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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

An optimally tuned range-separated hybrid starting point for ab initio GW plus Bethe–Salpeter equation calculations of molecules

The ab initio GW plus Bethe–Salpeter equation (GW-BSE, where G is the one particle Green's function and W is the screened Coulomb interaction) approach has emerged as a leading method for predicting excitations in both solids and molecules with a predictive power contingent upon several factors. Among these factors are the (1) generalized Kohn–Sham eigensystem used to construct the GW self-energy and to solve the BSE and (2) the efficacy and suitability of the Tamm–Dancoff approximation. Here, we present a detailed benchmark study of low-lying singlet excitations from a generalized Kohn–Sham (gKS) starting point based on an optimally tuned range-separated hybrid (OTRSH) functional. We show that the use of this gKS starting point with one-shot G0W0 and G0W0-BSE leads to the lowest mean absolute errors (MAEs) and mean signed errors (MSEs), with respect to high-accuracy reference values, demonstrated in the literature thus far for the ionization potentials of the GW100 benchmark set and for low-lying neutral excitations of Thiel’s set molecules in the gas phase, without the need for self-consistency. The MSEs and MAEs of one-shot G0W0-BSE@OTRSH excitation energies are comparable to or lower than those obtained with other functional starting points after self-consistency. Additionally, we compare these results with linear-response time-dependent density functional theory (TDDFT) calculations and find GW-BSE to be superior to TDDFT when calculations are based on the same exchange-correlation functional. This work demonstrates tuned range-separated hybrids used in combination with GW and GW-BSE can greatly suppress starting point dependence for molecules, leading to accuracy similar to that for higher-order wavefunction-based theories for molecules without the need for costlier iterations to self-consistency.

McKeon, Caroline A. (ORCID:0000000217373503)↗
DOE OSTI2021

Multimeric antibodies from antigen-specific human IgM+ memory B cells restrict Plasmodium parasites

Multimeric immunoglobulin-like molecules arose early in vertebrate evolution, yet the unique contributions of multimeric IgM antibodies to infection control are not well understood. This is partially due to the difficulty of distinguishing low-affinity IgM, secreted rapidly by plasmablasts, from high-affinity antibodies derived from later-arising memory cells. We developed a pipeline to express B cell receptors (BCRs) from Plasmodium falciparum–specific IgM+ and IgG+ human memory B cells (MBCs) as both IgM and IgG molecules. BCRs from both subsets were somatically hypermutated and exhibited comparable monomeric affinity. Crystallization of one IgM+ MBC-derived antibody complexed with antigen defined a linear epitope within a conserved Plasmodium protein. In its physiological multimeric state, this antibody displayed exponentially higher antigen binding than a clonally identical IgG monomer, and more effectively inhibited P. falciparum invasion. Forced multimerization of this IgG significantly improved both antigen binding and parasite restriction, underscoring how avidity can alter antibody function. This work demonstrates the potential of high-avidity IgM in both therapeutics and vaccines.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Numerical calculation of the particle–fluid–particle stress in random arrays of fixed particles

Based on the nearest particle statistics [Zhang, J. Fluid Mech. 910, A16 (2021)], the phase interaction force in a multiphase flow is decomposed into a particle–mean-field force and the divergence of the particle–fluid–particle (PFP) stress. The PFP stress is proportional to the correlation product of the distance from a particle to its nearest neighbor and the force on the particle conditionally averaged on the nearest-neighbor location. In this work, a functional form of the stress is obtained corrected to the first order of the ratio between the interparticle distance and the macroscopic length scale of the flow. Particle-resolved numerical simulations are used to calculate the PFP stress in random arrays of fixed particles and to explore the physics represented by the stress. The numerical results show that the PFP stress is attractive along the direction of the flow and is repulsive in the directions perpendicular to the flow. In the flow regime simulated, this PFP stress can be considered as a macroscopic representation of the drafting–kissing–tumbling mechanism. Finally, the Reynolds stress for the fluid phase is also calculated and compared with the PFP stress.

42 ENGINEERING↗
DOE OSTI2021

Long-range hydrogen-binding effects of carbide interfaces in iron

A micromechanics model was developed to evaluate the elastic binding energy between carbide precipitates and hydrogen interstitials using Eshelby's equivalent inclusion method. In this work, density functional theory (DFT) simulations were performed to obtain the material-specific quantities, e.g., lattice constants and the elastic constants, for the continuum model. Using this model, we find that for coherent carbide precipitates, hydrogen atoms are more likely to bind on the broad surfaces of the disk-like precipitates, which is consistent with experimental observations. For semicoherent and incoherent precipitates, our model suggests that it is possible for semicoherent precipitates to have significant hydrogen binding capability while there is no hydrogen-binding capability of incoherent precipitates, which also agrees with experimental findings. In addition, several factors that influence the binding energies between hydrogen atoms and carbide precipitates were quantitatively analyzed, including the precipitate size, morphology, orientation, and interface. These collective results include both the position and the value of the strongest hydrogen-binding interaction for a wide range of carbide stoichiometries, which contributes to our understanding of hydrogen trapping in steel-based materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

The Use of Machine Learning Models for Predicting the Dielectric Strength of Gases

Technological advancements in high voltage systems have pushed sulfur hexafluoride (SF6) to its operational limits. Furthermore, this gas has other drawbacks including a high liquefaction temperature and a high global warming potential. Therefore, there has been an urgent need to find alternative gases with high dielectric strength (DS). In this work, density functional theory (DFT) is used to calculate molecular descriptors that are fed into an artificial neural network (ANN) and a random forest (RF). These machine learning (ML) models are then used to predict the DS for hundreds of molecules. A finite element model (FEM) is also used to calculate the electric field profile of multiple simple electrode geometries as the applied voltage to the system is increased. Results indicate that the random forest model has better generalization to unseen data than the neural network. The highest DS value predicted by the RF was 2.16 relative to the experimental DS of SF6. The results also demonstrate how choosing a gas with a higher DS and a geometry with minimal edges and corners can significantly increase the operating voltage of an electrical system. Due to its superior generalization, the RF represents the most promising path toward an accurate DS predictor once sufficient experimental data are available.

Mileski, Matthew [AFIT]↗
DOE OSTI2024

Coupled Process/Device Modeling and Point Defect Engineering of Cu(In,Ga)Se 2 Solar Cells

Point defects directly impact solar cell device performance by limiting the carrier lifetime. In this work, density functional theory calculations are first used to determine the formation energy and diffusion energy barriers of dominant defects in Cu(In,Ga)Se 2 . Next, continuum reaction–diffusion models are developed to analyze the redistribution of defects during manufacturing processes. We estimate defect capture cross sections using a first-principles-based approach. Here, these cross sections are combined with our calculated defect profiles and trap energy levels to parameterize a Shockley–Read–Hall recombination model, which we implement into a device simulator to predict carrier lifetimes and device performance. In that way, a predictive technology computer aided design model is built to predict and optimize the performance of Cu(In,Ga)Se 2 solar cells.

14 SOLAR ENERGY↗
DOE OSTI2023

A Multi-Objective Bayesian Optimized Human Assessed Multi-Target Generated Spectral Recommender System for Rapid Pareto Discoveries of Material Properties

Optimization for different tasks like material characterization, synthesis, and functional properties for desired applications over multi-dimensional control parameter and function spaces need a rapid strategic search through active learning. However, in all cases prior to optimization, the target material properties are assumed known and fixed, which mostly deviates from real-world scenarios in material synthesis. This can be critical for running expensive experiments on new materials, when the experimental results are fuzzy for any scientific outcomes due to improper target setting, ultimately wasting time and cost. The failure rate and cost are even higher over exploring on multi-target space, where we want to learn the pareto among multiple properties, to jointly optimize during material synthesis for desired applications. To address the challenge, here we introduce the human-operator attempt flexibility in the active learning based automated experiment framework, with generating multiple human assessed targets through a voting-based recommender system during real-time microscope measurements over the large material image space, sequentially learn/update multiple desired targets through a weighting system, and adaptively search in multiple material properties functional space for non-dominated pareto discoveries to maximize the custom structural similarity based acquisition function. We term this a multi-objective Bayesian optimized human assessed multi-target generated spectral recommender systems (MOBO-HAM-SRS). The approach has been demonstrated to peizoresponse force spectroscopy of a ferroelectric thin film, exploring with different kernels and acquisition functions. This work shows an advancement towards human-AI collaborated automated experiments, steering optimization trajectories through human overpowering AI at the early stage when uncertainty is high and AI overpowering human at the later stage with rapid exploration towards optimal goal, following human-assessed multiple targets properties.

Biswas, Arpan↗
DOE OSTI2024

The Near-Gapless Penicillium fuscoglaucum Genome Enables the Discovery of Lifestyle Features as an Emerging Post-Harvest Phytopathogen

Penicillium spp. occupy many diverse biological niches that include plant pathogens, opportunistic human pathogens, saprophytes, indoor air contaminants, and those selected specifically for industrial applications to produce secondary metabolites and lifesaving antibiotics. Recent phylogenetic studies have established Penicillium fuscoglaucum as a synonym for Penicillium commune, which is an indoor air contaminant and toxin producer and can infect apple fruit during storage. During routine culturing on selective media in the lab, we obtained an isolate of P. fuscoglaucum Pf_T2 and sequenced its genome. The Pf_T2 genome is far superior to available genomic resources for the species. Our assembly exhibits a length of 35.1 Mb, a BUSCO score of 97.9% complete, and consists of five scaffolds/contigs representing the four expected chromosomes. It was determined that the Pf_T2 genome was colinear with a type specimen P. fuscoglaucum and contained a lineage-specific, intact cyclopiazonic acid (CPA) gene cluster. For comparison, a highly virulent postharvest apple pathogen, P. expansum strain TDL 12.1, was included and showed a similar growth pattern in culture to our Pf_T2 isolate but was far more aggressive in apple fruit than P. fuscoglaucum. The genome of Pf_T2 serves as a major improvement over existing resources, has superior annotation, and can inform forthcoming omics-based work and functional genetic studies to probe secondary metabolite production and disparities in aggressiveness during apple fruit decay.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

DOE CESER 6 GHz Interference Study

DOE CESER has sponsored Idaho National Laboratory (INL) to conduct an objective and independent study of potential 6 GHz interference from outdoor operation of unlicensed devices in the 6 GHz band on fixed service (FS) microwave communication links operated by electrical sector incumbents in that band. INL is collaborating with University of Notre Dame (UND), Electric Power Research Institute (EPRI), Lockard & White, Southern Company, and AT&T, to gather data with real-world 6 GHz interference experiments and identify (1) the potential for interference from unlicensed devices and (2) the interference necessary to cause harm to the incumbents. In addition to the functional assessment, a security assessment of the FCC mandated Automatic Frequency Coordination (AFC) System to regulate use of unlicensed 6 GHz standard power devices is also being conducted. A major objective is to create a science-based and defensible methodology used to produce the necessary data and to derive objective conclusions. This proven methodology can then be used to produce objective data and conclusions for other spectrum bands with similar incumbent uses including 4.4 – 4.9 GHz and 7.125 – 7.4 GHz, identified in the reconciliation bill that was adopted on July 4, 2025, as well as the National Spectrum Strategy discussions that are ongoing. This report contains 6 GHz field experiments and findings in the following real-world scenarios with commercial unlicensed standard power (SP) 6 GHz devices regulated by Automated Frequency Coordination (AFC): • University of Notre Dame (UND) Stadium with a capacity of 80,000 spectators, where Wi-Fi operating in 6 GHz has been deployed recently • Southern Company 6 GHz FS microwave link between Columbus and Fortson, Georgia Following are the following key findings from this study. 1. The AFC is under-protective of FS when line-of-sight exists along the path centerline. Data collected at Southern’s 6 GHz fixed link site shows significant erosion of as much as 21.4-24.4 dB of under-protection that can lead to potential service degradation under typical operating conditions. This first key finding is most likely the result of erroneous use of the RF propagation model. INL will collaborate with EPRI and the AFC Functional Requirements Working Group to submit a change request to the WinnForum TS-1014 standard towards correct use of the propagation model by the AFC. 2. There is additive interference effect of about 3 dB from nearly equal power interferers measured from simultaneous operation of two SP AP's operating co-channel with the FS receive from different locations along the path. This second key finding should be used to add the impact of additive interference of operation of multiple APs in the same geographical area, to the next generation of AFCs. INL will collaborate with FCC on the need for the AFC to consider additive interference. We also recommend that additional experiments are conducted on 6 GHz spectrum interference to further improve the AFC operation as the number of outdoor Wi-Fi devices continues to increase. These proposed steps and recommendations will make the co-existence of the incumbents and the 6 GHz outdoor Wi-Fi providers possible without any impact on the incumbents with a win-win outcome for all.

6 GHz↗
DOE OSTI2020

Characterization of quantum chaos by two-point correlation functions

In this work, we propose a characterization of quantum many-body chaos: given a collection of simple operators, the set of all possible pair correlations between these operators can be organized into a matrix with a random-matrix-like spectrum. This approach is particularly useful for locally interacting systems, which do not generically show exponential Lyapunov growth of out-of-time-ordered correlators. We demonstrate the validity of this characterization by numerically studying the Sachdev-Ye-Kitaev model and a one-dimensional spin chain with random magnetic field (XXZ model).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Deriving Transmissibility Functions from Finite Elements for Specifications

This work explores deriving transmissibility functions for a missile from a measured location at the base of the fairing to a desired location within the payload. A pressure on the outside of the fairing and the rocket motor’s excitation creates an acceleration at a measured location and a desired location. Typically, the desired location is not measured. In fact, it is typical that the payload may change, but measured acceleration at the base of the fairing is generally similar to previous test flights. Given this knowledge, it is desired to use a finite-element model to create a transmissibility function which relates acceleration from the previous test flight’s measured location at the base of the fairing to acceleration at a location in the new payload. Four methods are explored for deriving this transmissibility, with the goal of finding an appropriate transmissibility when both the pressure and rocket motor excitation are equally present. In conclusion, these methods are assessed using transient results from a simple example problem, and it is found that one of the methods gives good agreement with the transient results for the full range of loads considered.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

Guided search for desired functional responses via Bayesian optimization of generative model: Hysteresis loop shape engineering in ferroelectrics

Advances in theoretical modeling across multiple disciplines have yielded generative models capable of high veracity in predicting macroscopic functional responses of materials emerging as a result of complex non-local interactions. Correspondingly, of interest is the inverse problem of finding the model parameter that will yield desired macroscopic responses, such as stress–strain curves, ferroelectric hysteresis loops, etc. Here, we suggest and implement Gaussian process based methods that allow to effectively sample the degenerate parameter space of a complex non-local model to output regions of parameter space which yield desired functionalities. In this work, we discuss the specific adaptation of the acquisition function and sampling function to make the process efficient and balance the efficient exploration of parameter space for multiple possible minima and exploitation to densely sample the regions of interest where target behaviors are optimized. Additionally, this approach is illustrated via the hysteresis loop engineering in ferroelectric materials but can be adapted to other functionalities and generative models.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Weak Form Scientific Machine Learning: Test Function Construction for System Identification

Weak form Scientific Machine Learning (WSciML) is a recently developed framework for data-driven modeling and scientific discovery. It leverages the weak form of equation error residuals to provide enhanced noise robustness in system identification via convolving model equations with test functions, reformulating the problem to avoid direct differentiation of data. The performance, however, relies on wisely choosing a set of compactly supported test functions. In this work, we mathematically motivate a novel data-driven method for constructing Single-scale-Local reference functions for creating the set of test functions. Our approach numerically approximates the integration error introduced by the quadrature and identifies the support size for which the error is minimal, without requiring access to the model parameter values. Through numerical experiments across various models, noise levels, and temporal resolutions, we demonstrate that the selected supports consistently align with regions of minimal parameter estimation error. We also compare the proposed method against the strategy for constructing Multi-scale-Global (and orthogonal) test functions introduced in our prior work, demonstrating the improved computational efficiency.

FOS: Computer and information sciences↗
DOE OSTI2022

Collaborative VR/AR Data Visualization Tool

This is the final report on Phase II SBIR for the development of a collaborative VR data visualization tool. In the 15 months of the Phase II, we have met and exceeded most objectives that we outlined for ourselves in the proposal. COVID19 has caused us to do some thing differently, but in fact the outcome is better for the development - we were meant to have traveled to institutions in the UK and EU to show the capabilities in April, but instead we have got into an iterative cycle in-house of weekly testing of the new features and functions. This is working well, so we will soon have the ability to share the application with collaborators in remote locations, without the need to travel. We are also making short videos of the features to share with those afar, again mitigating the need to travel and demonstrate in-person our new capabilities. That said, we are behind on getting input from the Beta testers, but when we do get their input it will be to test features and functions that are working, and not failing due to some glitch, which in turn will more likely lead to the acceptance of the product. One of our goals is to have a near-term commercial product, and we are tantalizingly close to it with commercial interest from LANL, Survice and other entities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

X-ray Spectroscopy Fingerprints of Pristine and Functionalized Graphene

In this work, we demonstrate how to identify and characterize the atomic structure of pristine and functionalized graphene materials from a combination of computational simulation of X-ray spectra, on the one hand, and computer-aided interpretation of experimental spectra, on the other. Despite the enormous scientific and industrial interest, the precise structure of these 2D materials remains under debate. As we show in this study, a wide range of model structures from pristine to heavily oxidized graphene can be studied and understood with the same approach. We move systematically from pristine to highly oxidized and defective computational models, and we compare the simulation results with experimental data. Comparison with experiments is valuable also the other way around; this method allows us to verify that the simulated models are close to the real samples, which in turn makes simulated structures amenable to several computational experiments. Our results provide ab initio semiquantitative information and a new platform for extended insight into the structure and chemical composition of graphene-based materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Dopant levels in large nanocrystals using stochastic optimally tuned range-separated hybrid density functional theory

In this work, we apply a stochastic version of an optimally tuned range-separated hybrid functional to provide insight on the electronic properties of P- and B- doped Si nanocrystals of experimentally relevant sizes. We show that we can use the range-separation parameter for undoped systems to calculate accurate results for dopant activation energies. We apply this strategy for tuning functionals to study doped nanocrystals up to 2.5 nm in diameter at the hybrid functional level. In this confinement regime, the P- and B- dopants have large activation energies and have strongly localized states that lie deep within the energy gaps. Structural relaxation plays a greater role for B-substituted dopants and contributes to the increase in activation energy when the B dopant is near the nanocrystal surface.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Role of Transition Metals in Metal–Organic Frameworks as Nanoporous Ion Emitters for Thermal Ionization Mass Spectrometry

Thermal ionization mass spectroscopy (TIMS) is a powerful analytical technique that allows for precise determination of isotopic ratios. Analysis on low abundance samples, however, can be limited by the ionization efficiency. Following an investigation into a new type of metal-organic hybrid material devised to promote the emission of analyte ions (nano-PIES) and reduce traditional sample loading problems, this work probes the impact that changing the metal in the material has on the ionization of Uranium-238. Being derived from metal-organic frameworks (MOFs), nano-PIEs inherit the tunability of the parent MOFs; the MOF-74 series has been well studied for probing the impact various framework metals (i.e., Mg, Mn, Co, Ni, Cu, Zn, and Cd) have on material properties, and thus, a series of nano-PIEs with different metals were derived from an isoreticular MOF-74 series. In conclusion, trends in ionization efficiency were studied as a function of ionization potential, volatility, and work function of the framework metals as well as the mechanism of ionization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Improved band gaps and structural properties from Wannier–Fermi–Löwdin self-interaction corrections for periodic systems

The accurate prediction of band gaps and structural properties in periodic systems continues to be one of the central goals of electronic structure theory. However, band gaps obtained from popular exchange–correlation (XC) functionals (such as LDA and PBE) are severely underestimated partly due to the spurious self-interaction error (SIE) inherent to these functionals. In this work, we present a new formulation and implementation of Wannier function-derived Fermi–Löwdin (WFL) orbitals for correcting the SIE in periodic systems. Since our approach utilizes a variational minimization of the self-interaction energy with respect to the Wannier charge centers (WCC), it is computationally more efficient than the HSE hybrid functional and other self-interaction corrections that require a large number of transformation matrix elements. Here, calculations on several (17 in total) prototypical molecular solids, semiconductors, and wide-bandgap materials show that our WFL self-interaction correction approach gives better band gaps and bulk moduli compared to semilocal functionals, largely due to the partial removal of self-interaction errors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗