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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

A LIKELY CLOSE-IN LOW-MASS STELLAR COMPANION TO THE TRANSITIONAL DISK STAR HD 142527

With the uniquely high contrast within 0.''1 ({Delta}mag(L') = 5-6.5 mag) available using Sparse Aperture Masking with NACO at Very Large Telescope, we detected asymmetry in the flux from the Herbig Fe star HD 142527 with a barycenter emission situated at a projected separation of 88 {+-} 5 mas (12.8 {+-} 1.5 AU at 145 pc) and flux ratios in H, K, and L' of 0.016 {+-} 0.007, 0.012 {+-} 0.008, and 0.0086 {+-} 0.0011, respectively (3{sigma} errors), relative to the primary star and disk. After extensive closure-phase modeling, we interpret this detection as a close-in, low-mass stellar companion with an estimated mass of {approx}0.1-0.4 M{sub Sun }. HD 142527 has a complex disk structure, with an inner gap imaged in both the near and mid-IR as well as a spiral feature in the outer disk in the near-IR. This newly detected low-mass stellar companion may provide a critical explanation of the observed disk structure.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Filtered Backprojection in Compton Imaging using a Spherical Harmonic Wiener Filter with Pixelated CdZnTe

Filtered backprojection is an image reconstruction technique for Compton imaging that provides reasonably high resolution at much lower computational costs when compared to iterative methods. Here, this work applies a Wiener filter that has been derived for spherical harmonics on Compton imaging using the OrionUM pixelated CdZnTe imaging-spectrometer. To regularize the filter, an investigation is made into the power spectral density of the signal and noise to develop an appropriate spectral signal-to-noise ratio model for the restoration process. Experimental measurements were conducted with two 228Th sources placed 30° apart. The resulting filtered image of the two sources have an average full-width-at-half-maximum (FWHM) of 9.8° or 7.5° when using a mean squared error and structural similarity optimization approach respectively; an improvement from the 29.0° FWHM image when using simple backprojection.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Monte Carlo Control Loops for Cosmic Shear Cosmology with DES Year 1

Weak lensing by large-scale structure is a powerful probe of cosmology and of the dark universe. This cosmic shear technique relies on the accurate measurement of the shapes and redshifts of background galaxies and requires precise control of systematic errors. The Monte Carlo Control Loops (MCCL) is a forward modelling method designed to tackle this problem. It relies on the Ultra Fast Image Generator (UFig) to produce simulated images tuned to match the target data statistically, followed by calibrations and tolerance loops. We present the first end-to-end application of this method, on the Dark Energy Survey (DES) Year 1 wide field imaging data. We simultaneously measure the shear power spectrum $C_{\ell}$ and the redshift distribution $n(z)$ of the background galaxy sample. The method includes maps of the systematic sources, Point Spread Function (PSF), an Approximate Bayesian Computation (ABC) inference of the simulation model parameters, a shear calibration scheme, and the fast estimation of the covariance matrix. We find a close statistical agreement between the simulations and the DES Y1 data using an array of diagnostics. In a non-tomographic setting, we derive a set of $C_\ell$ and $n(z)$ curves that encode the cosmic shear measurement, as well as the systematic uncertainty. Following a blinding scheme, we measure the combination of $\Omega_m$, $\sigma_8$, and intrinsic alignment amplitude $A_{\rm{IA}}$, defined as $S_8D_{\rm{IA}} = \sigma_8(\Omega_m/0.3)^{0.5}D_{\rm{IA}}$, where $D_{\rm{IA}}=1-0.11(A_{\rm{IA}}-1)$. We find $S_8D_{\rm{IA}}=0.895^{+0.054}_{-0.039}$, where systematics are at the level of roughly 60\% of the statistical errors. We discuss these results in the context of earlier cosmic shear analyses of the DES Y1 data. Our findings indicate that this method and its fast runtime offer good prospects for cosmic shear measurements with future wide-field surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

CLASH: PRECISE NEW CONSTRAINTS ON THE MASS PROFILE OF THE GALAXY CLUSTER A2261

We precisely constrain the inner mass profile of A2261 (z = 0.225) for the first time and determine that this cluster is not 'overconcentrated' as found previously, implying a formation time in agreement with {Lambda}CDM expectations. These results are based on multiple strong-lensing analyses of new 16-band Hubble Space Telescope imaging obtained as part of the Cluster Lensing and Supernova survey with Hubble. Combining this with revised weak-lensing analyses of Subaru wide-field imaging with five-band Subaru + KPNO photometry, we place tight new constraints on the halo virial mass M{sub vir} = (2.2 {+-} 0.2) Multiplication-Sign 10{sup 15} M{sub Sun} h {sup -1}{sub 70} (within r{sub vir} Almost-Equal-To 3 Mpc h {sup -1}{sub 70}) and concentration c{sub vir} = 6.2 {+-} 0.3 when assuming a spherical halo. This agrees broadly with average c(M, z) predictions from recent {Lambda}CDM simulations, which span 5 {approx}< (c) {approx}< 8. Our most significant systematic uncertainty is halo elongation along the line of sight (LOS). To estimate this, we also derive a mass profile based on archival Chandra X-ray observations and find it to be {approx}35% lower than our lensing-derived profile at r{sub 2500} {approx} 600 kpc. Agreement can be achieved by a halo elongated with a {approx}2:1 axis ratio along our LOS. For this elongated halo model, we find M{sub vir} = (1.7 {+-} 0.2) Multiplication-Sign 10{sup 15} M{sub Sun} h {sup -1}{sub 70} and c{sub vir} = 4.6 {+-} 0.2, placing rough lower limits on these values. The need for halo elongation can be partially obviated by non-thermal pressure support and, perhaps entirely, by systematic errors in the X-ray mass measurements. We estimate the effect of background structures based on MMT/Hectospec spectroscopic redshifts and find that these tend to lower M{sub vir} further by {approx}7% and increase c{sub vir} by {approx}5%.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Persistence of systematic errors in the Asian-Australian monsoon precipitation basic states in climate models: a way forward

The annual cycle of the Asian-Australian monsoon (AAM) system can be regarded as the seasonal displacement of the large-scale Intertropical Convergence Zone (ITCZ), which is anchored by the north-south migration of the Indo-Pacific warm pool. In the respective hemispheres, intense solar heating over land during spring and early summer provide the necessary thermodynamic conditions for the occurrence of deep convection off the equator. The rainfall and diabatic heating associated with the AAM is perhaps the most vigorous of all the regional monsoon components in the globe. Yet, skill in monsoon prediction (days to seasons) by dynamical models remains low, partly due to our lack of understanding of the entirety of the monsoon system and our inability to model the interactive processes that govern it. Simulating the monsoon precipitation climatology remains a grand challenge. The multi-model mean (MMM) errors for summertime precipitation relative to GPCP observations have shown little improvement in CMIP5 as compared to CMIP3. For the Asian Summer Monsoon, the MMM monsoon rainfall is weaker over South Asia, the western equatorial Indian Ocean and tropical West Pacific. For the Australian summer monsoon, it is excessive over most of the Maritime Continent, and deficient over northern Australia. Throughout the year, excessive rainfall is simulated over South China Sea – Maritime Continent regions, and CMIP5 models do not capture the annual cycle of the AAM. One implication is that uncertainties in future projections (e.g., IPCC 2013) of AAM mean rainfall may not have reduced from CMIP3 to CMIP5. Solutions from an intermediate model show that diabatic heating (Q) associated with the AAM influences the global circulation. Compared to the reanalysis, many models tend to have maxima at the mid-troposphere but their simulated amplitude is overestimated in the lower troposphere (900-700 hPa) and underestimated from 600-300 hPa, a feature readily apparent in the MMM composite (dashed black line). The lower troposphere peak may be attributed to misrepresentations in shallow convection. Some outliers, such as CSIROMk3- 6-0 and ACCESS1-3, do not show any appreciable vertical structure, and the simulated monsoon over South Asia is virtually absent in these models. TRMM observations indicate that over the monsoon region the contribution from stratiform rainfall is about 40% to the Q intensity. In contrast, most of the CMIP3 models produce too much convective rainfall (95% of the total) and too little stratiform precipitation. Given the persistence of errors, we speculate that errors in the partitioning of total rainfall into convective-stratiform still persist in CMIP5, and may be one of the reasons for underestimation (overestimation) of Q in the layer 600-300 (900-700) hPa. In summary, the rather slow progress in modeling in the last decade or so led us to wonder: Has the scientific community reached a “plateau” in modeling mean monsoon precipitation?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Resilience Design Patterns - A Structured Approach to Resilience at Extreme Scale (version 1.1)

Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. Projections based on the current generation of HPC systems and technology roadmaps suggest the prevalence of very high fault rates in future systems. The errors resulting from these faults will propagate and generate various kinds of failures, which may result in outcomes ranging from result corruptions to catastrophic application crashes. Therefore the resilience challenge for extreme-scale HPC systems requires management of various hardware and software technologies that are capable of handling a broad set of fault models at accelerated fault rates. Also, due to practical limits on power consumption in HPC systems future systems are likely to embrace innovative architectures, increasing the levels of hardware and software complexities. As a result the techniques that seek to improve resilience must navigate the complex trade-off space between resilience and the overheads to power consumption and performance. While the HPC community has developed various resilience solutions, application-level techniques as well as system-based solutions, the solution space of HPC resilience techniques remains fragmented. There are no formal methods and metrics to investigate and evaluate resilience holistically in HPC systems that consider impact scope, handling coverage, and performance & power efficiency across the system stack. Additionally, few of the current approaches are portable to newer architectures and software environments that will be deployed on future systems. In this document, we develop a structured approach to the management of HPC resilience using the concept of resilience-based design patterns. A design pattern is a general repeatable solution to a commonly occurring problem. We identify the commonly occurring problems and solutions used to deal with faults, errors and failures in HPC systems. Each established solution is described in the form of a pattern that addresses concrete problems in the design of resilient systems. The complete catalog of resilience design patterns provides designers with reusable design elements. We also define a framework that enhances a designer's understanding of the important constraints and opportunities for the design patterns to be implemented and deployed at various layers of the system stack. This design framework may be used to establish mechanisms and interfaces to coordinate flexible fault management across hardware and software components. The framework also supports optimization of the cost-benefit trade-offs among performance, resilience, and power consumption. The overall goal of this work is to enable a systematic methodology for the design and evaluation of resilience technologies in extreme-scale HPC systems that keep scientific applications running to a correct solution in a timely and cost-efficient manner in spite of frequent faults, errors, and failures of various types.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Comparative Study on the Machine Learning-Based Prediction of Adsorption Energies for Ring and Chain Species on Metal Catalyst Surfaces

Computation of adsorption and transition state energies for a large number of surface intermediates for numerous active site models pose significant computational overhead in computational screening of catalysts. Machine learning (ML) techniques can be used to predict part of these energies. To predict the energies, ML models need to be fed appropriate metal and species descriptors. For complex surface chemistries, the structures of the intermediate species can vary greatly. In this paper, working with the hydrodeoxygenation of succinic acid on six different metal surfaces, we have studied the effect of linear and non-linear ML models used along with pen-and-paper based species descriptors and two categories of metal descriptors on two different categories of intermediate species: chain and ring. More specifically, our computations include the prediction of chain species when trained on only chain species and also when trained on both chain and ring species. Similar computations were performed for predictions of ring species. In each case, results of linear ML models were compared with kernel based non-linear models. Our results indicate that ring species data does not improve the prediction of chain species. Similarly, chain species data does not improve the prediction of ring species. The use of non-linear ML models, however, did help to minimize the prediction errors compared to the linear models. Furthermore, the study also shows that electronic or adsorption energy based metal descriptors along with bond count based species fingerprints can achieve a mean absolute error (MAE) of less than 0.2 eV for complex chain molecules when used with an appropriate machine learning model.

Adsorption↗
DOE OSTI2024

Geophysical and Environmental Monitoring Data, and Subsurface Flow Modelling Results for Chicken Bone Meadow, Mt. Snodgrass, Crested Butte, CO

This dataset includes geoelectrical monitoring data acquired between October 2021 and November 2022, soil moisture and temperature data, groundwater data obtained from borehole SNIB covering the period from June 2021 to September 2022, and hydrological modelling results. The data were acquired to investigate how variations in bedrock type and topography, and vegetation cover control subsurface flow dynamics. To provide insights into the subsurface flow dynamics and their controls, a monitoring transect was installed at the Chicken Bone Meadow, Mt. Snodgrass, Crested Butte, CO, measuring the spatio-temporal variations of soil moisture, soil and snow temperature, subsurface electrical resistivity variations, and groundwater dynamics. Field data are organized in a folder structure, with Electrical Resistivity Tomography (ERT) data being provided as one file per measurement, and data of the soil moisture and temperature sensors being provided as text files covering the entire monitoring period. The ‘Locations.csv’ file contains the location of all sensors, given in NAD83 – UTM Zone 13N. ERT monitoring data has been processed to filter data based on reciprocal errors (data with errors > 30% were removed), a linear error model was fitted to each survey, and to ensure a constant set of measurements for time-lapse inversion, filtered data were interpolated and assigned a 100% measurement error. Soil moisture and temperature data were acquired at 15 min intervals, and averaged to provide 1h data. Weather data and borehole data (groundwater depth, conductivity and temperature) were acquired at 30 min intervals, and are provided as daily measurements; all measurements are averaged, except of precipitation values, which are given as daily accumulation. The hydrological model was set up along the ERT monitoring transect, and net infiltration was used as surface boundary condition and derived from the weather data. Four different results are provided, (1) results for a parameterization using hydraulic permeability and porosity as derived from the ERT data through petrophysical relationships, and (2) three simplified model results, using 1 to 3 geological layers above the bedrock. Modelling was performed using PFLOTRAN, and for each model the PFLOTRAN input files are provided. The result files include weekly hydrological modelling results (e.g., saturation, velocities, pressures), as well as the model parameterization. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Resilience Design Patterns - A Structured Approach to Resilience at Extreme Scale (version 1.0)

Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. Projections based on the current generation of HPC systems and technology roadmaps suggest that very high fault rates in future systems. The errors resulting from these faults will propagate and generate various kinds of failures, which may result in outcomes ranging from result corruptions to catastrophic application crashes. Practical limits on power consumption in HPC systems will require future systems to embrace innovative architectures, increasing the levels of hardware and software complexities. The resilience challenge for extreme-scale HPC systems requires management of various hardware and software technologies that are capable of handling a broad set of fault models at accelerated fault rates. These techniques must seek to improve resilience at reasonable overheads to power consumption and performance. While the HPC community has developed various solutions, application-level as well as system-based solutions, the solution space of HPC resilience techniques remains fragmented. There are no formal methods and metrics to investigate and evaluate resilience holistically in HPC systems that consider impact scope, handling coverage, and performance & power eciency across the system stack. Additionally, few of the current approaches are portable to newer architectures and software ecosystems, which are expected to be deployed on future systems. In this document, we develop a structured approach to the management of HPC resilience based on the concept of resilience-based design patterns. A design pattern is a general repeatable solution to a commonly occurring problem. We identify the commonly occurring problems and solutions used to deal with faults, errors and failures in HPC systems. The catalog of resilience design patterns provides designers with reusable design elements. We define a design framework that enhances our understanding of the important constraints and opportunities for solutions deployed at various layers of the system stack. The framework may be used to establish mechanisms and interfaces to coordinate flexible fault management across hardware and software components. The framework also enables optimization of the cost-benefit trade-os among performance, resilience, and power consumption. The overall goal of this work is to enable a systematic methodology for the design and evaluation of resilience technologies in extreme-scale HPC systems that keep scientific applications running to a correct solution in a timely and cost-ecient manner in spite of frequent faults, errors, and failures of various types.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Sub-additivity in combining infiltration with mechanical ventilation for single zone buildings

We report that in determining ventilation rates, it is often necessary to combine naturally-driven infiltration, with air flows from mechanical systems. When there are balanced mechanical systems, the solution is simple additivity, because a balanced system does not impact the internal pressure of the space or the air flows through the building envelope. Unbalanced systems, however, change internal pressures and therefore can impact natural ventilation non-linearly in such a way as to make it sub-additive. Several sub-additive approaches are found in the literature, but they are not robust across the full spectrum from tight to leaky buildings and ranges of mechanical ventilation air flow rates. There are two approaches for combining natural infiltration with mechanical ventilation that require different solutions. The forward problem is to find the total air flow when adding mechanical ventilation to natural infiltration, and this application has been investigated in previous studies. The inverse problem finds the required mechanical ventilation in order to meet a total ventilation rate given a known amount of natural infiltration. This article presents the results of millions of hours of simulations of the physically correct solution, which span a broad range of climates, air leakage and structural conditions. This large dataset allows for the comparison with three literature models and the development of new robust sub-additivity models. These improved models are for use with unbalanced systems appropriate for consensus standards and guidelines for both the forward and inverse problem. Finally, they reduce errors to 1% or less and work across the air tightness spectrum.

42 ENGINEERING↗
DOE OSTI2013

PROSPECTS FOR MEASURING THE RELATIVE VELOCITIES OF GALAXY CLUSTERS IN PHOTOMETRIC SURVEYS USING THE KINETIC SUNYAEV-ZEL'DOVICH EFFECT

We consider the prospects for measuring the pairwise kinetic Sunyaev-Zel'dovich (kSZ) signal from galaxy clusters discovered in large photometric surveys such as the Dark Energy Survey (DES). We project that the DES cluster sample will, in conjunction with existing mm-wave data from the South Pole Telescope (SPT), yield a detection of the pairwise kSZ signal at the 8{sigma}-13{sigma} level, with sensitivity peaking for clusters separated by {approx}100 Mpc distances. A next-generation version of SPT would allow for a 18{sigma}-30{sigma} detection and would be limited by variance from the kSZ signal itself and the residual thermal Sunyaev-Zel'dovich (tSZ) signal. Throughout our analysis, we assume photometric redshift errors that wash out the signal for clusters separated by {approx}<50 Mpc; a spectroscopic survey of the DES sample would recover this signal and allow for a 26{sigma}-43{sigma} detection, and would again be limited by kSZ/tSZ variance. Assuming a standard model of structure formation, these high-precision measurements of the pairwise kSZ signal will yield detailed information on the gas content of the galaxy clusters. Alternatively, if the gas can be sufficiently characterized by other means (e.g., using tSZ, X-ray, or weak lensing), then the relative velocities of the galaxy clusters can be isolated, thereby providing a precision measurement of gravity on 100 Mpc scales. We briefly consider the utility of these measurements for constraining theories of modified gravity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Atomic protein structure refinement using all-atom graph representations and SE(3)-equivariant graph transformer

Abstract Motivation The state-of-art protein structure prediction methods such as AlphaFold are being widely used to predict structures of uncharacterized proteins in biomedical research. There is a significant need to further improve the quality and nativeness of the predicted structures to enhance their usability. In this work, we develop ATOMRefine, a deep learning-based, end-to-end, all-atom protein structural model refinement method. It uses a SE(3)-equivariant graph transformer network to directly refine protein atomic coordinates in a predicted tertiary structure represented as a molecular graph. Results The method is first trained and tested on the structural models in AlphaFoldDB whose experimental structures are known, and then blindly tested on 69 CASP14 regular targets and 7 CASP14 refinement targets. ATOMRefine improves the quality of both backbone atoms and all-atom conformation of the initial structural models generated by AlphaFold. It also performs better than two state-of-the-art refinement methods in multiple evaluation metrics including an all-atom model quality score—the MolProbity score based on the analysis of all-atom contacts, bond length, atom clashes, torsion angles, and side-chain rotamers. As ATOMRefine can refine a protein structure quickly, it provides a viable, fast solution for improving protein geometry and fixing structural errors of predicted structures through direct coordinate refinement. Availability and implementation The source code of ATOMRefine is available in the GitHub repository (https://github.com/BioinfoMachineLearning/ATOMRefine). All the required data for training and testing are available at https://doi.org/10.5281/zenodo.6944368.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Coefficient-to-Basis Network: a fine-tunable operator learning framework for inverse problems with adaptive discretizations and theoretical guarantees

We propose a Coefficient-to-Basis Network (C2BNet), a novel framework for solving inverse problems within the operator learning paradigm. C2BNet efficiently adapts to different discretizations through fine-tuning, using a pre-trained model to significantly reduce computational cost while maintaining high accuracy. Unlike traditional approaches that require retraining from scratch for new discretizations, our method enables seamless adaptation without sacrificing predictive performance. Furthermore, we establish theoretical approximation and generalization error bounds for C2BNet by exploiting low-dimensional structures in the underlying datasets. Our analysis demonstrates that C2BNet adapts to low-dimensional structures without relying on explicit encoding mechanisms, highlighting its robustness and efficiency. To validate our theoretical findings, we conducted extensive numerical experiments that showcase the superior performance of C2BNet on several inverse problems. The results confirm that C2BNet effectively balances computational efficiency and accuracy, making it a promising tool to solve inverse problems in scientific computing and engineering applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Data Evaluation of Actinide Cross Sections: 240 Am, 241 Am, and 242 Am

Evaluations for neutron-induced reactions on the isotopes 240 Am, 241 Am, and 242 Am were per formed by optimizing reaction-model calculations based on existing experimental data from both direct measurements and surrogate reactions. The evaluated data were based on results obtained from the LLNL reaction codes YAHFC and FRESCO using up-to-date nuclear models; including an improvement upon some previous treatments for inelastic processes. Nuclear reaction codes generated data for all relevant reaction channels, which were then translated into Generalized Nu clear Data Structure and LLNL’s Evaluated Neutron Data Libraries (ENDL). These data libraries were tested for potential errors and applied to verification and validation (V&V) tests suites where applicable for comparisons.

42 ENGINEERING↗
DOE OSTI2022

Chemical Cartography with APOGEE: Mapping Disk Populations with a 2-process Model and Residual Abundances

We apply a novel statistical analysis to measurements of 16 elemental abundances in 34,410 Milky Way disk stars from the final data release (DR17) of APOGEE-2. Building on recent work, we fit median abundance ratio trends [X/Mg] versus [Mg/H] with a 2-process model, which decomposes abundance patterns into a "prompt" component tracing core-collapse supernovae and a "delayed" component tracing Type Ia supernovae. For each sample star, we fit the amplitudes of these two components, then compute the residuals Δ[X/H] from this two-parameter fit. The rms residuals range from ~0.01–0.03 dex for the most precisely measured APOGEE abundances to ~0.1 dex for Na, V, and Ce. The correlations of residuals reveal a complex underlying structure, including a correlated element group comprised of Ca, Na, Al, K, Cr, and Ce and a separate group comprised of Ni, V, Mn, and Co. Selecting stars poorly fit by the 2-process model reveals a rich variety of physical outliers and sometimes subtle measurement errors. Residual abundances allow for the comparison of populations controlled for differences in metallicity and [α/Fe]. Relative to the main disk (R = 3–13 kpc), we find nearly identical abundance patterns in the outer disk (R = 15–17 kpc), 0.05–0.2 dex depressions of multiple elements in LMC and Gaia Sausage/Enceladus stars, and wild deviations (0.4–1 dex) of multiple elements in ω Cen. The residual abundance analysis opens new opportunities for discovering chemically distinctive stars and stellar populations, for empirically constraining nucleosynthetic yields, and for testing chemical evolution models that include stochasticity in the production and redistribution of elements.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Accuracy of relativistic energy-consistent pseudopotentials for superheavy elements 111-118: Molecular calibration calculations

Relativistic energy-consistent pseudopotentials modelling the Dirac-Coulomb-Breit Hamiltonian with a finite nucleus model for the superheavy elements with nuclear charges 111-118 were calibrated in atomic and molecular calculations against fully relativistic all-electron reference data. Various choices for the adjustment of the f-potentials were investigated and an improved parametrization is recommended. Using the resulting pseudopotentials relativistic all-electron reference data can be reproduced at the self-consistent field level with average absolute (relative) errors of 0.0030 A (0.15%) for bond lengths and 2.79 N m{sup -1} (1.26%) for force constants for 24 diatomic test molecules, i.e., neutral or singly charged monohydrides, monofluorides, and monochlorides with closed-shell electronic structure. At the second-order Moller-Plesset perturbation theory level the corresponding average deviations are 0.0033 A (0.15%) for bond lengths and 2.86 N m{sup -1} (1.40%) for force constants. Corresponding improved f-potentials were also derived for the pseudopotentials modelling in addition the leading contributions from quantum electrodynamics.

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

SYSTEMATIC AND STOCHASTIC VARIATIONS IN PULSAR DISPERSION MEASURES

We analyze deterministic and random temporal variations in the dispersion measure (DM) from the full three-dimensional velocities of pulsars with respect to the solar system, combined with electron-density variations over a wide range of length scales. Previous treatments have largely ignored pulsars’ changing distances while favoring interpretations involving changes in sky position from transverse motion. Linear trends in pulsar DMs observed over 5–10 year timescales may signify sizable DM gradients in the interstellar medium (ISM) sampled by the changing direction of the line of sight to the pulsar. We show that motions parallel to the line of sight can also account for linear trends, for the apparent excess of DM variance over that extrapolated from scintillation measurements, and for the apparent non-Kolmogorov scalings of DM structure functions inferred in some cases. Pulsar motions through atomic gas may produce bow-shock ionized gas that also contributes to DM variations. We discuss the possible causes of periodic or quasi-periodic changes in DM, including seasonal changes in the ionosphere, annual variations of the solar elongation angle, structure in the heliosphere and ISM boundary, and substructure in the ISM. We assess the solar cycle’s role on the amplitude of ionospheric and solar wind variations. Interstellar refraction can produce cyclic timing variations from the error in transforming arrival times to the solar system barycenter. We apply our methods to DM time series and DM gradient measurements in the literature and assess their consistency with a Kolmogorov medium. Finally, we discuss the implications of DM modeling in precision pulsar timing experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis

Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.

97 MATHEMATICS AND COMPUTING↗