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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Quantitative interpretation of molecular dynamics simulations for X-ray photoelectron spectroscopy of aqueous solutions

Over the past decade, energy-dependent ambient pressure X-ray photoelectron spectroscopy (XPS) has emerged as a powerful analytical probe of the ion spatial distributions at the vapor (vacuum)-aqueous electrolyte interface. These experiments are often paired with complementary molecular dynamics (MD) simulations in an attempt to provide a complete description of the liquid interface. There is, however, no systematic protocol that permits a straightforward comparison of the two sets of results. XPS is an integrated technique that averages signals from multiple layers in a solution even at the lowest photoelectron kinetic energies routinely employed, whereas MD simulations provide a microscopic layer-by-layer description of the solution composition near the interface. Here, we use the National Institute of Standards and Technology database for the Simulation of Electron Spectra for Surface Analysis (SESSA) to quantitatively interpret atom-density profiles from MD simulations for XPS signal intensities using sodium and potassium iodide solutions as examples. We show that electron inelastic mean free paths calculated from a semi-empirical formula depend strongly on solution composition, varying by up to 30% between pure water and concentrated NaI. The XPS signal thus arises from different information depths in different solutions for a fixed photoelectron kinetic energy. XPS signal intensities are calculated using SESSA as a function of photoelectron kinetic energy (probe depth) and compared with a widely employed ad hoc method. SESSA simulations illustrate the importance of accounting for elastic-scattering events at low photoelectron kinetic energies (<300 eV) where the ad hoc method systematically underestimates the preferential enhancement of anions over cations. Finally, some technical aspects of applying SESSA to liquid interfaces are discussed.

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

Natural ionization orbitals for interpreting electron detachment processes

A compact orbital representation of ionization processes is described utilizing the difference of calculated one-particle density matrices. Natural orbital analysis involving this difference density matrix simplifies interpretation of electronic detachment processes and allows differentiation between one-electron transitions and shake-up/shake-off transitions, in which one-electron processes are accompanied by excitation of a second electron into the virtual orbital space.

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

Evidence for a liquid-liquid critical point in supercooled water within the E3B3 model and a possible interpretation of the kink in the homogeneous nucleation line

Supercooled water exhibits many thermodynamic anomalies, and several scenarios have been proposed to interpret them, among which the liquid-liquid critical point (LLCP) hypothesis is the most commonly discussed. We investigated Widom lines and the LLCP of deeply supercooled water, by using molecular dynamics simulation with a newly reparameterized water model that explicitly includes three-body interactions. Seven isobars are studied from ambient pressure to 2.5 kbar, and Widom lines are identified by calculating maxima in the coefficient of thermal expansion and the isothermal compressibility (both with respect to temperature). From these data we estimate that the LLCP of the new water model is at 180 K and 2.1 kbar. The oxygen radial distribution function is calculated along the 2 kbar isobar. It shows a steep change in the height of its second peak between 180 and 185 K, which indicates a transition between the high-density liquid and low-density liquid phases and which is consistent with the ascribed location of the critical point. The good agreement of the height of the second peak of the radial distribution function between simulation and experiment at 1 bar, as a function of temperature, supports the validity of the model. The location of the LLCP within the model is close to the kink in the experimental homogeneous nucleation line. We use existing experimental data to argue that the experimental LLCP is at 168 K and 1.95 kbar and speculate how this LLCP and its Widom line might be responsible for the kink in the homogeneous nucleation line.

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

Kinetic interpretation of resonance phenomena in low pressure capacitively coupled radio frequency plasmas

Low pressure capacitive radio frequency (RF) plasmas are often described by equivalent circuit models based on fluid approaches that predict the self-excitation of resonances, e.g., high frequency oscillations of the total current in asymmetric discharges, but do not provide a kinetic interpretation of these effects. In fact, they leave important questions open: How is current continuity ensured in the presence of energetic electron beams generated by the expanding sheaths that lead to a local enhancement of the conduction current propagating through the bulk? How do the beam electrons interact with cold bulk electrons? What is the kinetic origin of resonance phenomena? Based on kinetic simulations, we find that the energetic beam electrons interact with cold bulk electrons (modulated on a timescale of the inverse local electron plasma frequency) via a time dependent electric field outside the sheaths. This electric field is caused by the electron beam itself, which leaves behind a positive space charge, that attracts cold bulk electrons towards the expanding sheath. The resulting displacement current ensures current continuity by locally compensating the enhancement of the conduction current. The backflow of cold electrons and their interaction with the nonlinear plasma sheath cause the generation of multiple electron beams during one phase of sheath expansion and contribute to a strongly non-sinusoidal RF current. These kinetic mechanisms are the basis for a fundamental understanding of the electron power absorption dynamics and resonance phenomena in such plasmas, which are found to occur in discharges of different symmetries including perfectly symmetric plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Structural and vibrational study of 2-MethoxyEthylAmmonium Nitrate (2-OMeEAN): Interpretation of experimental results with ab initio molecular dynamics

In this work we report an analysis of the bulk phase of 2-methoxyethylammonium nitrate based on ab initio molecular dynamics. The structural and dynamical features of the ionic liquid have been characterized and the computational findings have been compared with the experimental X-ray diffraction patterns, with infrared spectroscopy data, and with the results obtained from molecular dynamics simulations. The experimental infrared spectrum was interpreted with the support of calculated vibrational density of states as well as harmonic frequency calculations of selected gas phase clusters. Particular attention was addressed to the high frequency region of the cation (ω > 2000 cm{sup −1}), where the vibrational motions involve the NH{sub 3}+ group responsible for hydrogen bond formation, and to the frequency range 1200-1400 cm{sup −1} where the antisymmetric stretching mode (ν{sub 3}) of nitrate is found. Its multiple absorption lines in the liquid arise from the removal of the degeneracy present in the D{sub 3h} symmetry of the isolated ion. Our ab initio molecular dynamics leads to a rationalization of the frequency shifts and splittings, which are inextricably related to the structural modifications induced by a hydrogen bonding environment. The DFT calculations lead to an inhomogeneous environment.

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

Number series of atoms, interatomic bonds and interface bonds defining zinc-blende nanocrystals as function of size, shape and surface orientation: Analytic tools to interpret solid state spectroscopy data

Semiconductor nanocrystals (NCs) experience stress and charge transfer by embedding materials or ligands and impurity atoms. In return, the environment of NCs experiences a NC stress response which may lead to matrix deformation and propagated strain. Up to now, there is no universal gauge to evaluate the stress impact on NCs and their response as a function of NC size d{sub NC}. I deduce geometrical number series as analytical tools to obtain the number of NC atoms N{sub NC}(d{sub NC}[i]), bonds between NC atoms N{sub bnd}(d{sub NC}[i]) and interface bonds N{sub IF}(d{sub NC}[i]) for seven high symmetry zinc-blende (zb) NCs with low-index faceting: {001} cubes, {111} octahedra, {110} dodecahedra, {001}-{111} pyramids, {111} tetrahedra, {111}-{001} quatrodecahedra and {001}-{111} quadrodecahedra. The fundamental insights into NC structures revealed here allow for major advancements in data interpretation and understanding of zb- and diamond-lattice based nanomaterials. The analytical number series can serve as a standard procedure for stress evaluation in solid state spectroscopy due to their deterministic nature, easy use and general applicability over a wide range of spectroscopy methods as well as NC sizes, forms and materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Coupled-cluster interpretation of the photoelectron spectrum of Ag$-\atop{3}$

Here, we use the scalar relativistic ionized equation-of-motion coupled-cluster (IP-EOMCC) approaches to investigate the photoelectron spectrum of Ag$-\atop{3}$, examining the effects of basis set, number of correlated electrons, level of applied theory including up to 3-hole-2-particle terms, and geometry relaxation. By employing an IP-EOMCC-based extrapolation scheme, we are able to provide an accurate interpretation and complete assignment of peaks and other key features in the experimentally observed spectra, including electron binding energies as high as about 6.5 eV.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Cluster decomposition of full configuration interaction wave functions: A tool for chemical interpretation of systems with strong correlation

Approximate full configuration interaction (FCI) calculations have recently become tractable for systems of unforeseen size, thanks to stochastic and adaptive approximations to the exponentially scaling FCI problem. The result of an FCI calculation is a weighted set of electronic configurations, which can also be expressed in terms of excitations from a reference configuration. The excitation amplitudes contain information on the complexity of the electronic wave function, but this information is contaminated by contributions from disconnected excitations, i.e., those excitations that are just products of independent lower-level excitations. The unwanted contributions can be removed via a cluster decomposition procedure, making it possible to examine the importance of connected excitations in complicated multireference molecules which are outside the reach of conventional algorithms. We present an implementation of the cluster decomposition analysis and apply it to both true FCI wave functions, as well as wave functions generated from the adaptive sampling CI algorithm. Furthermore, the cluster decomposition is useful for interpreting calculations in chemical studies, as a diagnostic for the convergence of various excitation manifolds, as well as as a guidepost for polynomially scaling electronic structure models. Applications are presented for (i) the double dissociation of water, (ii) the carbon dimer, (iii) the π space of polyacenes, and (iv) the chromium dimer. While the cluster amplitudes exhibit rapid decay with an increasing rank for the first three systems, even connected octuple excitations still appear important in Cr 2 , suggesting that spin-restricted single-reference coupled-cluster approaches may not be tractable for some problems in transition metal chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Interpreting the electron temperature inferred from x-ray continuum emission for direct-drive inertial confinement fusion implosions on OMEGA

In this paper, we present a theoretical framework for interpreting the hot-spot electron temperature ($T_e$) inferred from hard (10- to 20-keV) x-ray continuum emission for inertial confinement fusion implosions on OMEGA. We first show that the inferred $T_e$ represents the emission-weighted, harmonic mean of the hot-spot $T_e$ distribution, both spatially and temporally. A scheme is then provided for selecting a photon energy of which the emission weighting approximates neutron weighting. Simulations are then used to quantify the predicted relationship between the inferred T e , neutron-weighted T i , and implosion performance on OMEGA. In an ensemble of 1-D simulations, it was observed that hot-spot thermal nonequilibrium precluded a sufficiently unique mapping between the inferred $T_e$ and neutron-weighted T i . In 3-D simulations with low-harmonic-mode perturbations (i.e., laser beam power imbalance, target offset, and beam port geometry departures from spherical symmetry) and laser imprint (1 max = 200).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Universal and interpretable classification of atomistic structural transitions via unsupervised graph learning

Materials processing often occurs under extreme dynamic conditions leading to a multitude of unique structural environments. These structural environments generally occur at high temperatures and/or high pressures, often under non-equilibrium conditions, which results in drastic changes in the material's structure over time. Computational techniques, such as molecular dynamics simulations, can probe the atomic regime under these extreme conditions. However, characterizing the resulting diverse atomistic structures as a material undergoes extreme changes in its structure has proved challenging due to the inherently non-linear relationship between structures as large-scale changes occur. Here, we introduce SODAS++, a universal graph neural network framework, that can accurately and intuitively quantify the atomistic structural evolution corresponding to the transition between any two arbitrary phases. We showcase SODAS++ for both solid–solid and solid–liquid transitions for systems of increasing geometric and chemical complexity, such as colloidal systems, elemental Al, rutile and amorphous TiO 2 , and the non-stoichiometric ternary alloy Ag 26 Au 5 Cu 19 . Finally, we show that SODAS++ can accurately quantify all transitions in a physically interpretable manner, showcasing the power of unsupervised graph neural network encodings for capturing the complex and non-linear pathway, a material's structure takes as it evolves.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Interpretation of autoencoder-learned collective variables using Morse–Smale complex and sublevelset persistent homology: An application on molecular trajectories

Dimensionality reduction often serves as the first step toward a minimalist understanding of physical systems as well as the accelerated simulations of them. In particular, neural network-based nonlinear dimensionality reduction methods, such as autoencoders, have shown promising outcomes in uncovering collective variables (CVs). However, the physical meaning of these CVs remains largely elusive. In this work, we constructed a framework that (1) determines the optimal number of CVs needed to capture the essential molecular motions using an ensemble of hierarchical autoencoders and (2) provides topology-based interpretations to the autoencoder-learned CVs with Morse–Smale complex and sublevelset persistent homology. Furthermore, this approach was exemplified using a series of n-alkanes and can be regarded as a general, explainable nonlinear dimensionality reduction method.

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

Novel data interpretation method for DIII-D divertor retarding field energy analyzer with 3-D particle-in-cell simulations

A novel data interpretation process that utilizes comprehensive particle-in-cell (PIC) simulations is developed for the new retarding field energy analyzer (RFEA) currently being constructed at DIII-D for the lower divertor using the Divertor Material Evaluation System. Furthermore, this probe is expected to survive a heat load of up to 100 MW/m 2 for up to 5 s and reliably measure the main ion temperature (T i ) on the divertor target ranging from 10 to 200 eV. These extreme conditions posed significant engineering limitations on the probe geometry, thus extensive validation work has been performed. The conventional fitting method for the RFEA I–V characteristics is based on a simplified 1-D model without considering the ion space charge inside the probe cavity and may not be sufficient for probes designed for the DIII-D divertor environment. In this article, a more realistic description of the particle propagation process within the RFEA cavity is achieved by including both 3-D geometric effects and ion space charge in the PIC simulations, and the capability to reconstruct the ion energy distribution functions is demonstrated with reasonable consistency.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

New interpretation of ion cyclotron emission from a tokamak

The tempting interpretation of ion cyclotron emission in terms of compressional Alfvén eigenmodes involving energetic ions is inconsistent with recent TCV experimental observations in some important aspects, such as (i) the perturbed poloidal field is exceeding the parallel perturbed magnetic field significantly, and (ii) the modes are near cyclotron harmonic and exhibit Alfvèn scaling of their frequency. We show that these characteristics can be explained by considering finite Larmor radius effects of thermal ions in shear Alfvén waves that allow such waves to exist well above the ion cyclotron frequency in the form of wave-packets bouncing within the plasma volume.

Plasma waves↗
DOE OSTI2016

Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks

Spatial gene expression patterns enable the detection of local covariability and are extremely useful for identifying local gene interactions during normal development. The abundance of spatial expression data in recent years has led to the modeling and analysis of regulatory networks. The inherent complexity of such data makes it a challenge to extract biological information. We developed staNMF, a method that combines a scalable implementation of nonnegative matrix factorization (NMF) with a new stability-driven model selection criterion. When applied to a set of Drosophila early embryonic spatial gene expression images, one of the largest datasets of its kind, staNMF identified 21 principal patterns (PP). Providing a compact yet biologically interpretable representation of Drosophila expression patterns, PP are comparable to a fate map generated experimentally by laser ablation and show exceptional promise as a data-driven alternative to manual annotations. Our analysis mapped genes to cell-fate programs and assigned putative biological roles to uncharacterized genes. Finally, we used the PP to generate local transcription factor regulatory networks. Spatially local correlation networks were constructed for six PP that span along the embryonic anterior-posterior axis. Using a two-tail 5% cutoff on correlation, we reproduced 10 of the 11 links in the well-studied gap gene network. In conclusion, the performance of PP with the Drosophila data suggests that staNMF provides informative decompositions and constitutes a useful computational lens through which to extract biological insight from complex and often noisy gene expression data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

An interpretable machine learning framework to understand bikeshare demand before and during the COVID-19 pandemic in New York City

In recent years, bikesharing systems have become increasingly popular as affordable and sustainable micromobility solutions. Advanced mathematical models such as machine learning are required to generate good forecasts for bikeshare demand. Here, this study proposes a machine learning modeling framework to estimate hourly demand in a large-scale bikesharing system. Two Extreme Gradient Boosting models were developed: one using data from before the COVID-19 pandemic (March 2019 to February 2020) and the other using data from during the pandemic (March 2020 to February 2021). Furthermore, a model interpretation framework based on SHapley Additive exPlanations was implemented. Based on the relative importance of the explanatory variables considered in this study, share of female users and hour of day were the two most important explanatory variables in both models. However, the month variable had higher importance in the pandemic model than in the pre-pandemic model.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Progress Toward Interpretable Machine Learning–Based Disruption Predictors Across Tokamaks

Here in this paper we lay the groundwork for a robust cross-device comparison of data-driven disruption prediction algorithms on DIII-D and JET tokamaks. In order to consistently carry on a comparative analysis, we define physics-based indicators of disruption precursors based on temperature, density, and radiation profiles that are currently not used in many other machine learning predictors for DIII-D data. These profile-based indicators are shown to well-describe impurity accumulation events in both DIII-D and JET discharges that eventually disrupt. The univariate analysis of the features used as input signals in the data-driven algorithms applied on the data of both tokamaks statistically highlights the differences in the dominant disruption precursors. JET with its ITER-like wall is more prone to impurity accumulation events, while DIII-D is more subject to edge-cooling mechanisms that destabilize dangerous magnetohydrodynamic modes. Even though the analyzed data sets are characterized by such intrinsic differences, we show through a few examples that the inclusion of physics-based disruption markers in data-driven algorithms is a promising path toward the realization of a uniform framework to predict and interpret disruptive scenarios across different tokamaks. As long as the destabilizing precursors are diagnosed in a device-independent way, the knowledge that data-driven algorithms learn on one device can be re-used to explain a disruptive behavior on another device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

INTERPRETATION OF INFRARED VIBRATION-ROTATION SPECTRA OF INTERSTELLAR AND CIRCUMSTELLAR MOLECULES

Infrared vibration-rotation lines can be valuable probes of interstellar and circumstellar molecules, especially symmetric molecules, which have no pure rotational transitions. But most such observations have been interpreted with an isothermal absorbing slab model, which leaves out important radiative transfer and molecular excitation effects. A more realistic non-LTE and non-isothermal radiative transfer model has been constructed. The results of this model are in much better agreement with the observations, including cases where lines in one branch of a vibration-rotation band are in absorption and another in emission. In general, conclusions based on the isothermal absorbing slab model can be very misleading, but the assumption of LTE may not lead to such large errors, particularly if the radiation field temperature is close to the gas temperature.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2013

INTERPRETING ERUPTIVE BEHAVIOR IN NOAA AR 11158 VIA THE REGION'S MAGNETIC ENERGY AND RELATIVE-HELICITY BUDGETS

In previous works, we introduced a nonlinear force-free method that self-consistently calculates the instantaneous budgets of free magnetic energy and relative magnetic helicity in solar active regions (ARs). Calculation is expedient and practical, using only a single vector magnetogram per computation. We apply this method to a time series of 600 high-cadence vector magnetograms of the eruptive NOAA AR 11158 acquired by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory over a five-day observing interval. Besides testing our method extensively, we use it to interpret the dynamical evolution in the AR, including eruptions. We find that the AR builds large budgets of both free magnetic energy and relative magnetic helicity, sufficient to power many more eruptions than the ones it gave within the interval of interest. For each of these major eruptions, we find eruption-related decreases and subsequent free-energy and helicity budgets that are consistent with the observed eruption (flare and coronal mass ejection (CME)) sizes. In addition, we find that (1) evolution in the AR is consistent with the recently proposed (free) energy-(relative) helicity diagram of solar ARs, (2) eruption-related decreases occur before the flare and the projected CME-launch times, suggesting that CME progenitors precede flares, and (3) self terms of free energy and relative helicity most likely originate from respective mutual terms, following a progressive mutual-to-self conversion pattern that most likely stems from magnetic reconnection. This results in the non-ideal formation of increasingly helical pre-eruption structures and instigates further research on the triggering of solar eruptions with magnetic helicity firmly placed in the eruption cadre.

79 ASTRONOMY AND ASTROPHYSICS↗