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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 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Correlation of the nanostructure with optoelectronic properties during rapid thermal annealing of Ga(NAsP) quantum wells grown on Si(001) substrates

Ga(NAsP) quantum wells grown pseudomorphically on Si substrate are promising candidates for optically active light sources in future optoelectronically integrated circuits on Si substrates. As the material is typically grown at low temperatures, it has to be thermally annealed after growth to remove defects and optimize optoelectronic properties. Here we show by quantitative transmission electron microscopy that two different kinds of structural development are associated with the annealing. First of all, the quantum well homogeneity improves with increasing annealing temperature. For annealing temperatures above 925 °C the composition becomes less homogeneous again. Second, voids form in the quantum well for annealing temperatures above 850 °C. Their density and size increase continuously with increasing annealing temperature. These results are correlated to the optical properties of the samples, where we find from temperature-dependent photoluminescence measurements two scales of disorder, which show the same temperature dependence as the structural properties.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Electromechanical characterization of individual micron-sized metal coated polymer particles

Micron-sized polymer particles with nanoscale metal coatings are essential in conductive adhesives for electronics assembly. The particles function in a compressed state in the adhesives. The link between mechanical properties and electrical conductivity is thus of the utmost importance in the formation of good electrical contact. A custom flat punch set-up based on nanoindentation has been developed to simultaneously deform and electrically probe individual particles. The set-up has a sufficiently low internal resistance to allow the measurement of sub-Ohm contact resistances. Additionally, the set-up can capture mechanical failure of the particles. Combining this data yields a fundamental understanding of contact behavior. We demonstrate that this method can clearly distinguish between particles of different sizes, with different thicknesses of metal coating, and different metallization schemes. The technique provides good repeatability and physical insight into the behavior of these particles that can guide adhesive design and the optimization of bonding processes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2011

Invited Review Article: Measurement uncertainty of linear phase-stepping algorithms

Phase retrieval techniques are widely used in optics, imaging and electronics. Originating in signal theory, they were introduced to interferometry around 1970. Over the years, many robust phase-stepping techniques have been developed that minimize specific experimental influence quantities such as phase step errors or higher harmonic components of the signal. However, optimizing a technique for a specific influence quantity can compromise its performance with regard to others. We present a consistent quantitative analysis of phase measurement uncertainty for the generalized linear phase stepping algorithm with nominally equal phase stepping angles thereby reviewing and generalizing several results that have been reported in literature. All influence quantities are treated on equal footing, and correlations between them are described in a consistent way. For the special case of classical N-bucket algorithms, we present analytical formulae that describe the combined variance as a function of the phase angle values. For the general Arctan algorithms, we derive expressions for the measurement uncertainty averaged over the full 2{pi}-range of phase angles. We also give an upper bound for the measurement uncertainty which can be expressed as being proportional to an algorithm specific factor. Tabular compilations help the reader to quickly assess the uncertainties that are involved with his or her technique.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

An adjoint method for a high-order discretization of deforming domain conservation laws for optimization of flow problems

We report the fully discrete adjoint equations and the corresponding adjoint method are derived for a globally high-order accurate discretization of conservation laws on parametrized, deforming domains. The conservation law on the deforming domain is transformed into one on a fixed reference domain by the introduction of a time-dependent mapping that encapsulates the domain deformation and parametrization, resulting in an Arbitrary Lagrangian–Eulerian form of the governing equations. A high-order discontinuous Galerkin method is used to discretize the transformed equation in space and a high-order diagonally implicit Runge–Kutta scheme is used for the temporal discretization. Quantities of interest that take the form of space–time integrals are discretized in a solver-consistent manner. The corresponding fully discrete adjoint method is used to compute exact gradients of quantities of interest along the manifold of solutions of the fully discrete conservation law. These quantities of interest and their gradients are used in the context of gradient-based PDE-constrained optimization. The adjoint method is used to solve two optimal shape and control problems governed by the isentropic, compressible Navier–Stokes equations. The first optimization problem seeks the energetically optimal trajectory of a 2D airfoil given a required initial and final spatial position. The optimization solver, driven by gradients computed via the adjoint method, reduced the total energy required to complete the specified mission nearly an order of magnitude. The second optimization problem seeks the energetically optimal flapping motion and time-morphed geometry of a 2D airfoil given an equality constraint on the x-directed impulse generated on the airfoil. The optimization solver satisfied the impulse constraint to greater than 8 digits of accuracy and reduced the required energy between a factor of 2 and 10, depending on the value of the impulse constraint, as compared to the nominal configuration.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Green’s functions on a renormalized lattice: An improved method for the integer quantum Hall transition

Highlights: • Speed up of numerical calculations by exact renormalization group steps. • Practical approach to handle delicate scaling analysis. • Absence of evidence to prefer marginal corrections over power-law corrections. We introduce a performance-optimized method to simulate localization problems on bipartite tight-binding lattices. It combines an exact renormalization group step to reduce the sparseness of the original problem with the recursive Green’s function method. We apply this framework to investigate the critical behavior of the integer quantum Hall transition of a tight-binding Hamiltonian defined on a simple square lattice. In addition, we employ an improved scaling analysis that includes two irrelevant exponents to characterize the shift of the critical energy as well as the corrections to the dimensionless Lyapunov exponent. We compare our findings with the results of a conventional implementation of the recursive Green’s function method, and we put them into broader perspective in view of recent development in this field.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Minimal Time Problem with Impulsive Controls

Time optimal control problems for systems with impulsive controls are investigated. Sufficient conditions for the existence of time optimal controls are given. A dynamical programming principle is derived and Lipschitz continuity of an appropriately defined value functional is established. The value functional satisfies a Hamilton–Jacobi–Bellman equation in the viscosity sense. A numerical example for a rider-swing system is presented and it is shown that the reachable set is enlargered by allowing for impulsive controls, when compared to nonimpulsive controls.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Strongly transverse-electric-polarized emission from deep ultraviolet AlGaN quantum well light emitting diodes

The optical polarization of emission from ultraviolet (UV) light emitting diodes (LEDs) based on (0001)-oriented Al{sub x}Ga{sub 1−x}N multiple quantum wells (MQWs) has been studied by simulations and electroluminescence measurements. With increasing aluminum mole fraction in the quantum well x, the in-plane intensity of transverse-electric (TE) polarized light decreases relative to that of the transverse-magnetic polarized light, attributed to a reordering of the valence bands in Al{sub x}Ga{sub 1−x}N. Using k ⋅ p theoretical model calculations, the AlGaN MQW active region design has been optimized, yielding increased TE polarization and thus higher extraction efficiency for bottom-emitting LEDs in the deep UV spectral range. Using (i) narrow quantum wells, (ii) barriers with high aluminum mole fractions, and (iii) compressive growth on patterned aluminum nitride sapphire templates, strongly TE-polarized emission was observed at wavelengths as short as 239 nm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Charge-transfer excited states: Seeking a balanced and efficient wave function ansatz in variational Monte Carlo

We present a simple and efficient wave function ansatz for the treatment of excited charge-transfer states in real-space quantum Monte Carlo methods. Using the recently-introduced variation-after-response method, this ansatz allows a crucial orbital optimization step to be performed beyond a configuration interaction singles expansion, while only requiring calculation of two Slater determinant objects. As a result, we demonstrate this ansatz for the illustrative example of the stretched LiF molecule, for a range of excited states of formaldehyde, and finally for the more challenging ethylene-tetrafluoroethylene molecule.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2012

Predictive Capability for Strongly Correlated Systems

Diffusion Monte Carlo methods can give highly accurate results for correlated systems, provided that well optimized trial wave functions with accurate nodal surfaces are employed. The Cornell team developed powerful methods for optimizing all the parameters within a multi-determinant Slater-Jastrow form of the wave function. These include the Jastrow parameters within a flexible electron-electron-nucleus form of the Jastrow function, the parameters multiplying the configuration state functions, the orbital parameters and the basis exponents. The method optimizes a linear combination of the energy and the variance of the local energy. The optimal parameters are found iteratively by diagonalizing the Hamiltonian matrix in the space spanned by the wave function and its first-order derivatives, making use of a strong zero-variance principle. It is highly robust, has become the method of choice for correlated wave function optimization and has been adopted by other QMC groups. This optimization method was used on the first-row atoms and homonuclear diatomic molecules, demonstrating that molecular well depths can be obtained with near chemical accuracy quite systematically at the diffusion Monte Carlo level for these systems. In addition the complete ground-state potential energy curve of the C 2 molecule up to the dissociation limit was obtained, and, size consistency and broken spin-symmetry issues in quantum Monte Carlo calculations were studied. The method was used with a eight-electrons-in-eight-orbitals complete active space CAS(8,8) wave function to study the relative energies of the monocyclic and bicyclic forms of m-benzyne. The DMC calculations show that the monocyclic structure is lower in energy than the bicyclic structure by 1.92 kcal/ mole, which is in excellent agreement with the best coupled cluster results (CCSD(T)) and in disagreement with the CCSD results. QMC methods have for the most part been used only for ground states of a given symmetry. We developed a method for calculating low-lying excited states as well and tested it on the ground and lowest three adiabatic excited states of methylene with progressively larger JastrowSlater multideterminant complete active space CAS wave functions. The highest of these states has the same symmetry, 1 A 1 , as the first excited state. The DMC excitation energies obtained using any of the CAS wave functions are in excellent agreement with experiment. In contrast, single-determinant wave functions do not yield accurate DMC energies, indicating that it is important to include in the wave function Slater determinants that describe static strong correlation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2013

Investigation of laser-driven proton acceleration using ultra-short, ultra-intense laser pulses

We report optimization of laser-driven proton acceleration, for a range of experimental parameters available from a single ultrafast Ti:sapphire laser system. We have characterized laser-generated protons produced at the rear and front target surfaces of thin solid targets (15 nm to 90 {mu}m thicknesses) irradiated with an ultra-intense laser pulse (up to 10{sup 20} W Dot-Operator cm{sup -2}, pulse duration 30 to 500 fs, and pulse energy 0.1 to 1.8 J). We find an almost symmetric behaviour for protons accelerated from rear and front sides, and a linear scaling of proton energy cut-off with increasing pulse energy. At constant laser intensity, we observe that the proton cut-off energy increases with increasing laser pulse duration, then roughly constant for pulses longer than 300 fs. Finally, we demonstrate that there is an optimum target thickness and pulse duration.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Quantum Alternating Operator Ansatz (QAOA) Phase Diagrams and Applications for Quantum Chemistry

Determining Hamiltonian ground states and energies is a challenging task with many possible approaches on quantum computers. While variational quantum eigensolvers are popular approaches for near term hardware, adiabatic state preparation is an alternative that does not require noisy optimization of parameters. Beyond adiabatic schedules, QAOA is an important method for optimization problems. In this work we modify QAOA to apply to finding ground states of molecules and empirically evaluate the modified algorithm on several molecules. This modification applies physical insights used in classical approximations to construct suitable QAOA operators and initial state. We find robust qualitative behavior for QAOA as a function of the number of steps and size of the parameters, and demonstrate this behavior also occurs in standard QAOA applied to combinatorial search. To this end we introduce QAOA phase diagrams that capture its performance and properties in various limits. In particular we show a region in which non-adiabatic schedules perform better than the adiabatic limit while employing lower quantum circuit depth. We further provide evidence our results and insights also apply to QAOA applications beyond chemistry.

Kremenetski, Vladimir↗
DOE OSTI2019

Accelerating materials science with high-throughput computations and machine learning

With unprecedented amounts of materials data generated from experiments as well as high-throughput density functional theory calculations, machine learning techniques has the potential to greatly accelerate materials discovery and design. In this report we review our efforts in the Materials Virtual Lab to integrate software automation, data generation and curation and machine learning to (i) design and optimize technological materials for energy storage, energy efficiency and high-temperature alloys; (ii) develop scalable quantum-accurate models, and (iii) enhance the speed and accuracy in interpreting characterization spectra.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Ultrafast radiographic imaging and tracking: An overview of instruments, methods, data, and applications

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond transients or dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes are fundamental to modern technologies and applications, such as nuclear fusion energy, advanced manufacturing, communication, and green transportation, which often involve one mole or more atoms and elementary particles, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: (a.) Detectors such as high-speed complementary metal-oxide semiconductor (CMOS) cameras, hybrid pixelated array detectors integrated with Timepix4 and other application-specific integrated circuits (ASICs), and digital photon detectors; (b.) U-RadIT modalities such as dynamic phase contrast imaging, dynamic diffractive imaging, and four-dimensional (4D) particle tracking; (c.) U-RadIT data and algorithms such as neural networks and machine learning, and (d.) Applications in ultrafast dynamic material science using XFELs, synchrotrons and laser-driven sources. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2012

Molecular Dynamics Simulation of Tri-n-Butyl-Phophate Liquid: A Force Field Comparative Study

Molecular dynamics (MD) simulations were conducted to compare the performance of four force fields in predicting thermophysical properties of tri-n-butyl-phosphate (TBP) in the liquid phase. The intramolecular force parameters used were from the Assisted Model Building with Energy Refinement (AMBER) force field model. The van der Waals parameters were based on either the AMBER or the Optimized Potential for Liquid Simulation (OPLS) force fields. The atomic partial charges were either assigned by performing quantum chemistry calculations or utilized previously published data, and were scaled to approximate the average experimental value of the electric dipole moment. Canonical ensemble computations based on the aforementioned parameters were performed near the atmospheric pressure and temperature to obtain the electric dipole moment, mass density, and self-diffusion coefficient. In addition, the microscopic structure of the liquid was characterized via pair correlation functions between selected atoms. It has been demonstrated that the electric dipole moment can be approximated within 1% of the average experimental value by virtue of scaled atomic partial charges. The liquid mass density can be predicted within 0.5-1% of its experimentally determined value when using the corresponding charge scaling. However, in all cases the predicted self- diffusion coefficient is significantly smaller than a commonly quoted experimental measurement; this result is qualified by the fact that the uncertainty of the experimental value was not available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

GaInN light-emitting diodes using separate epitaxial growth for the p-type region to attain polarization-inverted electron-blocking layer, reduced electron leakage, and improved hole injection

A GaInN light-emitting diode (LED) structure is analyzed that employs a separate epitaxial growth for the p-type region, i.e., the AlGaN electron-blocking layer (EBL) and p-type GaN cladding layer, followed by wafer or chip bonding. Such LED structure has a polarization-inverted EBL and allows for uncompromised epitaxial-growth optimization of the p-type region, i.e., without the need to consider degradation of the quantum-well active region during p-type region growth. Simulations show that such an LED structure reduces electron leakage, reduces the efficiency droop, improves hole injection, and has the potential to extend high efficiencies into the green spectral region.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Projected density matrix embedding theory with applications to the two-dimensional Hubbard model

Density matrix embedding theory (DMET) is a quantum embedding theory for strongly correlated systems. From a computational perspective, one bottleneck in DMET is the optimization of the correlation potential to achieve self-consistency, especially for heterogeneous systems of large size. We introduce a new approach, called projected DMET (p-DMET), which achieves self-consistency without needing to optimize the correlation potential. We demonstrate the performance of p-DMET on the two-dimensional Hubbard model.

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

Multi-modality deep learning for pulse prediction in homogeneous nonlinear systems via parametric conversion

In this Letter, we introduce FusionNet, a multi-modality deep learning framework designed to predict and analyze output pulses in high-power rare-earth-doped laser systems driving parametric conversion in homogeneous guided nonlinear media. FusionNet integrates temporal, spectral, and physical experimental conditions to model ultrafast nonlinear phenomena, including parametric nonlinear frequency conversion, self-phase modulation, and cross-phase modulation in homogeneous guided systems such as gas-filled hollow-core fibers. These systems bridge physical models with experimental data, advancing our understanding of light-guiding principles and nonlinear interactions while expediting the design and optimization of on-demand high-power, high-brightness systems. Our results demonstrate a 73% reduction in prediction error and an 83% improvement in computational efficiency compared to conventional neural networks. This work establishes a new paradigm for accelerating parametric simulations and optimizing experimental designs in high-power laser systems, with further implications for high-precision spectroscopy, quantum information science, and distributed entangled interconnects.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Design of Hopfield Networks Based on Superconducting Coupled Oscillators

The global energy shortage has driven the development of many energy-efficient computational platforms beyond Moore's law, among which brain-inspired neuromorphic computing is one of the promising solutions. Associative memory and pattern recognition are important computations solved by brain-inspired Hopfield networks. Classical Hopfield networks store memories via fixed point attractors of their dynamics. In oscillatory Hopfield networks, these attractors are replaced by periodic orbits. Here, we design an oscillatory Hopfield network based on coupled superconducting oscillators. We first employ a mathematical phase reduction approach to map networks of coupled superconducting rapid single flux quantum (RSFQ) ring oscillators to coupled Kuramoto phase-oscillator networks. We use this theory to numerically optimize the hardware's mutual inductances in order to directly match the phase-reduced superconducting oscillators to a model of phase-oscillator-based Hopfield networks. The resulting network can store multiple oscillatory phase-locked memory patterns and recover the patterns based on the initial phase conditions. As different pattern recognition tasks, or learning, require tunable connectivity strengths between the oscillatory nodes, we further employ a coupler circuit that enables tuning the coupling strength between two oscillators by applying an external flux. We demonstrate the functionality of our design through numerical simulations of a small example network with oscillators operating at 86 GHz and recognizing patterns within 10 ns. Our approach enables the learning and retrieval of dynamical memory patterns with a wide range of applications where rhythmic dynamic output is beneficial.

Cheng, Ran↗