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DOE OSTI2023

Charged point defect benchmark of Hybrid and GGA-PBE

Data used by the publication "High-throughput calculations of charged point defect properties with semi-local density functional theory - performance benchmarks for materials screening applications." This work presented an in-depth benchmark analysis of automated, semi-local point defect calculations with a-posteriori corrections, compared to 245 “gold standard” hybrid calculations previously published. We considered three different a-posteriori correction sets for semi-local calculations, implemented in a fully automated workflow, and consider the qualitative and quantitative differences for four different categories of defect information: thermodynamic transition levels, formation energies, fermi levels, and dopability limits. We highlighted the type of qualitative information about point defect properties that can be extracted from high-throughput calculations based on semi-local DFT methods, while also demonstrating the limits of quantitative accuracy that can be achieved by these approaches.

Broberg, Danny↗

DOE OSTI2026

Fermilab Booster loss modelling and rebalancing using Bayesian methods

To meet PIP-II upgrade requirements, Fermilab Booster losses need to be reduced by 50% compared to present levels. So far, simulations are not good enough to predict loss patterns. Thus, an extensive Booster tune up will be necessary to achieve required performance. In this paper we present an effort to build a data-driven loss model using Bayesian techniques, and subsequently to rebalance losses for higher trip margins. We first created several sets of spatially and temporally isolated orbit and optics knobs, and trained Gaussian process models for each beam loss monitor as well as beam current. Novel techniques of uncertainty constraints and approximate GP fitting were introduced to handle safety and timing requirements. We then performed single and multi-objective tuning using scalarized objectives comprised of critical beam loss locations. We achieved significant rebalancing of losses, increasing margins by 25%, as well as an overall improvement in transmission efficiency of 0.4%. Automated data collection is being developed so that more accurate surrogate models can be trained over time.

Kuklev, N. [Fermilab]↗

DOE OSTI2021

Dark Matter Constraints from a Unified Analysis of Strong Gravitational Lenses and Milky Way Satellite Galaxies

Joint analyses of small-scale cosmological structure probes have been relatively unexplored yet promise to advance measurements of microphysical dark matter properties using heterogeneous data. Here, we present a multidimensional analysis of dark matter substructure using strong gravitational lenses and the Milky Way (MW) satellite galaxy population, accounting for degeneracies in model predictions and using covariances in the constraining power of these individual probes for the first time. We simultaneously infer the projected subhalo number density and the half-mode mass describing the suppression of the subhalo mass function in thermal relic warm dark matter (WDM), $M_{\mathrm{hm}}$, using the semianalytic model \texttt{Galacticus} to connect the subhalo population inferred from MW satellite observations to the strong lensing host halo mass and redshift regime. Combining MW satellite and strong lensing posteriors in this parameter space yields $M_{\mathrm{hm}}<10^{7.0}\ M_{\mathrm{\odot}}$ (WDM particle mass $m_{\mathrm{WDM}}>9.7\ \mathrm{keV}$) at $95\%$ confidence and disfavors $M_{\mathrm{hm}}=10^{7.4}\ M_{\mathrm{\odot}}$ ($m_{\mathrm{WDM}}=7.4\ \mathrm{keV}$) with a 20:1 marginal likelihood ratio, improving limits on $m_{\mathrm{WDM}}$ set by the two methods independently by $\sim 30\%$. These results are marginalized over the line-of-sight contribution to the strong lensing signal, the mass of the MW host halo, and the efficiency of subhalo disruption due to baryons and are robust to differences in the disruption efficiency between the MW and strong lensing regimes at the $\sim 10\%$ level. This work paves the way for unified analyses of next-generation small-scale structure measurements covering a wide range of scales and redshifts.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1981

The development and utilization of solar photovoltaic cells: An assessment of the potential for a new energy technology

The Government set the goal of accelerating the adaptation of photovoltaics by reducing system costs to a competitive level and overcoming the technical, institutional, legal, environmental, and social barriers impeding the diffusion of photovoltaic technology. The technology of silicon solar arrays was examined and the status of development efforts are reviewed. The political, legal, economic, social, and environmental issues are discussed, and several methods for selecting development projects are described. A number of market forecasting techniques, including time trend, judgemental, and econometric methods, were reviewed, and the results of these models are presented.

Cyr, K. J.↗

NASA NTRS1989

Molecular cloud cores and bimodal star formation

The phenomenon of bimodal star formation is reviewed in the context of supercritical and subcritical states for molecular clouds that are supported against their self-gravitation by magnetic fields. The governing set of equations is derived subject to the quasi-static and axisymmetric approximations. The method of numerical solution and tests of the resultant computer code are outlined. The results of the evolutionary calculations are discussed, emphasizing time scales, masses, and typical sizes of modeled cores that can be compared with observations. For a fixed mass, it is found that the level or turbulent support determines whether a dense core forms or not. This is used to generalize the concept of a critical mass to account for the contributions of turbulence and thermal pressures to the support of a cloud.

Lizano, Susana↗

NASA NTRS1992

Theoretical study of transition-metal ions bound to benzene

Theoretical binding energies are reported for all first-row and selected second-row transition metal ions (M+) bound to benzene. The calculations employ basis sets of at least double-zeta plus polarization quality and account for electron correlation using the modified coupled-pair functional method. While the bending is predominantly electrostatic, the binding energies are significantly increased by electron correlation, because the donation from the metal d orbitals to the benzene pi* orbitals is not well described at the self-consistent-field level. The uncertainties in the computed binding energies are estimated to be about 5 kcal/mol. Although the calculated and experimental binding energies generally agree to within their combined uncertainties, it is likely that the true binding energies lie in the lower portion of the experimental range. This is supported by the very good agreement between the theoretical and recent experimental binding energies for AgC6H6(+).

Bauschlicher, Charles W., Jr.↗

NASA NTRS1993

Applicability of PM3 to transphosphorylation reaction path: Toward designing a minimal ribozyme

A growing body of evidence shows that RNA can catalyze many of the reactions necessary both for replication of genetic material and the possible transition into the modern protein-based world. However, contemporary ribozymes are too large to have self-assembled from a prebiotic oligonucleotide pool. Still, it is likely that the major features of the earliest ribozymes have been preserved as molecular fossils in the catalytic RNA of today. Therefore, the search for a minimal ribozyme has been aimed at finding the necessary structural features of a modern ribozyme (Beaudry and Joyce, 1990). Both a three-dimensional model and quantum chemical calculations are required to quantitatively determine the effects of structural features of the ribozyme on the reaction it catalyzes. Using this model, quantum chemical calculations must be performed to determine quantitatively the effects of structural features on catalysis. Previous studies of the reaction path have been conducted at the ab initio level, but these methods are limited to small models due to enormous computational requirements. Semiempirical methods have been applied to large systems in the past; however, the accuracy of these methods depends largely on a simple model of the ribozyme-catalyzed reaction, or hydrolysis of phosphoric acid. We find that the results are qualitatively similar to ab initio results using large basis sets. Therefore, PM3 is suitable for studying the reaction path of the ribozyme-catalyzed reaction.

Manchester, John I.↗

NASA NTRS2017

S-band Fence Study Phase 1: Estimating the Increased Level of Screening Volume Incursions

Motivation: CARA assesses collision risk for a set of high-value satellites. Deploying the S-band Fence (SBF) radar system will significantly increase the number of conjunctions to process, possibly overloading the current CARA system.Phase 1 study objective: Estimate changes in screening volume incursion rates caused by the SBF deploymentPhase 2 study objective: Estimate associated changes in serious conjunction rates (e.g., Pc > 10-4) Phase 3 study objective: Develop methods for filtering and prioritizing the increased tasking, as requiredAll to be discussed today are Phase I results

conjunction assessment operations↗

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↗

NASA NTRS2009

Probabilistic Models for Solar Particle Events

Probabilistic Models of Solar Particle Events (SPEs) are used in space mission design studies to provide a description of the worst-case radiation environment that the mission must be designed to tolerate.The models determine the worst-case environment using a description of the mission and a user-specified confidence level that the provided environment will not be exceeded. This poster will focus on completing the existing suite of models by developing models for peak flux and event-integrated fluence elemental spectra for the Z>2 elements. It will also discuss methods to take into account uncertainties in the data base and the uncertainties resulting from the limited number of solar particle events in the database. These new probabilistic models are based on an extensive survey of SPE measurements of peak and event-integrated elemental differential energy spectra. Attempts are made to fit the measured spectra with eight different published models. The model giving the best fit to each spectrum is chosen and used to represent that spectrum for any energy in the energy range covered by the measurements. The set of all such spectral representations for each element is then used to determine the worst case spectrum as a function of confidence level. The spectral representation that best fits these worst case spectra is found and its dependence on confidence level is parameterized. This procedure creates probabilistic models for the peak and event-integrated spectra.

Adams, James H., Jr.↗

DOE OSTI2017

Validation of Material Models For Automotive Carbon Fiber Composite Structures Via Physical And Crash Testing (VMM Composites Project)

As automotive fuel economy requirements increase, the push for reducing overall vehicle weight will likely include the consideration of materials that have not previously been part of mainstream vehicle design and manufacturing, including carbon fiber composites. Vehicle manufacturers currently rely on computer-aided engineering (CAE) methods as part of the design and development process, so going forward, the ability to accurately and predictably model carbon fiber composites will be necessary. If composites are to be used for structural components, this need applies to both, crash and quasi-static modeling. This final report covers the results of a five-year, $6.89M, 50% cost-shared research project between Department of Energy (DOE) and the US Advanced Materials Partnership (USAMP) under Cooperative Agreement DE-EE-0005661 known as “Validation of Material Models for Automotive Carbon Fiber Composite Structures Via Physical and Crash Testing (VMM).” The objective of the VMM Composites Project was to validate and assess the ability of physics-based material models to predict crash performance of automotive primary load-carrying carbon fiber composite structures. Simulation material models that were evaluated included micro-mechanics based meso-scale models developed by the University of Michigan (UM) and micro-plane models by Northwestern University (NWU) under previous collaborations with the DOE and Automotive Composites Consortium/USAMP, as well as five commercial crash codes: LS-DYNA, RADIOSS, VPS/PAM-CRASH, Abaqus, and GENOA-MCQ. CAE predictions obtained from seven organizations were compared with experimental results from quasi-static testing and dynamic crash testing of a thermoset carbon fiber composite front-bumper and crush-can (FBCC) system gathered under multiple loading conditions. This FBCC design was developed to demonstrate progressive crush, virtual simulation, tooling, fabrication, assembly, non-destructive evaluation and crash testing advances in order to assess the correlation of the predicted results to the physical tests. The FBCC was developed to meet a goal of 30-35% mass reduction while aiming for equivalent energy absorption as a steel component for which baseline experimental results were obtained from testing in the same crash modes. The project also evaluated crash performance of thermoplastic composite structures fabricated from commercial prepreg materials and low cost carbon fiber sourced from Oak Ridge National Laboratory. The VMM Project determined that no set of predictions from a CAE supplier were found to be universally accurate among all the six crash modes evaluated. In general, crash modes that were most dependent on the properties of the prepreg were more accurate than those that were dependent on the behavior of the joints. The project found that current CAE modeling methods or best practices for carbon fiber composites have not achieved standardization, and accuracy of CAE is highly reliant on the experience of its users. Coupon tests alone are not sufficient to develop an accurate material model, but it is necessary to bridge the gap between the coupon data and performance of the actual structure with a series of subcomponent level tests. Much of the unreliability of the predictions can be attributed to shortcomings in our ability to mathematically link the effects of manufacturing and material variability into the material models. This is a subject of ongoing research in the industry. The final report is organized by key technical tasks to describe how the validation project developed, modeled and compared crash data obtained on the composite FBCC to the multiple sets of CAE predictions. Highlights of the report include a discussion of the quantitative comparison between predictions and experimental data, as well as an in-depth discussion of remaining technological gaps that exist in the industry, which are intended to spur innovations and improvements in CAE technology.

36 MATERIALS SCIENCE↗

NASA NTRS2022

Uncertainty Models for the Hybrid Parametric Variation Method of Uncertainty Quantification; Analysis

There is some level of uncertainty in every finite element model (FEM), which flows to a level of uncertainty in predicted results. The purpose of uncertainty quantification (UQ) is to provide statistical bounds on prediction accuracy based on model uncertainty. This is distinct from model updating, which attempts to modify models to improve their accuracy. UQ does not improve the accuracy of models, but accepts that the models are inherently inaccurate and attempts to quantify the impact of that inaccuracy on predicted results. Previously, an alternate method for UQ, called the Hybrid Parametric Variation (HPV) method, was applied to Space Launch System (SLS) Hurty/Craig-Bampton (HCB) components to predict system-level statistics for launch vehicle attitude control transfer functions and core stage section loads due to buffet. The HPV method combines a parametric variation of the HCB fixed-interface (FI) modal frequencies with a nonparametric variation (NPV) method that randomly varies the HCB mass and stiffness matrices as Wishart random matrix distributions using random matrix theory (RMT). Alternatively, the most common method for modeling uncertainty in the structural dynamics community is a parametric approach, which varies physical parameters in the model. However, there are several disadvantages associated with the parametric method. Determining a reduced set of parameters that have a significant impact on the system response can be time consuming, and the selected parameter probability distributions are rarely reliably known. Therefore, in practice, the parameters are surrogates for the actual errors, and the link to parameter uncertainty is unknown. Another major drawback is that the uncertainty that can be represented is limited to the form of the nominal FEM. It is the experience of the authors that based on numerous aerospace programs, almost all FEM errors are in form rather than parameter values. This hypothesis is supported by the observation of the authors that it is almost never possible to ‘tune’ a FEM to match modal test results by only modifying model parameters. Model-form uncertainty cannot be directly represented by FEM input parameters nor included in a parametric approach. However, model-form uncertainty can be modeled using RMT, where a probability distribution is developed for the matrix ensemble of interest. The major advantage of the NPV method is that it covers errors in model form. The HPV method anchors uncertainty at the HCB component level to component modal test results by matching the HCB and test modes based on mode descriptions or other methods, and then applying differing levels of frequency variation. The specific variations depend on the confidence to which a component FEM has been validated through modal testing. The NPV method is layered on the frequency variation to match modal test self-orthogonality and cross-orthogonality (XO) results. Once the component uncertainty models are identified, they are assembled, and the uncertainty is propagated to the system level using a Monte Carlo (MC) analysis approach that generates statistics for system-level predictions This provides a UQ method that can be traced to test data, which can be updated as additional data and improved correlated models become available. The purpose of this paper is to collect and present all of the theory for HPV that has been previously published in reports and papers and to present examples of its application. Specifically, component uncertainty models based on the dispersion of corresponding mass and stiffness matrices using proposed test/analysis correlation metrics are investigated. The first example is purely academic so that the true answers are known, and the validity of the HPV method and the corresponding uncertainty models can be determined. The purpose of this paper is to collect and present all of the theory for HPV that has been previously published in reports and papers and to present examples of its application. Specifically, component uncertainty models based on the dispersion of corresponding mass and stiffness matrices using proposed test/analysis correlation metrics are investigated. The first example is purely academic so that the true answers are known, and the validity of the HPV method and the corresponding uncertainty models can be determined. The second example is an application to a component that is design specific to the SLS. Based on this work and other assessments, the HPV method provides another tool to the toolset used for complex system UQ analysis. From experience gathered to date using the HPV method, additional design specific applications must be investigated to provide further confidence in the validity of the HPV method of UQ analysis.

Uncertainty quantification↗

DOE OSTI2021

A solution framework for linear PDE-constrained mixed-integer problems

Abstract We present a general numerical solution method for control problems with state variables defined by a linear PDE over a finite set of binary or continuous control variables. We show empirically that a naive approach that applies a numerical discretization scheme to the PDEs to derive constraints for a mixed-integer linear program (MILP) leads to systems that are too large to be solved with state-of-the-art solvers for MILPs, especially if we desire an accurate approximation of the state variables. Our framework comprises two techniques to mitigate the rise of computation times with increasing discretization level: First, the linear system is solved for a basis of the control space in a preprocessing step. Second, certain constraints are just imposed on demand via the IBM ILOG CPLEX feature of a lazy constraint callback. These techniques are compared with an approach where the relations obtained by the discretization of the continuous constraints are directly included in the MILP. We demonstrate our approach on two examples: modeling of the spread of wildfire and the mitigation of water contamination. In both examples the computational results demonstrate that the solution time is significantly reduced by our methods. In particular, the dependence of the computation time on the size of the spatial discretization of the PDE is significantly reduced.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Methods for evaluation and treatment of data shift

This is a code repository for a set of tools for detecting and mitigating data shifts in machine learning. The goal of the tools is to provide capabilities for determining when new data sets differ from training data sets and for adapting existing models to new data or correcting data shifts (via domain adaptation). The components will be written in Python, a high-level programming language that takes advantage of the Python ecosystem of high-quality open-source packages for machine learning and signal processing.

Parikh, Nidhi↗

DOE OSTI2022

Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification

Electronic health records (EHR) are not designed for population‐based research, but they provide easy and quick access to longitudinal health information for a large number of individuals. Many statistical methods have been proposed to account for selection bias, missing data, phenotyping errors, or other problems that arise in EHR data analysis. However, addressing multiple sources of bias simultaneously is challenging. We developed a methodological framework (R package, SAMBA ) for jointly handling both selection bias and phenotype misclassification in the EHR setting that leverages external data sources. These methods assume factors related to selection and misclassification are fully observed, but these factors may be poorly understood and partially observed in practice. As a follow‐up to the methodological work, we demonstrate how to apply these methods for two real‐world case studies, and we evaluate their performance. In both examples, we use individual patient‐level data collected through the University of Michigan Health System and various external population‐based data sources. In case study (a), we explore the impact of these methods on estimated associations between gender and cancer diagnosis. In case study (b), we compare corrected associations between previously identified genetic loci and age‐related macular degeneration with gold standard external summary estimates. These case studies illustrate how to utilize diverse auxiliary information to achieve less biased inference in EHR‐based research.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Examination of How Well Long-Range-Corrected Density Functionals Satisfy the Ionization Energy Theorem

For this work, we calculated the vertical ionization energies (VIE) of 99 species in two ways to examine the accuracy of several long-range-corrected (LC) hybrid meta functionals in comparison with a gradient approximation (GA), global hybrids, and doubly hybrids. In the category of LC functionals, we examined both those with meta ingredients (i.e., that depend on the kinetic energy density) and those without them. The LC-hybrid meta functionals examined are M11, revM11, M11plus, and ωB97M-V. The reference data used to assess accuracy consist of 95 molecules and 4 atoms in the GW100 set. The two methods studied are the ΔSCF method (involving the difference of neutral and cation self-consistent field (SCF) energies) and the ionization energy theorem (involving the orbital energy of the highest occupied molecular orbital, HOMO). We calculated linear correlation coefficients (r 2 ) and mean absolute deviations (MADs) between each approach and the reference VIE value from the CCSD(T)/def2-TZVPP level of theory. We compared the new LC-hybrid meta calculations to calculations with the 10 functionals in a previous VIE study by Brémond et al. and to the calculations with LC-BLYP (LC-Becke, Lee–Yang–Parr), CAM-B3LYP (Coulomb-attenuating-method Becke-3-parameter Lee–Yang–Parr), LC-ωHPBE, and ωB97X-D. The results show that Minnesota LC-hybrid meta functionals have the smallest mean absolute deviation of ionization energy theorem VIEs with the reference data; the LC-ωHPBE functional also does quite well in this test. This is very encouraging and indicates that LC-hybrid meta functionals would be the best starting points for the tuning strategy that has been shown to be a very good procedure for improving time-dependent density functional calculations, and it also helps explain the good success of LC-hybrid meta functionals for molecular excitation energies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

An assessment of low-lying excitation energies and triplet instabilities of organic molecules with an ab initio Bethe-Salpeter equation approach and the Tamm-Dancoff approximation

The accurate prediction of singlet and triplet excitation energies is an area of intense research of significant fundamental interest and critical for many applications. Most calculations of singlet and triplet energies use time-dependent density functional theory (TDDFT) in conjunction with an approximate exchange-correlation functional. In this work, we examine and critically assess an alternative method for predicting low-lying neutral excitations with similar computational cost, the ab initio Bethe-Salpeter equation (BSE) approach, and compare results against high-accuracy wavefunction-based methods. We consider singlet and triplet excitations of 27 prototypical organic molecules, including members of Thiel’s set, the acene series, and several aromatic hydrocarbons exhibiting charge-transfer-like excitations. Analogous to its impact in TDDFT, we find that the Tamm-Dancoff approximation (TDA) overcomes triplet instabilities in the BSE approach, improving both triplet and singlet energetics relative to higher level theories. Lastly, we find that BSE-TDA calculations built on effective DFT starting points, such as those utilizing optimally tuned range-separated hybrid functionals, can yield accurate singlet and triplet excitation energies for gas-phase organic molecules.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2025

Neighborhood sociome factors and pediatric asthma exacerbations: Protective role of tree crown density and importance of pharmacy access in Chicago's south side

Abstract Background Pediatric asthma exacerbations remain a critical public health concern, particularly in historically underserved urban settings. Objective This study investigates sociome factors—the social context of disease—associated with asthma exacerbations among children living in Chicago's South Side, leveraging clinical and publicly available generalizable census tract‐level datasets from agencies including ChiVes, the City of Chicago Data Portal, EPA, Census Bureau, HUD, NOAA, and more. The aim is to uncover novel hypotheses for potential new interventions. Methods A generalized linear model assessed associations with the outcome of asthma exacerbations while accounting for clustering at the patient level. Predictors included all variables from the Sociome Data Commons, including social, environmental, behavioral, economic, housing, and school variables. Results Predictors of decreased risk included patient age (+4.8 years, −22%), tree crown density (+6% coverage, −17%), parks per acre (+0.41, −8%), and labor market engagement (+0.8 points, −9%). Conversely, predictors of increased risk included increased distance to the nearest pharmacy (+0.28 miles, +12%), limited English skills (+2.3%, +10%), higher inequality (+0.08 points, +8%), and visits in the Spring (+11%) and Fall (+20%). Conclusion The results suggest that tree crown density, a novel finding in the context of asthma exacerbations, may play a protective role. Limited access to health care facilities such as pharmacies continues to complicate care. Clinical Implications These findings provide hypotheses for future interventions for long‐standing asthma disparities.

Allergy↗