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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Resonant tunneling assisted propagation and amplification of plasmons in high electron mobility transistors

A rigorous theoretical and computational model is developed for the plasma-wave propagation in high electron mobility transistor structures with electron injection from a resonant tunneling diode at the gate. We discuss the conditions in which low-loss and sustainable plasmon modes can be supported in such structures. The developed analytical model is used to derive the dispersion relation for these plasmon-modes. A non-linear full-wave-hydrodynamic numerical solver is also developed using a finite difference time domain algorithm. The developed analytical solutions are validated via the numerical solution. We also verify previous observations that were based on a simplified transmission line model. It is shown that at high levels of negative differential conductance, plasmon amplification is indeed possible. The proposed rigorous models can enable accurate design and optimization of practical resonant tunnel diode-based plasma-wave devices for terahertz sources, mixers, and detectors, by allowing a precise representation of their coupling when integrated with other electromagnetic structures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Color optimization of red OLEDs via periodic and gradient deposition rate of fluorescent dopants

We have investigated a method to enhance the light emission efficiency and color tuning for mixture of dyes with varying the growth parameters, during Alq{sub 3}:Nile Red deposition. We used solvent bath and green laser (532 nm) for improvement of the performance of devices. This paper presents two kind of evaporation method, periodic and gradient. We observed that periodic emission layers can greatly improve the device performances such as electroluminescence, chromaticity coordinates, luminance, and reduced the turn on voltage. The periodic 3 structure with evaporation rate of (0.1 → 3 → 2 → 3 → 2 → 0.1 A°/s) has a luminance of 3000 cd/m{sup 2} and maximum efficiency of 1.8 cd/A at 18 V which are the highest values among devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Quantum Nonlinear Hall Effect Induced by Berry Curvature Dipole in Time-Reversal Invariant Materials

It is well known that a nonvanishing Hall conductivity requires broken time-reversal symmetry. However, we demonstrate that Hall-like currents can occur in second-order response to external electric fields in a wide class of time-reversal invariant and inversion breaking materials, at both zero and twice the driving frequency. This nonlinear Hall effect has a quantum origin arising from the dipole moment of the Berry curvature in momentum space, which generates a net anomalous velocity when the system is in a current-carrying state. The nonlinear Hall coefficient is a rank-two pseudotensor, whose form is determined by point group symmetry. We discus optimal conditions to observe this effect and propose candidate two- and three-dimensional materials, including topological crystalline insulators, transition metal dichalcogenides, and Weyl semimetals.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Analytical gradients for molecular-orbital-based machine learning

We report molecular-orbital-based machine learning (MOB-ML) enables the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. Here, we present the derivation, implementation, and numerical demonstration of MOB-ML analytical nuclear gradients, which are formulated in a general Lagrangian framework to enforce orthogonality, localization, and Brillouin constraints on the molecular orbitals. The MOB-ML gradient framework is general with respect to the regression technique (e.g., Gaussian process regression or neural networks) and the MOB feature design. We show that MOB-ML gradients are highly accurate compared to other ML methods on the ISO17 dataset while only being trained on energies for hundreds of molecules compared to energies and gradients for hundreds of thousands of molecules for the other ML methods. The MOB-ML gradients are also shown to yield accurate optimized structures at a computational cost for the gradient evaluation that is comparable to a density-corrected density functional theory calculation.

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

Surf-Deformer: Mitigating Dynamic Defects on Surface Code via Adaptive Deformation

In this paper, we introduce Surf-Deformer, a code deformation framework that seamlessly integrates adaptive defect mitigation functionality into the current surface code workflow. It crafts several basic deformation instructions based on fundamental gauge transformations, which can be combined to explore a larger design space than previous methods. This enables more optimized deformation processes tailored to specific defect situations, restoring the QEC capability of deformed codes more efficiently with minimal qubit resources. Additionally, we design an adaptive code layout that accommodates our defect mitigation strategy while ensuring efficient execution of logical operations. Our evaluation shows that Surf-Deformer outperforms previous methods by significantly reducing the end-to-end failure rate of various quantum programs by 35× to 70×, while requiring only about 50% of the qubit resources compared to the previous method to achieve the same level of failure rate. Ablation studies show that Surf-Deformer surpasses previous defect removal methods in preserving QEC capability and facilitates surface code communication by achieving nearly optimal throughput.

Yin, Keyi↗
DOE OSTI2020

PANNA: Properties from Artificial Neural Network Architectures

We report prediction of material properties from first principles is often a computationally expensive task. Recently, artificial neural networks and other machine learning approaches have been successfully employed to obtain accurate models at a low computational cost by leveraging existing example data. Here, we present a software package “Properties from Artificial Neural Network Architectures” (PANNA) that provides a comprehensive toolkit for creating neural network models for atomistic systems following the Behler–Parrinello topology. Besides the core routines for neural network training, it includes data parser, descriptor builder for Behler–Parrinello class of symmetry functions and force-field generator suitable for integration within molecular dynamics packages. PANNA offers a variety of activation and cost functions, regularization methods, as well as the possibility of using fully-connected networks with custom size for each atomic species. PANNA benefits from the optimization and hardware-flexibility of the underlying TensorFlow engine which allows it to be used on multiple CPU/GPU/TPU systems, making it possible to develop and optimize neural network models based on large datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

A numerical analysis of a magnetocaloric refrigerator with a 16-layer regenerator

A numerical analysis was conducted to study a room temperature magnetocaloric refrigerator with a 16-layer parallel plates active magnetic regenerator (AMR). Sixteen layers of LaFeMnSiH having different Curie temperatures were employed as magnetocaloric material (MCM) in the regenerator. Measured properties data was used. A transient one dimensional (1D) model was employed, in which a unique numerical method was developed to significantly accelerate the simulation speed of the multi-layer AMR system. As a result, the computation speed of a multi-layer AMR case was very close to the single-layer configuration. The performance of the 16-layer AMR system in different frequencies and utilizations has been investigated using this model. To optimize the layer length distribution of the 16-layer MCMs in the regenerator, a set of 137 simulations with different MCM distributions based on the Design of Experiments (DoE) method was conducted and the results were analyzed. The results show that the 16-layer AMR system can operate up to 84% of Carnot cycle COP at a temperature span of 41 K, which cannot be obtained using an AMR with fewer layers. Here, the DoE results indicate that for a 16-layer AMR system, the uniform distribution is very close to the optimized design.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Effects of 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane doping on diketopyrrolopyrrole-based, low crystalline, high mobility polymeric semiconductor

The effects of 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4TCNQ) doping on diketopyrrolo-pyrrole-based polymeric semiconductors in terms of charge transport behavior and structural ordering are systematically investigated. Although the energy level offset between the polymeric semiconductor and the F4TCNQ acceptor was not particularly large, ultraviolet photoelectron spectroscopy analyses revealed that a low doping ratio of 1 wt. % is sufficient to tune the energy distance between the Fermi level and the HOMO level, reaching saturation at roughly 5 wt. %, which is further confirmed by the depletion mode measurements of field effect transistors (FETs). Structural analyses using grazing-incidence X-ray diffraction (GIXD) show that the overall degree of edge-on orientation is disturbed by the addition of dopants, with significant influence appearing at high doping ratios (>3 wt. %). The calculated charge carrier mobility from accumulation mode measurements of FETs showed a maximum value of 2 cm{sup 2}/V·s at the optimized doping ratio of 1%, enabled by additional holes in the channel region, which results in a roughly 40% increase relative to the undoped device. Further increases in the doping ratio, however, resulted in worse FET performance, which can be attributed to structural deformation. This result suggests that the electrochemical doping method can be also applied to donor-acceptor copolymers to further enhance their charge transport characteristics, once the optimized doping condition has been established.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Size-dependent error of the density functional theory ionization potential in vacuum and solution

Density functional theory is often the method of choice for modeling the energetics of large molecules and including explicit solvation effects. It is preferable to use a method that treats systems of different sizes and with different amounts of explicit solvent on equal footing. However, recent work suggests that approximate density functional theory has a size-dependent error in the computation of the ionization potential. We here investigate the lack of size-intensivity of the ionization potential computed with approximate density functionals in vacuum and solution. We show that local and semi-local approximations to exchange do not yield a constant ionization potential for an increasing number of identical isolated molecules in vacuum. Instead, as the number of molecules increases, the total energy required to ionize the system decreases. Rather surprisingly, we find that this is still the case in solution, whether using a polarizable continuum model or with explicit solvent that breaks the degeneracy of each solute, and we find that explicit solvent in the calculation can exacerbate the size-dependent delocalization error. We demonstrate that increasing the amount of exact exchange changes the character of the polarization of the solvent molecules; for small amounts of exact exchange the solvent molecules contribute a fraction of their electron density to the ionized electron, but for larger amounts of exact exchange they properly polarize in response to the cationic solute. In vacuum and explicit solvent, the ionization potential can be made size-intensive by optimally tuning a long-range corrected hybrid functional.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Communication: A difference density picture for the self-consistent field ansatz

We formulate self-consistent field (SCF) theory in terms of an interaction picture where the working variable is the difference density matrix between the true system and a corresponding superposition of atomic densities. As the difference density matrix directly represents the electronic deformations inherent in chemical bonding, this “difference self-consistent field (dSCF)” picture provides a number of significant conceptual and computational advantages. We show that this allows for a stable and efficient dSCF iterative procedure with wholly single-precision Coulomb and exchange matrix builds. We also show that the dSCF iterative procedure can be performed with aggressive screening of the pair space. These approximations are tested and found to be accurate for systems with up to 1860 atoms and >10 000 basis functions, providing for immediate overall speedups of up to 70% in the heavily optimized TERACHEM SCF implementation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Characterization of Low Gain Avalanche Detector Prototypes’ Response to Gamma Radiation

Motivated by the need for fast timing detectors to withstand up to 2 MGy of ionizing dose at the High Luminosity Large Hadron Collider, prototype low gain avalanche detectors (LGADs) have been fabricated in a single pad configuration, 2 × 2 arrays, and related p-i-n diodes, and exposed to Co-60 sources for study. Devices were fabricated with a range of dopant layer concentrations, and for the arrays, a variety of inter-pad distances and distances from the active area to the edge. Measurements of capacitance versus voltage and leakage current versus voltage have been made to compare pre- and post-irradiation characteristics in gain layer depletion voltage, full bulk depletion voltage, and breakdown voltage. Conclusions are drawn regarding the effects of the gammas on both surface and interface states and on their contribution to acceptor removal through non-ionizing energy loss from Compton electrons or photoelectrons. Comparison of the performances of members of the set of devices can be used to optimize gain layer parameters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

The Global Optimization of Pt{sub 13} Cluster Using the First-Principle Molecular Dynamics with the Quenching Technique

The high-temperature first-principle molecular dynamics method used to obtain the low energy configurations of clusters [L. L. Wang and D. D. Johnson, PRB 75, 235405 (2007)] is extended to a considerably large temperature range by combination with the quenching technique. Our results show that there are strong correlations between the possibilities for obtaining the ground-state structure and the temperatures. Larger possibilities can be obtained at relatively low temperatures (as corresponds to the pre-melting temperature range). Details of the structural correlation with the temperature are investigated by taking the Pt{sub 13} cluster as an example, which suggests a quite efficient method to obtain the lowest-energy geometries of metal clusters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Post-processing interstitialcy diffusion from molecular dynamics simulations

An algorithm to rigorously trace the interstitialcy diffusion trajectory in crystals is developed. The algorithm incorporates unsupervised learning and graph optimization which obviate the need to input extra domain specific information depending on crystal or temperature of the simulation. The algorithm is implemented in a flexible framework as a post-processor to molecular dynamics (MD) simulations. We describe in detail the reduction of interstitialcy diffusion into known computational problems of unsupervised clustering and graph optimization. We also discuss the steps, computational efficiency and key components of the algorithm. Using the algorithm, thermal interstitialcy diffusion from low to near-melting point temperatures is studied. We encapsulate the algorithms in a modular framework with functionality to calculate diffusion coefficients, migration energies and other trajectory properties. The study validates the algorithm by establishing the conformity of output parameters with experimental values and provides detailed insights for the interstitialcy diffusion mechanism. The algorithm along with the help of supporting visualizations and analysis gives convincing details and a new approach to quantifying diffusion jumps, jump-lengths, time between jumps and to identify interstitials from lattice atoms. -- Graphical abstract:.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Sequential ensemble transform for Bayesian inverse problems

In this work, we present the Sequential Ensemble Transform (SET) method, an approach for generating approximate samples from a Bayesian posterior distribution. The method explores the posterior distribution by solving a sequence of discrete optimal transport problems to produce a series of transport plans which map prior samples to posterior samples. We prove that the sequence of Dirac mixture distributions produced by the SET method converges weakly to the true posterior as the sample size approaches infinity. Furthermore, our numerical results indicate that, when compared to standard Sequential Monte Carlo (SMC) methods, the SET approach is more robust to the choice of Markov mutation kernels and requires less computational efforts to reach a similar accuracy when used to explore complex posterior distributions. Finally, we describe adaptive schemes that allow to completely automate the use of the SET method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Using Higher-Order Modes of Chalcogenide Optical Fibers for the Optimization of Evanescent Wave Mid-IR Spectroscopy

On the basis of a new theoretical approach that is proposed for solving tasks of the evanescent wave mid-IR spectroscopy, it is shown that the dispersion properties of higher-order modes of a multimode chalcogenide optical fiber partly immersed into an absorbing medium can be used for the creation of fiber-optic devices combining the functions of a supercontinuum generator and a sensing element for mid-IR spectroscopic sensors. The propagation of radiation in higher-order modes makes it possible to control the position of zero dispersion of group velocity and to generate supercontinuum with near-IR pumping. Using higher-order evanescent modes in a sensing element, it will be possible to increase its sensitivity and expand the dynamic range.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Si quantum dots in silicon nitride: Quantum confinement and defects

Luminescence of amorphous Si quantum dots (Si QDs) in a hydrogenated silicon nitride (SiN{sub x}:H) matrix was examined over a broad range of stoichiometries from Si{sub 3}N{sub 2.08} to Si{sub 3}N{sub 4.14}, to optimize light emission. Plasma-enhanced chemical vapor deposition was used to deposit hydrogenated SiN{sub x} films with excess Si on Si (001) substrates, with stoichiometry controlled by variation of the gas flow rates of SiH{sub 4} and NH{sub 3} gases. The compositional and optical properties were analyzed by Rutherford backscattering spectroscopy, elastic recoil detection, spectroscopic ellipsometry, photoluminescence (PL), time-resolved PL, and energy-filtered transmission electron microscopy. Ultraviolet-laser-excited PL spectra show multiple emission bands from 400 nm (3.1 eV) to 850 nm (1.45 eV) for different Si{sub 3}N{sub x} compositions. There is a red-shift of the measured peaks from ∼2.3 eV to ∼1.45 eV as Si content increases, which provides evidence for quantum confinement. Higher N content samples show additional peaks in their PL spectra at higher energies, which we attribute to defects. We observed three different ranges of composition where Tauc band gaps, PL, and PL lifetimes change systematically. There is an interesting interplay of defect luminescence and, possibly, small Si QD luminescence observed in the intermediate range of compositions (∼Si{sub 3}N{sub 3.15}) in which the maximum of light emission is observed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Excited states in variational Monte Carlo using a penalty method

Here, in this article, the authors present a technique using variational Monte Carlo to solve for excited states of electronic systems. This technique is based on enforcing orthogonality to lower energy states, which results in a simple variational principle for the excited states. Energy optimization is then used to solve for the excited states. This technique is applied to the well-characterized benzene molecule, in which ~10 000 parameters are optimized for the first 12 excited states. Agreement within ~0.2 eV is obtained with higher scaling coupled cluster methods; small disagreements with experiment are likely due to vibrational effects.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Crystal Engineering of Hydrogen Bonding for Direct Air Capture of CO2: A Quantum Crystallography Perspective

Rising atmospheric CO2 levels demand efficient and sustainable carbon capture solutions. Direct air capture (DAC) via crystallizing hydrogen-bonded frameworks such as carbonate salts has emerged as a promising approach. This review explores the potential of crystal engineering, in tandem with advanced quantum crystallography techniques and computational modeling, to unlock the full potential of DAC materials. We examine the critical role of hydrogen bonding and other noncovalent interactions within a family of bis-guanidines that governs the formation of carbonate salts with high CO2 capture capacity and low regeneration energies for utilization. Quantum crystallography and charge density analysis prove instrumental in elucidating these interactions. A case study of a highly insoluble carbonate salt of a 2,6-pyridine-bis-(iminoguanidine) exemplifies the effectiveness of these approaches. However, challenges remain in the systematic and precise determination of hydrogen atom positions and atomic displacement parameters within DAC materials using quantum crystallography, and limitations persist in the accuracy of current energy estimation models for hydrogen bonding interactions. Future directions lie in exploring diverse functional groups, designing advanced hydrogen-bonded frameworks, and seamlessly integrating experimental and computational modeling with machine learning. This synergistic approach promises to propel the design and optimization of DAC materials, paving the way for a more sustainable future.

36 MATERIALS SCIENCE↗

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