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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Facile alignment estimation in carbon nanotube films using image processing

Whether a macroscopic assembly of carbon nanotubes can exhibit the one-dimensional properties expected from individual nanotubes critically depends on how well the nanotubes are aligned inside the assembly. Therefore, a simple and accurate method for assessing the degree of alignment is desired for the rapid characterization of carbon nanotube films and fibers. Here, we present an end-to-end solution for determining the global and local spatial orientation of carbon nanotubes in films within a short amount of time using a fast, precise, and economical approach based on an image processing method applied to scanning electron microscopy images. Further, we first use Laplacian edge enhancement filtering for improving the appearance of edge regions, which is followed by image partitioning into multiple blocks to capture the nanoscale orientation characteristics and total variation-based image decomposition of these image blocks. We then perform a 2D-fast Fourier transform on the image decomposed textural components of these edge-enhanced image blocks to determine the orientation distribution, which is utilized to estimate the 2D nematic order parameter. To show the effectiveness of our method, we corroborated our results against results obtained with other state-of-the-art image processing and experimental techniques.

2D-FFT↗
DOE OSTI2020

Graph interpolating activation improves both natural and robust accuracies in data-efficient deep learning

Improving the accuracy and robustness of deep neural nets (DNNs) and adapting them to small training data are primary tasks in deep learning (DL) research. In this paper, we replace the output activation function of DNNs, typically the data-agnostic softmax function, with a graph Laplacian-based high-dimensional interpolating function which, in the continuum limit, converges to the solution of a Laplace–Beltrami equation on a high-dimensional manifold. Furthermore, we propose end-to-end training and testing algorithms for this new architecture. The proposed DNN with graph interpolating activation integrates the advantages of both deep learning and manifold learning. Compared to the conventional DNNs with the softmax function as output activation, the new framework demonstrates the following major advantages: First, it is better applicable to data-efficient learning in which we train high capacity DNNs without using a large number of training data. Second, it remarkably improves both natural accuracy on the clean images and robust accuracy on the adversarial images crafted by both white-box and black-box adversarial attacks. Third, it is a natural choice for semi-supervised learning. This paper is a significant extension of our earlier work published in NeurIPS, 2018. For reproducibility, the code is available at https://github.com/BaoWangMath/DNN-DataDependentActivation .

Mathematics↗
DOE OSTI2022

A deterministic gradient-based approach to avoid saddle points

Abstract Loss functions with a large number of saddle points are one of the major obstacles for training modern machine learning (ML) models efficiently. First-order methods such as gradient descent (GD) are usually the methods of choice for training ML models. However, these methods converge to saddle points for certain choices of initial guesses. In this paper, we propose a modification of the recently proposed Laplacian smoothing gradient descent (LSGD) [Osher et al., arXiv:1806.06317 ], called modified LSGD (mLSGD), and demonstrate its potential to avoid saddle points without sacrificing the convergence rate. Our analysis is based on the attraction region, formed by all starting points for which the considered numerical scheme converges to a saddle point. We investigate the attraction region’s dimension both analytically and numerically. For a canonical class of quadratic functions, we show that the dimension of the attraction region for mLSGD is $\lfloor (n-1)/2\rfloor$ , and hence it is significantly smaller than that of GD whose dimension is $n-1$ .

Mathematics↗
DOE OSTI2025

Symmetrization of Strong Hydrogen Bond under High Pressure in Bihydroxide-Ion-Containing NaCu 2 (SO 4 ) 2 ·H 3 O 2 Revealed by Experimental Charge Density, Single-Crystal Electron Diffraction, and Neutron Diffraction Studies

In minerals and inorganic compounds, strong hydrogen bonding can lead to the formation of complex ionic species such as the H 3 O 2 – bihydroxide anion and Zundel cation H 5 O 2 + . We studied [NaCu 2 (SO 4 ) 2 ·H 3 O 2 ] natrochalcite, which contains bihydroxide anions and undergoes hydrogen bond symmetrization at the lowest pressure reported so far among inorganic compounds. Hydrogen bond symmetrization leads to changes in the bulk modulus, seismic wave velocities, and proton mobility and plays a primary role in high-temperature superconductivity, but its characteristics are not well understood due to a lack of systematic studies and limitations of experimental methods sensitive to this subtle change. In this work, we applied experimental charge density analysis based on in situ single-crystal X-ray diffraction data, along with the single-crystal neutron and electron diffraction experiments, to probe the behavior of hydrogen atoms during the hydrogen bond symmetrization process under high-pressure conditions. On the way to the symmetrical H-bonding, natrochalcite undergoes a series of complex redistributions of electron density, which we trace with multipole refinement and detailed analysis of changes in the Laplacian of electron density values. Additionally, we deconvoluted the equation of state (volume of the unit cell vs pressure relation) into the atomic equation of states describing dependencies of atomic charges or volumes vs pressure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Suppressed Daytime Convection Over the Amazon River

We investigated the interaction between surface conditions and precipitating convection by comparing the Amazon River against the surrounding forest. Despite similar synoptic conditions within a few tens of kilometers, the river surface is substantially cooler than the surrounding forest during the day and warmer at night. We analyzed 20 years of high-resolution satellite precipitation data and confirmed previous findings of daytime rainfall reduction over the river for the whole Amazon Basin. The percentage reduction is strongest during the dry-to-wet transition season. In addition, the percentage reduction of individual tributary is significantly correlated with the Laplacian of surface temperature, which causes thermally driven surface divergence and suppresses local convection. Additionally, nighttime rainfall is enhanced over tributaries near the Atlantic coast during the wet season. A regional climate model then simulates the local rainfall anomalies associated with the river. Above the river, moisture diverges near the surface and converges above the surface before the daytime rainfall, partially driven by the horizontal gradient of humidity. Unlike the river, moisture convergence within the boundary layer is more critical for the rainfall above the forest region. Our studies suggest that strong thermal contrast can be important in deriving heterogeneous convection in moist tropical regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Surface Variability Mapping and Roughness Analysis of the Moon Using a Coarse–Graining Decomposition

The lunar surface contains a wide variety of topographic shapes and features, each with different distributions and scales, and any analysis technique to objectively measure roughness must respect these qualities. Coarse-graining is a naturally scale-dependent filtering technique that preserves scale-dependent symmetries and produces coarse elevation maps that gradually erase the smaller features from the original topography. In this study of the lunar surface, we present two surface variability metrics obtained from coarse-graining lunar topography: fine elevation and coarse curvature. Both metrics are isotropic, deterministic, slope-independent, and coordinate-agnostic. Fine (detrended) elevation is acquired by subtracting the coarse elevation from the original topography and contains features that are smaller than the coarse-graining length-scale. Coarse curvature is the Laplacian of coarsened topography, and naturally quantifies the curvature at any scale and indicates whether a location is elevated or depressed relative to its neighborhood at that scale. We find that highlands and maria have distinct roughness characteristics at all length-scales. Our topographic spectra reveal four scale-breaks that mark characteristic shifts in surface roughness: 100, 300, 1,000, and 4,000 km. Comparing fine elevation distributions between maria and highlands, we show that maria fine elevation is biased toward smaller-magnitude elevations and that the maria–highland discrepancies are more pronounced at larger length-scales. Here, we also provide local examples of selected regions to demonstrate that these metrics can successfully distinguish geological features of different length-scales.

58 GEOSCIENCES↗
DOE OSTI2022

SIMILE enables alignment of tandem mass spectra with statistical significance

Abstract Interrelating small molecules according to their aligned fragmentation spectra is central to tandem mass spectrometry-based untargeted metabolomics. Current alignment algorithms do not provide statistical significance and compounds that have multiple delocalized structural differences and therefore often fail to have their fragment ions aligned. Here we align fragmentation spectra with both statistical significance and allowance for multiple chemical differences using Significant Interrelation of MS/MS Ions via Laplacian Embedding (SIMILE). SIMILE yields spectral alignment inferred structural connections in molecular networks that are not found with cosine-based scoring algorithms. In addition, it is now possible to rank spectral alignments based on p-values in the exploration of structural relationships between compounds and enhance the chemical connectivity that can be obtained with molecular networking.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Quantum computing and preconditioners for hydrological linear systems

Modeling hydrological fracture networks is a hallmark challenge in computational earth sciences. Accurately predicting critical features of fracture systems, e.g. percolation, can require solving large linear systems far beyond current or future high performance capabilities. Quantum computers can theoretically bypass the memory and speed constraints faced by classical approaches, however several technical issues must first be addressed. Chief amongst these difficulties is that such systems are often ill-conditioned, i.e. small changes in the system can produce large changes in the solution, which can slow down the performance of linear solving algorithms. We test several existing quantum techniques to improve the condition number, but find they are insufficient. We then introduce the inverse Laplacian preconditioner, which improves the scaling of the condition number of the system from O(N) to O($\sqrt{N}$) and admits a quantum implementation. These results are a critical first step in developing a quantum solver for fracture systems, both advancing the state of hydrological modeling and providing a novel real-world application for quantum linear systems algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

It is widely known that neural networks (NNs) are universal approximators of continuous functions. However, a less known but powerful result is that a NN with a single hidden layer can accurately approximate any nonlinear continuous operator. This universal approximation theorem of operators is suggestive of the structure and potential of deep neural networks (DNNs) in learning continuous operators or complex systems from streams of scattered data. Here, in this work, we thus extend this theorem to DNNs. We design a new network with small generalization error, the deep operator network (DeepONet), which consists of a DNN for encoding the discrete input function space (branch net) and another DNN for encoding the domain of the output functions (trunk net). We demonstrate that DeepONet can learn various explicit operators, such as integrals and fractional Laplacians, as well as implicit operators that represent deterministic and stochastic differential equations. We study different formulations of the input function space and its effect on the generalization error for 16 different diverse applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Physics-based reward driven image analysis in microscopy

The rise of electron microscopy has expanded our ability to acquire nanometer and atomically resolved images of complex materials. The resulting vast datasets are typically analyzed by human operators, an intrinsically challenging process due to the multiple possible analysis steps and the corresponding need to build and optimize complex analysis workflows. We present a methodology based on the concept of a Reward Function coupled with Bayesian Optimization, to optimize image analysis workflows dynamically. The Reward Function is engineered to closely align with the experimental objectives and broader context and is quantifiable upon completion of the analysis. Here, cross-section, high-angle annular dark field (HAADF) images of ion-irradiated (Y, Dy)Ba 2 Cu 3 O 7–δ thin-films were used as a model system. The reward functions were formed based on the expected materials density and atomic spacings and used to drive multi-objective optimization of the classical Laplacian-of-Gaussian (LoG) method. These results can be benchmarked against the DCNN segmentation. This optimized LoG* compares favorably against DCNN in the presence of the additional noise. We further extend the reward function approach towards the identification of partially-disordered regions, creating a physics-driven reward function and action space of high-dimensional clustering. We pose that with correct definition, the reward function approach allows real-time optimization of complex analysis workflows at much higher speeds and lower computational costs than classical DCNN-based inference, ensuring the attainment of results that are both precise and aligned with the human-defined objectives.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Maximum entropy distributions of dark matter in ΛCDM cosmology

Context. Small-scale challenges to ΛCDM cosmology require a deeper understanding of dark matter physics. Aims. This paper aims to develop the maximum entropy distributions for dark matter particle velocity (denoted by X ), speed (denoted by Z ), and energy (denoted by E ) that are especially relevant on small scales where system approaches full virialization. Methods. For systems involving long-range interactions, a spectrum of halos of different sizes is required to form to maximize system entropy. While the velocity in halos can be Gaussian, the velocity distribution throughout the entire system, involving all halos of different sizes, is non-Gaussian. With the virial theorem for mechanical equilibrium, we applied the maximum entropy principle to the statistical equilibrium of entire system, such that the maximum entropy distribution of velocity (the X distribution) could be analytically derived. The halo mass function was not required in this formulation, but it did indeed result from the maximum entropy. Results. The predicted X distribution involves a shape parameter α and a velocity scale, v 0 . The shape parameter α reflects the nature of force ( α → 0 for long-range force or α → ∞ for short-range force). Therefore, the distribution approaches Laplacian with α → 0 and Gaussian with α → ∞. For an intermediate value of α , the distribution naturally exhibits a Gaussian core for v ≪ v 0 and exponential wings for v ≫ v 0 , as confirmed by N -body simulations. From this distribution, the mean particle energy of all dark matter particles with a given speed, v , follows a parabolic scaling for low speeds (∝ v 2 for v ≪ v 0 in halo core region, i.e., “Newtonian”) and a linear scaling for high speeds (∝ v for v ≫ v 0 in halo outskirt, i.e., exhibiting “non-Newtonian” behavior due to long-range gravity). We compared our results against N -body simulations and found a good agreement.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Filling data analysis gaps in time-resolved crystallography by machine learning

There is a growing understanding of the structural dynamics of biological molecules fueled by x-ray crystallography experiments. Time-resolved serial femtosecond crystallography (TR-SFX) with x-ray Free Electron Lasers allows the measurement of ultrafast structural changes in proteins. Nevertheless, this technique comes with some limitations. One major challenge is the quality of data from TR-SFX measurements, which often faces issues like data sparsity, partial recording of Bragg reflections, timing errors, and pixel noise. To overcome these difficulties, conventionally, large volumes of data are collected and grouped into a few temporal bins. The data in each bin are then averaged and paired with the mean of their corresponding jittered timestamps. This procedure provides one structure per bin, resulting in a limited number of averaged structures for the entire time interval spanned by the experiment. Therefore, the information on ultrafast structural dynamics at high temporal resolution is lost. This has initiated research for advanced methods of analyzing experimental TR-SFX data beyond the standard binning and averaging method. To address this problem, we use a machine learning algorithm called Nonlinear Laplacian Spectral Analysis (NLSA), which has emerged as a promising technique for studying the dynamics of complex systems. In this work, we demonstrate the power of this algorithm using synthetic x-ray diffraction snapshots from a protein with significant data incompleteness, timing uncertainties, and noise. Our study confirms that NLSA is a suitable approach that effectively mitigates the effects of these artifacts in TR-SFX data and recovers accurate structural dynamics information hidden in such data.

Trujillo, Justin (ORCID:0000000285505360)↗
DOE OSTI2020

The concept of spin ice graphs and a field theory for their charges

Originally detected in rare earth pyrochlores, spin ice physics is now being artificially extended to a variety of geometries that control collective behavior and exotic properties, making graph theory their proper framework. We relate spin ice notions, such as ice rule, ice manifold, Coulomb phases, charges, and monopoles, to graph-theoretical notions, such as balance, in/out-degrees, and Euler paths. We then propose a field-theoretical treatment in which topological charges and monopoles are the degrees of freedom, while the binary spins are subsumed in an entropic interaction among charges. We show that for a spin ice on a graph in a Gaussian approximation, the kernel of the entropic interaction is the inverse of the graph Laplacian, and we compute screening functions from the graph spectra as Green operators for the screened Poisson problem on a graph. We then apply the treatment to star graphs, tournaments, cycles, and regular spin ice in different dimensions. Our aim is twofold: to set spin ice physics in a proper graph setting, where only topological rather than geometrical notions hold, and to invite graph theorists to contribute their powerful tools to the field of spin ice.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Faster network disruption from layered oscillatory dynamics

Nonlinear complex network-coupled systems typically have multiple stable equilibrium states. Following perturbations or due to ambient noise, the system is pushed away from its initial equilibrium, and, depending on the direction and the amplitude of the excursion, it might undergo a transition to another equilibrium. It was recently demonstrated [M. Tyloo, J. Phys. Complex. 3 03LT01 (2022)] that layered complex networks may exhibit amplified fluctuations. Here, I investigate how noise with system-specific correlations impacts the first escape time of nonlinearly coupled oscillators. Interestingly, I show that, not only the strong amplification of the fluctuations is a threat to the good functioning of the network but also the spatial and temporal correlations of the noise along the lowest-lying eigenmodes of the Laplacian matrix. Finally, I analyze first escape times on synthetic networks and compare noise originating from layered dynamics to uncorrelated noise.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Efficient real space formalism for hybrid density functionals

We present an efficient real space formalism for hybrid exchange-correlation functionals in generalized Kohn–Sham density functional theory (DFT). In particular, we develop an efficient representation for any function of the real space finite-difference Laplacian matrix by leveraging its Kronecker product structure, thereby enabling the time to solution of associated linear systems to be highly competitive with the fast Fourier transform scheme while not imposing any restrictions on the boundary conditions. We implement this formalism for both the unscreened and range-separated variants of hybrid functionals. We verify its accuracy and efficiency through comparisons with established planewave codes for isolated as well as bulk systems. In particular, we demonstrate up to an order-of-magnitude speedup in time to solution for the real space method. We also apply the framework to study the structure of liquid water using ab initio molecular dynamics, where we find good agreement with the literature. Overall, the current formalism provides an avenue for efficient real-space DFT calculations with hybrid density functionals.

Chemistry↗
DOE OSTI2026

Polynomial-time preparation of low-temperature Gibbs states for two-dimensional toric code

In this work, we propose a polynomial-time algorithm for preparing the Gibbs state of the two-dimensional toric code Hamiltonian at any temperature, starting from any initial state, significantly improving upon prior estimates that suggested exponential scaling with inverse temperature. We prove that fast mixing at low temperature for the two-dimensional toric code can be achieved by augmenting local jump operators with simple global jump operators, which enable efficient transitions between logical sectors. To establish tight lower bounds on the spectral gap, we introduce a new reduction method that eventually maps the problem to estimating the spectral gap of a perturbed graph Laplacian on a stair graph. Our proof also shows that the Lindblad dynamics with a digitally implemented low-temperature local Davies generator is able to efficiently drive the quantum state toward the ground state manifold.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Electric field distribution in a non-self-sustained RF discharge with ionization generated by Ns discharge pulses

Abstract Electric field in a capacitively coupled, non-self-sustained RF discharge nitrogen plasma with external ionization generated by high-voltage ns pulses has been measured by ps Electric field induced second harmonic generation (EFISH). The measurements are made both in the bulk of the plasma and in the sheaths, using the absolute calibration by the Laplacian field between two plane electrodes. The results are compared with the kinetic modeling calculations. The RF electric field in the sheaths is significantly higher compared to that in the plasma, due to the displacement of the electrons by the drift oscillations and the resultant plasma self-shielding. However, the kinetic modeling predictions indicate that the electron impact ionization in the sheaths is largely ineffective, due to the low electron density. The reduction of the electric field in the plasma by the self-shielding in the sheaths is moderate, such that the energy is coupled to the plasma by the below-breakdown RF field. The peak RF field in the plasma is in the range of 15–25 Td, indicating the efficient vibrational excitation of N 2 by electron impact. The modeling predictions suggest that the targeted vibrational excitation of molecular species in a non-self-sustained RF discharge with external ionization is scalable to high pressures, electron densities, and discharge powers. The present approach can be extended to the vibrational excitation of other molecular species where the vibrational relaxation is relatively slow, such as CO, CO 2 , and H 2 .

Physics↗
DOE OSTI2024

Cosmic topology. Part IIa. Eigenmodes, correlation matrices, and detectability of orientable Euclidean manifolds

Abstract If the Universe has non-trivial spatial topology, observables depend on both the parameters of the spatial manifold and the position and orientation of the observer. In infinite Euclidean space, most cosmological observables arise from the amplitudes of Fourier modes of primordial scalar curvature perturbations. Topological boundary conditions replace the full set of Fourier modes with specific linear combinations of selected Fourier modes as the eigenmodes of the scalar Laplacian. We present formulas for eigenmodes in orientable Euclidean manifolds with the topologiesE 1 –E 6 ,E 11 ,E 12 ,E 16 , andE 18 that encompass the full range of manifold parameters and observer positions, generalizing previous treatments. Under the assumption that the amplitudes of primordial scalar curvature eigenmodes are independent random variables, for each topology we obtain the correlation matrices of Fourier-mode amplitudes (of scalar fields linearly related to the scalar curvature) and the correlation matrices of spherical-harmonic coefficients of such fields sampled on a sphere, such as the temperature of the cosmic microwave background (CMB). We evaluate the detectability of these correlations given the cosmic variance of the observed CMB sky. We find that topologies where the distance to our nearest clone is less than about 1.2 times the diameter of the last scattering surface of the CMB give a correlation signal that is larger than cosmic variance noise in the CMB. This implies that if cosmic topology is the explanation of large-angle anomalies in the CMB, then the distance to our nearest clone is not much larger than the diameter of the last scattering surface. We argue that the topological information is likely to be better preserved in three-dimensional data, such as will eventually be available from large-scale structure surveys.

Astronomy & Astrophysics↗