upon HMM searches by applying an iterative machine learning strategy to identify 400 putative PET-degrading enzymes (PET hydrolases) from naturally occurring homologs. Using high-throughput (HTP) experimental techniques, we successfully expressed and purified >200 enzyme candidates and assayed them for PET hydrolysis activity as a function of pH, temperature, and substrate crystallinity. From this library, we discovered 91 previously unknown PET hydrolases, 35 of which retain activity at pH 4.5 on crystalline material, which are conditions relevant to developing more efficient commercial processes. Notably, four enzymes showed equal to or higher activity than LCC-ICCG, a benchmark PET hydrolase, at this challenging condition in our screening assay, and 11 of which have pH optima <7. Using these data, we identified regions of PETases statistically correlated to activity at lower pH. We additionally investigated the effect of condition-specific activity data on trained machine learning predictors and found a precision (putative hit rate) improvement of up to 30% compared to a Hidden Markov Model alone. Our findings show that by pointing enzyme discovery toward conditions of interest with multiple rounds of experimental and machine learning, we can discover large sets of active enzymes and explore factors associated with activity at those conditions.

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

Topological flat bands in a family of multilayer graphene moiré lattices

Moiré materials host a wealth of intertwined correlated and topological states of matter, all arising from flat electronic bands with nontrivial quantum geometry. A prominent example is the family of alternating-twist magic-angle graphene stacks, which exhibit symmetry-broken states at rational fillings of the moiré band and superconductivity close to half filling. Here, we introduce a second family of twisted graphene multilayers made up of twisted sheets of M- and N-layer Bernal-stacked graphene flakes. Calculations indicate that applying an electric displacement field isolates a flat and topological moiré conduction band that is primarily localized to a single graphene sheet below the moiré interface. Phenomenologically, the result is a striking similarity in the hierarchies of symmetry-broken phases across this family of twisted graphene multilayers. Our results show that this family of structures offers promising new opportunities for the discovery of exotic new correlated and topological phenomena, enabled by using the layer number to fine tune the flat moiré band and its screening environment.

Electronic properties and materials↗
DOE OSTI2015

Quantum entanglement for helium atom in the Debye plasmas

In the present work, we present an investigation on quantum entanglement of the two-electron helium atom immersed in weakly coupled Debye plasmas, modeled by the Debye-Hückel, or screened Coulomb, potential to mimic the interaction between two charged particles inside the plasma. Quantum entanglement is related to correlation effects in a multi-particle system. In a bipartite system, a measurement made on one of the two entangled particles affects the outcome of the other particle, even if such two particles are far apart. Employing wave functions constructed with configuration interaction B-spline basis, we have quantified von Neumann entropy and linear entropy for a series of He {sup 1,3}S{sup e} and {sup 1,3}P{sup o} states in plasma-embedded helium atom.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

High-throughput, dynamic, multi-dimensional: an expanding repertoire of plant respiration measurements

A recent burst of technological innovation and adaptation has greatly improved our ability to capture respiration rate data from plant sources. At the tissue level, several independent respiration measurement options are now available, each with distinct advantages and suitability, including high-throughput sampling capacity. These advancements facilitate the inclusion of respiration rate data into large-scale biological studies such as genetic screens, ecological surveys, crop breeding trials, and multi-omics molecular studies. As a result, our understanding of the correlations of respiration with other biological and biochemical measurements is rapidly increasing. Difficult questions persist concerning the interpretation and utilization of respiration data; concepts such as allocation of respiration to growth versus maintenance, the unnecessary or inefficient use of carbon and energy by respiration, and predictions of future respiration rates in response to environmental change are all insufficiently grounded in empirical data. However, we emphasize that new experimental designs involving novel combinations of respiration rate data with other measurements will flesh-out our current theories of respiration. Furthermore, dynamic recordings of respiration rate, which have long been used at the scale of mitochondria, are increasingly being used at larger scales of size and time to reflect processes of cellular signal transduction and physiological response to the environment. Herein, we also highlight how respiratory methods are being better adapted to different plant tissues including roots and seeds, which have been somewhat neglected historically.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Electrical conductivity of a warm neutron star crust in magnetic fields: Neutron-drip regime

We compute the anisotropic electrical conductivity tensor of the inner crust of a compact star at nonzero temperature by extending a previous work on the conductivity of the outer crust. The physical scenarios, where such crust is formed, involve protoneutron stars born in supernova explosions, binary neutron star mergers, and accreting neutron stars. The temperature-density range studied covers the transition from a semidegenerate to a highly degenerate electron gas and assumes that the nuclei form a liquid, i.e., the temperature is above the melting temperature of the lattice of nuclei. The electronic transition probabilities include (i) the screening of electron-ion interaction in the hard-thermal-loop approximation for the QED plasma, (ii) the correlations of the ionic component in a one-component plasma, and (iii) finite nuclear size effects. The conductivity tensor is obtained from the Boltzmann kinetic equation in relaxation time approximation accounting for the anisotropy introduced by a magnetic field. The sensitivity of the results towards the matter composition of the inner crust is explored by using several compositions of the inner crust, which were obtained using different nuclear interactions and methods of solving the many-body problem. The standard deviations of relaxation time and components of the conductivity tensor from the average are below ≤25% except close to crust-core transition, where nonspherical nuclear structures are expected. Finally, our results can be used in dissipative magnetohydrodynamics simulations of warm compact stars.

Physics↗
DOE OSTI2023

Mixed Ionic Electronic Conducting Quaternary Perovskites: Materials by Design for Solar Thermochemical Hydrogen

The innovative research conducted by Arizona State University and Princeton University in the project "Mixed Ionic-Electronic Conducting Quaternary Perovskites: Materials by Design for Solar Thermochemical Hydrogen" marks a significant stride forward in thermochemical water splitting. Through an intricate blend of computational design and experimental validation, the project delved into the promising potential of Mixed Ionic Electronic Conducting (MIEC) perovskites. These complex materials, characterized by their unique redox-active nature and adaptability in stoichiometry, present a promising frontier for efficient solar thermochemical hydrogen production. Firstly, the research enhanced the science by utilizing state-of-the-art computational methodologies to unravel the nuanced chemical potentials of MIEC perovskites. By simulating various off-stoichiometric scenarios and redox conditions, the team was able to predict material behaviors under diverse environmental conditions, a feat unachievable through conventional experimental methodologies alone. This approach not only fast-tracks the material screening process, significantly reducing the time from laboratory re-search to practical application, but also uncovers trends and correlations that are pivotal for future materials innovation. Regarding technical effectiveness, the project stands out in its economic feasibility. Traditional methods of materials discovery are often marred by high costs and extensive timeframes, owing to the iterative nature of experimental processes. However, by employing theoretical computations and validating these findings with targeted experiments, the project introduced a cost-effective paradigm for materials discovery and the first ever prediction, synthesis, and preliminary validation of a material solely from computational and theoretical considerations. This synergy between computation and experimentation expedites the discovery of optimal materials conducive to high-efficiency solar-to-hydrogen conversion processes. Furthermore, the public stands to benefit substantially from this research. The success of MIEC perovskites in solar thermochemical applications heralds a shift towards lower cost and lower electricity input for clean hydrogen production, hence potentially impacting climate and energy resilience. By improving the efficiency of solar-to-hydrogen conversions, the research paves the way for reduced dependency on fossil fuels, addressing the urgent global need for accessible and renewable energy sources. Moreover, the project's advancements contribute to scientific literacy in renewable energy technologies, empowering society through knowledge and spurring future innovations. In essence, this research project demonstrates significant progress in the realm of advanced water splitting through solar thermochemistry. Through its groundbreaking approaches in computational materials science and its implications for real-world applications, it holds the promise of a cleaner, more energy-resilient future.

08 HYDROGEN↗
NASA NTRS2015

Schottky Diode Derating for Survivability in a Heavy Ion Environment

In this paper, we irradiate a number of silicon power Schottky diodes from a variety of manufacturers. The tested diodes represent a wide assortment of reverse voltages and forward currents. Additionally, we review correlations between single-event failures in Schottky diodes and device electrical parameters. The spatial locations of failures in the diode are discussed, as well as a possible explanation for why the failures occur. Based on these correlations to date, we propose a derating scheme for Schottky diodes flown in a heavy ion environment and suggest screening procedures for decreasing the risks of such failures.

Radiation effects↗
DOE OSTI2023

Right band gaps for the right reason at low computational cost with a meta-GGA

In density functional theory, traditional explicit density functionals such as the local density approximation and generalized gradient approximations cannot accurately predict the band gap of solids for a fundamental reason: They lack the exchange-correlation derivative discontinuity. By comparing Kohn-Sham and generalized Kohn-Sham calculations, we here show that the nonempirical meta-generalized-gradient-approximation (meta-GGA) TASK from Aschebrock and Kümmel [Phys. Rev. Res. 1, 033082 (2019)] predicts the right gaps for the right reason, i.e., as a combination of a proper Kohn-Sham gap and a substantial derivative discontinuity contribution. For many materials from small-gap semiconductors to large-gap insulators, the proper band gap is thus obtained. Here we further study a group of metal-halide perovskites for which the band gap is notoriously hard to predict. For these materials, TASK yields band gaps very similar to the nonlocal screened hybrid Heyd-Scuseria-Ernzerhof functional, yet at a fraction of the hybrid functional’s computational cost. We discuss the influence of correlation functionals, and open questions in the comparison of calculated band gaps with experimental ones.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Sub‐Diffraction Correlation of Quantum Emitters and Local Strain Fields in Strain‐Engineered WSe 2 Monolayers

Strain-engineering in atomically thin metal dichalcogenides is a useful method for realizing single-photon emitters (SPEs) for quantum technologies. Correlating SPE position with local strain topography is challenging due to localization inaccuracies from the diffraction limit. Currently, SPEs are assumed to be positioned at the highest strained location and are typically identified by randomly screening narrow-linewidth emitters, of which only a few are spectrally pure. In this work, hyperspectral quantum emitter localization microscopy is used to locate 33 SPEs in nanoparticle-strained WSe 2 monolayers with sub-diffraction-limit resolution (≈30 nm) and correlate their positions with the underlying strain field via image registration. In this system, spectrally pure emitters are not concentrated at the highest strain location due to spectral contamination; instead, isolable SPEs are distributed away from points of peak strain with an average displacement of 240 nm. These observations point toward a need for a change in the design rules for strain-engineered SPEs and constitute a key step toward realizing next-generation quantum optical architectures.

2D material↗
DOE OSTI2020

Protein Molecular Dynamics Simulations with Approximate QM: What Can We Learn?

Classical force fields are essential for computer simulations of proteins and are typically parameterized to reproduce secondary and tertiary structure of isolated proteins. However, while protein-protein interactions are ubiquitous in nature, they are not considered in parameterization efforts and are far less understood than isolated proteins. A better characterization of intermolecular interactions is widely recognized as a key to revolutionizing drug and therapeutic developments with high-throughput computational screening. Urgently needed is a critical assessment of the performance of modern protein force fields against first-principles electronic structure methods and experiments. In a daring step toward this goal, we here describe a comparison of peptide folding dynamics as predicted by a molecular mechanics force field on the one hand and by an approximate electronic structure quantum mechanical (QM) method based on density-functional tight-binding (DFTB) on the other. We further compare the dynamics from straightforward DFTB simulations with a near-linear scaling version of DFTB for massively parallel computation based on the fragment molecular orbital (FMO-DFTB) method. We illustrate differences between the phenomenology of the folding dynamics from these three methods for a small model peptide, as well as charge polarization and dynamic fluctuations, point out possible correlations and implications for force field developers, and discuss the lessons learned that might become applicable to future predictive high-throughput computer screening for personalized neoantigen cancer therapy.

Irle, Stephan↗
DOE OSTI2021

The chemical and physical origin of incineration ash reactivity in cementitious systems

Incorporating industrial byproducts and waste in concrete is the key to reducing landfill usage as well as lowering the environmental footprint of cement industry. An emerging industrial byproduct which can partly replace cement is the Municipal Solid Waste Incinerator Ash (MSWI ash: residue that is left after incineration of municipal solid waste in a Waste-to-Energy facility). These ashes are predominantly calcium-rich; however, they also contain additional elements whose speciation is not known. These elements can significantly alter the hydration characteristics of a cementitious system. Here, our results suggest these ashes can accelerate as well as retard cement hydration. Specifically, Pb, Br, S, Ca, and Cl appear to accelerate cement hydration, whereas Cu, Fe, Al, Ti, Si, K, Zn, and Sr appear to retard cement hydration. Changes in hydration characteristics can have a strong bearing on the physical characteristics of cementitious systems incorporating incineration ashes. Thus, to selectively screen ashes that synergistically enhance the physical characteristics, we introduce a novel “Incineration Ash Coefficient (IAC),” which shows a strong correlation with the compressive strength (R 2 =0.79) of cement-ash binary mixtures.

42 ENGINEERING↗
DOE OSTI2019

Predicting Adsorption Properties of Catalytic Descriptors on Bimetallic Nanoalloys with Site-Specific Precision

Bimetallic nanoparticles present a vastly tunable structural and compositional design space rendering them promising materials for catalytic and energy applications. Yet it remains an enduring challenge to efficiently screen candidate alloys with atomic level specificity while explicitly accounting for their inherent stabilities under reaction conditions. Herein, by leveraging correlations between binding energies of metal adsorption sites and metal–adsorbate complexes, we predict adsorption energies of typical catalytic descriptors (OH*, CH 3 *, CH*, and CO*) on bimetallic alloys with site-specific resolution. We demonstrate that our approach predicts adsorption energies on top and bridge sites of bimetallic nanoparticles having generic morphologies and chemical environments with errors between 0.09 and 0.18 eV. By forging a link between the inherent stability of an alloy and the adsorption properties of catalytic descriptors, we can now identify active site motifs in nanoalloys that possess targeted catalytic descriptor values while being thermodynamically stable under working conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1984

US Aeromagnetic and Satellite Magnetic Anomaly Comparisons

Scalar aeromagnetic data obtained by the U.S. Naval Oceanographic Office (NOO) Vector Magnetic Survey of the conterminous U.S. were screened for periods of intense diurnal magnetic activity and reduced to anomaly form, filtered, and continued upward. A number of correlations between the NOO, POGO and preliminary MAGSAT data are evident at satellite elevations, including a prominent transcontinental magnetic high which extends from the Anadarko Basin to the Cincinnati Arch. The transcontinental magnetic high is breached by negative anomalies located over the Rio Grande Rift and Mississippi River Aulacogen. Differentially reduced-to-pole NOO and POGO magnetic anomaly data show that the transcontinental magnetic high corresponds to a well-defined regional trend of negative free-air gravity and enhanced crustal thickness anomalies.

Vonfrese, R. R. B.↗
DOE OSTI2015

Structure and osmotic pressure of ionic microgel dispersions

We investigate structural and thermodynamic properties of aqueous dispersions of ionic microgels—soft colloidal gel particles that exhibit unusual phase behavior. Starting from a coarse-grained model of microgel macroions as charged spheres that are permeable to microions, we perform simulations and theoretical calculations using two complementary implementations of Poisson-Boltzmann (PB) theory. Within a one-component model, based on a linear-screening approximation for effective electrostatic pair interactions, we perform molecular dynamics simulations to compute macroion-macroion radial distribution functions, static structure factors, and macroion contributions to the osmotic pressure. For the same model, using a variational approximation for the free energy, we compute both macroion and microion contributions to the osmotic pressure. Within a spherical cell model, which neglects macroion correlations, we solve the nonlinear PB equation to compute microion distributions and osmotic pressures. By comparing the one-component and cell model implementations of PB theory, we demonstrate that the linear-screening approximation is valid for moderately charged microgels. By further comparing cell model predictions with simulation data for osmotic pressure, we chart the cell model’s limits in predicting osmotic pressures of salty dispersions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Microbial source tracking markers associated with domestic rainwater harvesting systems: Correlation to indicator organisms

Highlights: • Microbial source tracking markers are present in harvested rainwater. • HF183, Lachnospiraceae, B. adolescentis and adenovirus correlate with indicators. • HF183, adenovirus and Lachnospiraceae co-occurred with indicator organisms. • These MST markers may be incorporated in a toolbox to supplement indicator analysis. Domestic rainwater harvesting (tank water) systems were screened for the presence of a panel of microbial source tracking (MST) markers and traditional indicator organisms. The indicator organisms were enumerated utilizing traditional culture-based methods, while the MST markers were quantified by quantitative PCR (qPCR). The indicators Escherichia coli (E. coli) and enterococci were also quantified using qPCR. Correlations and concurrence between these parameters were then investigated to determine which markers could be utilized to supplement traditional indicator analysis. Quantitative PCR analysis indicated that Bacteroides HF183, adenovirus, Lachnospiraceae and E. coli were detected and quantifiable in 100% of the tank water samples collected throughout the sampling period, while human mitochondrial DNA (mtDNA) was quantifiable in 90% of the tank water samples and Bifidobacterium adolescentis (B. adolescentis) and enterococci were quantifiable in 67% of the tank water samples, respectively. Significant positive correlations were recorded for Lachnospiraceae versus heterotrophic bacteria (p = 0.000), adenovirus versus E. coli (culturing) (p = 0.000) and heterotrophic bacteria (p = 0.024), the HF183 marker versus E. coli (qPCR) (p = 0.024) and B. adolescentis versus fecal coliforms (p = 0.037). In addition, 100% concurrence was observed for the HF183 marker, adenovirus and Lachnospiraceae versus E. coli (qPCR), enterococci (qPCR) and heterotrophic bacteria, amongst others. Based on the correlations and the concurrence analysis, the HF183 marker, Lachnospiraceae and adenovirus may be utilized to supplement indicator organism analysis for the monitoring of harvested rainwater quality.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Interaction of Planar Brushes with End-Functionalized Carboxylic or Amine Groups: Water Structure, Hydrogen Bonds, and Electrostatic Correlations

In this work we present a detailed analysis of two parallel plates grafted with polyethylene glycol (PEG) terminated with carboxylic or amine groups, and discuss the cases of opposite and same charge in the presence of electrolytes. We analyze the screening of charged groups by electrolytes, the role of hydrogen bonds in enhancing attraction or repulsion, hydration forces, competitive binding with counterions, and the hydrogen bond network, crucial to stabilizing the system. Our simulations reveal that, for large plate separation, the system is well described as a “Stern layer”, characterized by strong counterion binding and a subsequent weak diffuse layer. For short plate separation, the system is characterized by strong correlations among end groups and counterions. We also discuss the relevance of charged carboxylic/amine groups in different problems with a particular emphasis on nanoparticle assembly.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗