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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 1,711 records · Page 95
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Understanding copper diffusion in CuInSe 2 with first-principles based atomistic and continuum models

We investigate the diffusion of copper in CuInSe 2 using thermodynamic and kinetic models based on density functional theory calculations, attempting to reconcile large differences in reported experimental diffusivities. We find that observations of rapid chemical diffusion can be explained by large thermodynamic factors, which we calculate using a compositionally constrained model of intrinsic point defect formation. We further characterize how copper diffusion coefficients depend on material synthesis conditions and exhibit their variation across the CuInSe 2 secondary phase diagram. In doing so, we identify stable off-stoichiometries that are dominated by either vacancy- or interstitial-mediated diffusion mechanisms. These results are employed in the development of a continuum reaction–diffusion model, which we use to simulate experimental depth profiles.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules

Abstract Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models. In chemistry, ML has been used to develop models for predicting molecular properties, for example quantum mechanics (QM) calculated potential energy surfaces and atomic charge models. The ANI-1x and ANI-1ccx ML-based general-purpose potentials for organic molecules were developed through active learning; an automated data diversification process. Here, we describe the ANI-1x and ANI-1ccx data sets. To demonstrate data diversity, we visualize it with a dimensionality reduction scheme, and contrast against existing data sets. The ANI-1x data set contains multiple QM properties from 5 M density functional theory calculations, while the ANI-1ccx data set contains 500 k data points obtained with an accurate CCSD(T)/CBS extrapolation. Approximately 14 million CPU core-hours were expended to generate this data. Multiple QM calculated properties for the chemical elements C, H, N, and O are provided: energies, atomic forces, multipole moments, atomic charges, etc. We provide this data to the community to aid research and development of ML models for chemistry.

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

GdWN 3 is a nitride perovskite

Nitride perovskites ABN3 are an emerging and highly underexplored class of materials that are of interest due to their intriguing calculated ferroelectric, optoelectronic, and other functional properties. Incorporating novel A-site cations is one strategy to tune and expand such properties; for example, Gd 3+ is compelling due to its large magnetic moment, potentially leading to multiferroic behavior. However, the theoretically predicted ground state of GdWN 3 was a non-perovskite monoclinic structure. Here, we experimentally show that GdWN 3-y crystallizes in a perovskite structure. High-throughput combinatorial sputtering with activated nitrogen is employed to synthesize thin films of Gd 2-x W x N 3-y O y with oxygen content y < 0.05. Ex situ annealing crystallizes a polycrystalline perovskite phase in a narrow composition window near x = 1. LeBail fits of synchrotron grazing incidence wide angle x-ray scattering data are consistent with a perovskite ground-state structure. Refined density functional theory calculations that included antiferromagnetic configurations confirm that the ground-state structure of GdWN 3 is a distorted Pnma perovskite with antiferromagnetic ordering, in contrast to prior predictions. Initial property measurements find that GdWN 3-y is paramagnetic down to T = 2 K with antiferromagnetic correlations and that the absorption onset depends on cation stoichiometry. This work provides an important path toward both the rapid expansion of the emerging family of nitride perovskites and understanding their potential multiferroic properties.

14 SOLAR ENERGY↗
DOE OSTI2020

The tensor t‐function: A definition for functions of third‐order tensors

Summary A definition for functions of multidimensional arrays is presented. The definition is valid for third‐order tensors in the tensor t‐product formalism, which regards third‐order tensors as block circulant matrices. The tensor function definition is shown to have similar properties as standard matrix function definitions in fundamental scenarios. To demonstrate the definition's potential in applications, the notion of network communicability is generalized to third‐order tensors and computed for a small‐scale example via block Krylov subspace methods for matrix functions. A complexity analysis for these methods in the context of tensors is also provided.

Lund, Kathryn↗
DOE OSTI2020

Hole-Trapping-Induced Stabilization of Ni 4+ in SrNiO 3 /LaFeO 3 Superlattices

Creating new functionality in materials containing transition metals is predicated on our ability to control the associated charge states. For a given metal, forming lower oxidation states is typically easier than generating higher values, and there is an upper limit on valence that is not exceeded under normal conditions. Here we demonstrate that we can exceed the normal upper limit of 3+ for Ni and Fe via synthesis of (SrNiO 3 )m/(LaFeO 3 )n superlattices by tuning n and m. The Goldschmidt tolerance constraints are lifted and SrNi 4+ O 3 with holes on adjacent O anions can be stabilized as a perovskite at the single unit cell level (m = 1). Spectroscopy reveals that the n = 1 superlattice contains Ni 3+ and Fe 4+ , whereas Ni 4+ and Fe 3+ are observed in the n = 5 superlattice. Here, our results show that the B-site cation valences can be tuned by controlling the magnitude of the FeO6 octahedral rotations which in turn drive the energy balance between Ni 3+ /Fe 4+ and Ni 4+ /Fe 3+ , thus controlling emergent electrical properties such as the band alignment and resulting hole confinement. We suggest that this approach may be generally useful for synthesizing novel, metastable layered structures with new functionalities.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Radical Coupling Reactions of Hydroxystilbene Glucosides and Coniferyl Alcohol: A Density Functional Theory Study

The monolignols, p-coumaryl, coniferyl, and sinapyl alcohol, arise from the general phenylpropanoid biosynthetic pathway. Increasingly, however, authentic lignin monomers derived from outside this process are being identified and found to be fully incorporated into the lignin polymer. Among them, hydroxystilbene glucosides, which are produced through a hybrid process that combines the phenylpropanoid and acetate/malonate pathways, have been experimentally detected in the bark lignin of Norway spruce (Picea abies). Several interunit linkages have been identified and proposed to occur through homo-coupling of the hydroxystilbene glucosides and their cross-coupling with coniferyl alcohol. In the current work, the thermodynamics of these coupling modes and subsequent rearomatization reactions have been evaluated by the application of density functional theory (DFT) calculations. The objective of this paper is to determine favorable coupling and cross-coupling modes to help explain the experimental observations and attempt to predict other favorable pathways that might be further elucidated via in vitro polymerization aided by synthetic models and detailed structural studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

DeePKS + ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials

Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for different levels of QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training an ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn–Sham (DeePKS), an ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model and then use the DeePKS model to label a much larger amount of configurations to train an ML potential. Further, this scheme for periodic systems is implemented in a DFT package ABACUS, which is open source and ready for use in various applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Photoinduced electric effects in various plasmonic materials

Photoinduced voltages associated with surface plasmon polariton excitations are studied both theoretically and experimentally in various plasmonic systems as the function of material, wavelength, and type of structure. Experimental photovoltage normalized to the absorbed power shows a general decrease upon an increase in the wavelength, enhancement in the nanostructured samples, and a strong variation in the magnitude as a function of the material, which are not in line with the theoretical predictions of the simple plasmonic pressure approach. Furthermore, the results can be used for clarification of the mechanisms and further development of an adequate theoretcial approach to the plasmon drag effect.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS1997

PROFILE: Airfoil Geometry Manipulation and Display

This report provides user information for program PROFILE, an aerodynamics design utility for plotting, tabulating, and manipulating airfoil profiles. A dozen main functions are available. The theory and implementation details for two of the more complex options are also presented. These are the REFINE option, for smoothing curvature in selected regions while retaining or seeking some specified thickness ratio, and the OPTIMIZE option, which seeks a specified curvature distribution. Use of programs QPLOT and BPLOT is also described, since all of the plots provided by PROFILE (airfoil coordinates, curvature distributions, pressure distributions)) are achieved via the general-purpose QPLOT utility. BPLOT illustrates (again, via QPLOT) the shape functions used by two of PROFILE's options. These three utilities should be distributed as one package. They were designed and implemented for the Applied Aerodynamics Branch at NASA Ames Research Center, Moffett Field, California. They are all written in FORTRAN 77 and run on DEC and SGI systems under OpenVMS and IRIX.

Collins, Leslie↗
DOE OSTI2022

Hypothesis testing via AI: Generating physically interpretable models of scientific data with machine learning (Full Technical Report)

Deep learning has demonstrated an exceptional ability to solve complex tasks (an engineering success); however, it has done so at the expense of the ability to generate new knowledge (a scientific failure). We propose an alternative framework—entitled Deep Symbolic Regression (DSR)—in which artificial neural networks (NNs) rapidly generate hypotheses about physical relationships among inputs. This framework bypasses the need to interpret an NN altogether, while still leveraging the representational power of deep learning. The resulting models are tractable mathematical expressions, which are inherently and readily human interpretable and can provide insights into underlying physical phenomena. Further, we fold this methodology into the scientific process by allowing the scientist to directly integrate a priori knowledge and beliefs to accelerate learning. We demonstrate this methodology on symbolic regression—the problem of rediscovering underlying expressions describing a dataset—and achieve state-of-the-art performance across a wide variety of symbolic regression problems. Further, we generalize our DSR framework to apply to the more general class of symbolic optimization problems, in which one seeks to optimize a sequence of symbols or “tokens” under a black-box reward function. Examples of other symbolic optimization problems include neural architecture search and computational antibody design. Our generalized tool, Deep Symbolic Optimization (DSO), has been demonstrated on the task of learning symbolic control policies for reinforcement learning environments, and has been adopted as an enabling capability for computational antibody design.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1988

Programmable pipelined image processor

A pipelined image processor selectively interconnects modules in a column of a two-dimensional array to modules of the next column of the array of modules 1,1 through M,N, where M is the number of modules in one dimension and N is the number of modules in the other direction. Each module includes two input selectors for A and B inputs, two convolvers, a binary function operator, a neighborhood comparison operator which produces an A output and an output selector which may select as a B output the output of any one of the components in the module, including the A output of the neighborhood comparison operator. Each module may be connected to as many as eight modules in the next column, preferably with the majority always in a different row that is up (or down) in the array for a generally spiral data path around the torus thus formed. The binary function operator is implemented as a look-up table addressed by the most significant 8 bits of each 12-bit argument. The table output includes a function value and the slopes for interpolation of the two arguments by multiplying the 4 least significant bits in multipliers and adding the products to the function value through adders.

Donald B Gennery↗
NASA NTRS2004

2004: Finite-Difference Time Domain Solution of Light Scattering by an Infinite Dielectric Column Immersed in an Absorbing Medium

The two-dimensional (2-D) finite-difference time domain (FDTD) method is applied to calculate light scattering and absorption by an arbitrarily shaped infinite column embedded in an absorbing dielectric medium. A uniaxial perfectly matched layer (UPML) absorbing boundary condition (ABC) is used to truncate the computational domain. The single-scattering properties of the infinite column embedded in the absorbing medium, including scattering phase functions, extinction and absorption efficiencies, are derived using an area integration of the internal field. An exact solution for light scattering and absorption by a circular cylinder in an absorbing medium is used to examine the accuracy of the 2-D UPML FDTD code. With use of a cell size of 1/120 incident wavelength in the FDTD calculations, the errors in the extinction and absorption efficiencies and asymmetry factors from the 2-D UPML FDTD are generally smaller than approx .1%. The errors in the scattering phase functions are typically smaller than approx .4%. Using the 2-D UPML FDTD technique, light scattering and absorption by long noncircular columns embedded in absorbing media can be accurately solved.

Sun, W.↗
DOE OSTI2022

Spin pumping from antiferromagnetic insulator spin-orbit-proximitized by adjacent heavy metal: a first-principles Floquet-nonequilibrium Green function study

Motivated by the recent experiment on spin pumping from sub-THz radiation-driven uniaxial antiferromagnetic insulator (AFI) MnF 2 into heavy metal (HM) Pt hosting strong spin-orbit (SO) coupling, we compute and compare pumped spin currents in Cu/MnF 2 /Cu and Pt/MnF 2 /Cu heterostructures. Recent theories of spin pumping by AFI have relied on simplistic Hamiltonians (such as tight-binding) and the scattering approach to quantum transport yielding the so-called interfacial spin mixing conductance (SMC), but the concept of SMC ceases to be applicable when SO coupling is present directly at the interface. In contrast, we use a more general first-principles quantum transport approach which combines noncollinear density functional theory with Floquet-nonequilibrium Green's functions in order to take into account: SO-proximitized AFI as a new type of quantum material, different from isolated AFI and brought about by AFI hybridization with adjacent HM layer; strong SO coupling at the interface; and evanescent wavefunctions penetrating from Pt or Cu into AFI layer to make its interfacial region conducting rather than insulating as in the isolated AFI. The DC component of pumped spin current $I_\mathrm{DC}^{S_z}$ vs. precession cone angle $\theta_{\boldsymbol{l}}$ of the Néel vector l of AFI does not follow putative $I^{S_z}_\mathrm{DC} \propto \sin^2 \theta_{\boldsymbol{l}}$, except for very small angles $\theta_{\boldsymbol{l}} \lesssim 10^\circ$ for which we define an effective SMC from the prefactor and find that it doubles from MnF2/Cu to MnF2/Pt interface. In addition, the angular dependence $I^{S_z}_\mathrm{DC}(\theta_{\boldsymbol{l}})$ differs for opposite directions of precession of the Néel vector, leading to twice as large SMC for the right-handed than for the left-handed chirality of the precession mode.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A high-performance implementation of atomistic spin dynamics simulations on x86 CPUs

Atomistic spin dynamics simulations provide valuable information about the energy spectrum of magnetic materials in different phases, allowing one to identify instabilities and the nature of their excitations. However, the time cost of evaluating the dynamical correlation function S(q,t) increases quadratically as the number of spins N, leading to significant computational effort, making the simulation of large spin systems very challenging. In this work, here, we propose to use a highly optimized general matrix multiply (GEMM) subroutine to calculate the dynamical spin-spin correlation function that can achieve near-optimal hardware utilization. Furthermore, we fuse the element-wise operations in the calculation of S(q,t) into the in-house GEMM kernel, which results in further performance improvements of 44\% - 71\% on several relatively large lattice sizes when compared to the implementation that uses the GEMM subroutine in OpenBLAS, which is the state-of-the-art open source library for Basic Linear Algebra Subroutine (BLAS).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Structure Sensitive Reaction Kinetics of Chiral Molecules on Intrinsically Chiral Surfaces

Enantiospecific heterogeneous catalysis utilizes chiral surfaces to resolve enantiomers via structure sensitive surface chemistry. The catalyst design challenge is the identification of chiral surface structures that maximize enantiospecificity. Herein, we develop data driven models for the enantiospecificity of tartaric acid reactions on chiral Cu(hkl) R&S surfaces. Measurements of enantiospecific rate constants were obtained by using curved Cu(hkl) R&S surfaces that enable kinetic measurements on hundreds of chiral surface orientations. One model uses feature vectors derived from generalized coordination numbers to capture the local structure around Cu atoms exposed by the Cu(hkl) R&S surfaces. The second model introduces the use of chiral cubic harmonic functions to capture the symmetry constraints of the face-centered cubic Cu structure. The model using 58 generalized coordination numbers has a fitting error similar to that of the model using only 5 cubic harmonic functions. The two models predict maxima in the enantiospecificity on surfaces with very similar surface orientations. The models developed in this work are applicable for any enantiospecific reaction happening on any chiral material with a cubic lattice structure, opening the way to understanding the surface structure sensitivity of the enantiospecific reaction kinetics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1989

Global models: Robot sensing, control, and sensory-motor skills

Robotics research has begun to address the modeling and implementation of a wide variety of unstructured tasks. Examples include automated navigation, platform servicing, custom fabrication and repair, deployment and recovery, and science exploration. Such tasks are poorly described at onset; the workspace layout is partially unfamiliar, and the task control sequence is only qualitatively characterized. The robot must model the workspace, plan detailed physical actions from qualitative goals, and adapt its instantaneous control regimes to unpredicted events. Developing robust representations and computational approaches for these sensing, planning, and control functions is a major challenge. The underlying domain constraints are very general, and seem to offer little guidance for well-bounded approximation of object shape and motion, manipulation postures and trajectories, and the like. This generalized modeling problem is discussed, with an emphasis on the role of sensing. It is also discussed that unstructured tasks often have, in fact, a high degree of underlying physical symmetry, and such implicit knowledge should be drawn on to model task performance strategies in a methodological fashion. A group-theoretic decomposition of the workspace organization, task goals, and their admissible interactions are proposed. This group-mechanical approach to task representation helps to clarify the functional interplay of perception and control, in essence, describing what perception is specifically for, versus how it is generically modeled. One also gains insight how perception might logically evolve in response to needs of more complex motor skills. It is discussed why, of the many solutions that are often mathematically admissible to a given sensory motor-coordination problem, one may be preferred over others.

Schenker, Paul S.↗
DOE OSTI2025

Inclusive open charm photoproduction in ultraperipheral collisions at the LHC with in the generalized photon-nucleus fixed-order next-to-leading logarithm framework

We compute the inclusive 𝐷 0 production cross section in ultraperipheral Pb-Pb collisions at the LHC as a function of the 𝐷 0 transverse momentum and rapidity. These calculations are carried out within the new generalized photon-nucleus FONLL (G⁡𝛾⁢A−FONLL) framework, which can predict photonuclear cross sections for charm and beauty hadrons in electron-proton, electron-nucleus, and ultraperipheral heavy-ion collisions. The framework relies on fixed-order next-to-leading logarithm (FONLL) to model heavy-quark production in photonuclear collisions and employs a photon-flux reweighting procedure to describe the production cross sections in ultraperipheral heavy-ion collisions. The G⁡𝛾⁢A calculations are first validated against the photoproduction cross sections of 𝐷* in electron-proton collisions at HERA. The predictions for the 𝐷 0 production cross section in ultraperipheral Pb-Pb collisions at the LHC are then presented and compared to the first experimental results obtained by CMS at $\sqrt{s_{NN}}$ = 5.36 ⁢TeV. The predictions are benchmarked against different choices of nuclear parton distribution functions, fragmentation functions, and renormalization and factorization scales.

Parton distribution functions↗
NASA NTRS2003

Simultaneous Ocean Wave Measurements by the Jason and Topex Satellites, With Buoy and Model Comparisons

The verification phase of the Jason-1 satellite altimeter mission presents a unique opportunity for comparing near-simultaneous, independent satellite measurements. We here examine simultaneous significant wave height measurements by the Jason-1 and Topex/Poseidon altimeters. These data are also compared with in-situ measurements from deep-ocean buoys and with predicted wave heights from the Wave Watch 111 operational model. The rms difference between Jason and Topex wave heights is 21 cm, and this can be further lowered by application of median filters to reduce high-frequency noise. This noise is slightly larger in the Jason dataset, amounting to about 7 cm rms for frequencies above 0.05 Hz, which is the frequency at which the coherence between Topex and Jason measurements drops to zero. The probability density function for Jason shows a dearth of small waves relative to Topex. Buoy comparisons confirm that this problem lies with the Jason measurements. The buoy comparisons confirm previous reports that Topex wave heights are roughly 5% smaller than buoy measurements for waves between 2 and 5m; Jason heights in general are 2.7% smaller than Topex. Spurious dips in the Topex density function for 3- and 6-meter waves, a problem that has existed since the beginning of the mission, can be solved by waveform retracking..

Ray, Richard D.↗