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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

AlGaAs-GaAs quantum-well lasers for direct solar photopumping

The paper theoretically examines the solar power requirements for low-threshold AlGaAs-GaAs quantum-well lasers directly photopumped by focused sunlight. A model of separate-confinement quantum-well-heterostructure (SCQWH) lasers was developed, which explicitly treats absorption and transport phenomena relevant to solar pumping. The model was used to identify separate-confinement single-quantum-well laser structures which should operate at photoexcitation intensities of less than 10,000 suns.

Unnikrishnan, Sreenath↗
NASA NTRS2003

Alloy Design Workbench-Surface Modeling Package Developed

NASA Glenn Research Center's Computational Materials Group has integrated a graphical user interface with in-house-developed surface modeling capabilities, with the goal of using computationally efficient atomistic simulations to aid the development of advanced aerospace materials, through the modeling of alloy surfaces, surface alloys, and segregation. The software is also ideal for modeling nanomaterials, since surface and interfacial effects can dominate material behavior and properties at this level. Through the combination of an accurate atomistic surface modeling methodology and an efficient computational engine, it is now possible to directly model these types of surface phenomenon and metallic nanostructures without a supercomputer. Fulfilling a High Operating Temperature Propulsion Components (HOTPC) project level-I milestone, a graphical user interface was created for a suite of quantum approximate atomistic materials modeling Fortran programs developed at Glenn. The resulting "Alloy Design Workbench-Surface Modeling Package" (ADW-SMP) is the combination of proven quantum approximate Bozzolo-Ferrante-Smith (BFS) algorithms (refs. 1 and 2) with a productivity-enhancing graphical front end. Written in the portable, platform independent Java programming language, the graphical user interface calls on extensively tested Fortran programs running in the background for the detailed computational tasks. Designed to run on desktop computers, the package has been deployed on PC, Mac, and SGI computer systems. The graphical user interface integrates two modes of computational materials exploration. One mode uses Monte Carlo simulations to determine lowest energy equilibrium configurations. The second approach is an interactive "what if" comparison of atomic configuration energies, designed to provide real-time insight into the underlying drivers of alloying processes.

Abel, Phillip B.↗
NASA NTRSDate not supplied

Quantum-Accelerated Distributed Algorithms for Approximate Steiner Trees and Directed Minimum Spanning Trees

We present two algorithms in the Quantum CONGEST-CLIQUE model of distributed computation that succeed with high probability; One for producing an approximately optimal Steiner Tree, and one for producing an exact Minimum Directed Spanning tree. These use O(n1/4) rounds of communication and O(n9/4) messages, leading to a quantum speedup in round and message complexity compared to any known algorithms in the classical CONGEST-CLIQUE model (vs O(n1/3) and O(n7/3)). At a high level, we achieve these results by combining classical algorithms with fast quantum subroutines. Further, these problems can not be sped up in the CONGEST (non-clique) setting, and we characterize the constants involved.

Phillip Kerger↗
NASA NTRS1995

Determination of Quantum Chemistry Based Force Fields for Molecular Dynamics Simulations of Aromatic Polymers

Ab initio quantum chemistry calculations for model molecules can be used to parameterize force fields for molecular dynamics simulations of polymers. Emphasis in our research group is on using quantum chemistry-based force fields for molecular dynamics simulations of organic polymers in the melt and glassy states, but the methodology is applicable to simulations of small molecules, multicomponent systems and solutions. Special attention is paid to deriving reliable descriptions of the non-bonded and electrostatic interactions. Several procedures have been developed for deriving and calibrating these parameters. Our force fields for aromatic polyimide simulations will be described. In this application, the intermolecular interactions are the critical factor in determining many properties of the polymer (including its color).

Jaffe, Richard↗
NASA NTRS2013

XIPE the X-Ray Imaging Polarimetry Explorer

X-ray polarimetry, sometimes alone, and sometimes coupled to spectral and temporal variability measurements and to imaging, allows a wealth of physical phenomena in astrophysics to be studied. X-ray polarimetry investigates the acceleration process, for example, including those typical of magnetic reconnection in solar flares, but also emission in the strong magnetic fields of neutron stars and white dwarfs. It detects scattering in asymmetric structures such as accretion disks and columns, and in the so-called molecular torus and ionization cones. In addition, it allows fundamental physics in regimes of gravity and of magnetic field intensity not accessible to experiments on the Earth to be probed. Finally, models that describe fundamental interactions (e.g. quantum gravity and the extension of the Standard Model) can be tested. We describe in this paper the X-ray Imaging Polarimetry Explorer (XIPE), proposed in June 2012 to the first ESA call for a small mission with a launch in 2017. The proposal was, unfortunately, not selected. To be compliant with this schedule, we designed the payload mostly with existing items. The XIPE proposal takes advantage of the completed phase A of POLARIX for an ASI small mission program that was cancelled, but is different in many aspects: the detectors, the presence of a solar flare polarimeter and photometer and the use of a light platform derived by a mass production for a cluster of satellites. XIPE is composed of two out of the three existing JET-X telescopes with two Gas Pixel Detectors (GPD) filled with a He-DME mixture at their focus. Two additional GPDs filled with a 3-bar Ar-DME mixture always face the Sun to detect polarization from solar flares. The Minimum Detectable Polarization of a 1 mCrab source reaches 14 in the 210 keV band in 105 s for pointed observations, and 0.6 for an X10 class solar flare in the 1535 keV energy band. The imaging capability is 24 arcsec Half Energy Width (HEW) in a Field of View of 14.7 arcmin 14.7 arcmin. The spectral resolution is 20 at 6 keV and the time resolution is 8 s. The imaging capabilities of the JET-X optics and of the GPD have been demonstrated by a recent calibration campaign at PANTER X-ray test facility of the Max-Planck-Institut fr extraterrestrische Physik (MPE, Germany). XIPE takes advantage of a low-earth equatorial orbit with Malindi as down-link station and of a Mission Operation Center (MOC) at INPE (Brazil). The data policy is organized with a Core Program that comprises three months of Science Verification Phase and 25 of net observing time in the following 2 years. A competitive Guest Observer program covers the remaining 75 of the net observing time.

Astronomy X-ray Polarimetry↗
NASA NTRSDate not supplied

Quantum Hardware-Enabled Molecular Dynamics via Transfer Learning

The ability to perform ab initio molecular dynamics simulations using potential energy surfaces provided by quantum computers would open the door to virtually exact dynamics for a variety of chemical and biochemical systems, with impacts on catalysis and biophysics. Nonetheless, performing molecular dynamics on surfaces produced by quantum hardware has been hampered by the noisy energies typically produced by quantum computers and challenges associated with computing gradients and scaling to large systems interest. A recent set of advances in machine learning, known as transfer learning, provides a new path forward for molecular dynamics simulations on quantum hardware. Transfer learning offers a workaround, where one first trains models on larger, less accurate classical datasets and then refines them on smaller, more accurate quantum datasets. We explore this approach by training machine learning models to predict a molecule's potential energy based on its geometric structure using Behler-Parrinello neural networks. When successfully trained, the model enables energy gradient predictions necessary for dynamic simulations. To reduce the quantum resources needed, the model is initially trained with data derived from classical density functional theory and subsequently refined with a smaller dataset obtained from a variational quantum eigensolver optimization of the unitary coupled cluster ansatz. We show that this approach significantly reduces the size of the needed quantum training dataset while capturing the high accuracies needed within quantum chemistry simulations. The success of this two-step training method opens more opportunities to apply machine learning models on quantum data, a significant stride towards efficient quantum-classical hybrid computational models.

quantum computing↗
NASA NTRS1991

GaAs solar cell photoresponse modeling using PC-1D V2.1

Photoresponse data of high efficiency GaAs solar cells were analyzed using PC-1D V2.1. The approach required to use PC-1D for photoresponse data analysis, and the physical insights gained from performing the analysis are discussed. In particular, the effect of Al(x)Ga(1-x)As heteroface quality was modeled. Photoresponse or spectral quantum efficiency is an important tool in characterizing material quality and predicting cell performance. The strength of the photoresponse measurement lies in the ability to precisely fit the experimental data with a physical model. PC-1D provides a flexible platform for calculations based on these physical models.

Huber, D. A.↗
NASA NTRS2002

Ordered nanoparticle arrays formed on engineered chaperonin protein templates

Traditional methods for fabricating nanoscale arrays are usually based on lithographic techniques. Alternative new approaches rely on the use of nanoscale templates made of synthetic or biological materials. Some proteins, for example, have been used to form ordered two-dimensional arrays. Here, we fabricated nanoscale ordered arrays of metal and semiconductor quantum dots by binding preformed nanoparticles onto crystalline protein templates made from genetically engineered hollow double-ring structures called chaperonins. Using structural information as a guide, a thermostable recombinant chaperonin subunit was modified to assemble into chaperonins with either 3 nm or 9 nm apical pores surrounded by chemically reactive thiols. These engineered chaperonins were crystallized into two-dimensional templates up to 20 microm in diameter. The periodic solvent-exposed thiols within these crystalline templates were used to size-selectively bind and organize either gold (1.4, 5 or 10nm) or CdSe-ZnS semiconductor (4.5 nm) quantum dots into arrays. The order within the arrays was defined by the lattice of the underlying protein crystal. By combining the self-assembling properties of chaperonins with mutations guided by structural modelling, we demonstrate that quantum dots can be manipulated using modified chaperonins and organized into arrays for use in next-generation electronic and photonic devices.

Chaperonins/chemistry/ultrastructure↗
NASA NTRS2003

Microscopic Modeling of Intersubband Optical Processes in Type II Semiconductor Quantum Wells: Linear Absorption

Linear absorption spectra arising from intersubband transitions in semiconductor quantum well heterostructures are analyzed using quantum kinetic theory by treating correlations to the first order within Hartree-Fock approximation. The resulting intersubband semiconductor Bloch equations take into account extrinsic dephasing contributions, carrier-longitudinal optical phonon interaction and carrier-interface roughness interaction which is considered with Ando s theory. As input for resonance lineshape calculation, a spurious-states-free 8-band kp Hamiltonian is used, in conjunction with the envelop function approximation, to compute self-consistently the energy subband structure of electrons in type II InAs/AlSb single quantum well structures. We demonstrate the interplay of nonparabolicity and many-body effects in the mid-infrared frequency range for such heterostructures.

Li, Jian-Zhong↗
NASA NTRS1990

Characteristics of AlGaAs/GaAs multiple quantum well infrared detectors

Researchers fabricated and characterized several AlGaAs/GaAs multiple quantum well infrared detectors to evaluate the ultimate performance of these devices for low infrared background applications. The detectors were designed to have a single bound state in the quantum well and the first excited state in the continuum above the AlGaAs conduction band edge. The difference in energy between the two levels, as determined by the quantum well width and aluminum mole fraction in the barrier, was chosen such that peak absorption would occur near 8 microns. The initial structures studied comprised 50 periods with 40 A well widths and 300 A Al(0.28)Ga(0.72)As barriers. The performance of these detectors are summarized. To better interpret these results and design optimized detectors, researchers modeled both the detector noise and tunneling currents. The noise model correctly predicts that multiple quantum well detectors will, indeed, exhibit noise lower than full shot noise. The tunneling current model predicts the dark current versus bias for any choice of design parameters in a multiple quantum well detector. This model predicts a substantially reduced dark current (x 10(exp 04)) for samples with 400 A barriers. To evaluate structures with thicker barriers, researchers fabricated and characterized detectors with 400 A and 500 A barriers; a comparison of detector dark currents is shown. These results are consistent with the predictions of the dark current model.

Janousek, Bruce K.↗
NASA NTRSDate not supplied

Introduction to Quantum Computing

Quantum computing offers the potential to revolutionize high-performance computing by providing a means to solve certain computational problems asymptotically faster than any classical computer. Quantum computing has advanced recently from merely a theoretical possibility to engineered reality, including commercial entities offering early prototype quantum processors, both special-purpose quantum annealers and general-purpose gate-model processors. The media have been showcasing each new development and implicitly conveying the message that quantum-computing ubiquity is nigh. Here, we will respond to this hype and provide an overview of the exciting but still early state of the field. In this tutorial, we introduce participants to the computational models that give quantum computing its immense computational power. We examine the thought processes that programmers need to map problems to quantum computers. And we discuss hardware and algorithmic challenges that must be overcome before quantum computing becomes a component of every software developer's repertoire.

Quantum computing↗
NASA NTRSDate not supplied

Introduction to Quantum Computing

Quantum computing offers the potential to revolutionize high-performance computing by providing a means to solve certain computational problems asymptotically faster than any classical computer. Quantum computing has advanced recently from merely a theoretical possibility to engineered reality, including commercial entities offering early prototype quantum processors, both special-purpose quantum annealers and general-purpose gate-model processors. The media have been showcasing each new development and implicitly conveying the message that quantum-computing ubiquity is nigh. Here, we will respond to this hype and provide an overview of the exciting but still early state of the field. In this tutorial, we introduce participants to the computational models that give quantum computing its immense computational power. We examine the thought processes that programmers need to map problems to quantum computers. And we discuss hardware and algorithmic challenges that must be overcome before quantum computing becomes a component of every software developer's repertoire. (Update of 2022 slides)

Quantum computing↗
NASA NTRS1977

Quantum mechanical features of optically pumped CW FIR lasers

Quantum mechanical predictions for the gain of an optically pumped CW FIR laser are presented for cases in which one or both of the pump and FIR transitions are pressure or Doppler broadened. The results are compared to those based on the rate equation model. Some of the quantum mechanical predictions are verified in CH3OH.

Seligson, D.↗
NASA NTRS1978

Optical and infrared lasers

Quantum mechanical predictions for the gain of an optically pumped CW FIR laser are presented for cases in which one or both of the pump and FIR transitions are pressure or Doppler broadened. The results are compared to those based on the rate equation model. Some of the quantum mechanical predictions are verified in CH3OH.

Javan, A.↗