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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Optimal decision-making in high-throughput virtual screening pipelines

Screening large pools of molecular candidates to identify those with specific design criteria or targeted properties is demanding in various science and engineering domains. While a high-throughput virtual screening (HTVS) pipeline can provide efficient means to achieving this goal, its design and operation often rely on experts' intuition, potentially resulting in suboptimal performance. In this paper, we fill this critical gap by presenting a systematic framework that can maximize the return on computational investment (ROCI) of such HTVS campaigns. Based on various scenarios, we empirically validate the proposed framework and demonstrate its potential to accelerate scientific discoveries through optimal computational campaigns, especially in the context of virtual screening.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Wide Band Gap Chalcogenide Semiconductors

Wide band gap semiconductors are essential for today’s electronic devices and energy applications because of their high optical transparency, controllable carrier concentration, and tunable electrical conductivity. The most intensively investigated wide band gap semiconductors are transparent conductive oxides (TCOs), such as tin-doped indium oxide (ITO) and amorphous In–Ga–Zn–O (IGZO), used in displays and solar cells, carbides (e.g., SiC) and nitrides (e.g., GaN) used in power electronics, and emerging halides (e.g., γ-CuI) and 2D electronic materials (e.g., graphene) used in various optoelectronic devices. Compared to these prominent materials families, chalcogen-based (Ch = S, Se, Te) wide band gap semiconductors are less heavily investigated but stand out because of their propensity for p-type doping, high mobilities, high valence band positions (i.e., low ionization potentials), and broad applications in electronic devices such as CdTe solar cells. This manuscript provides a review of wide band gap chalcogenide semiconductors. First, we outline general materials design parameters of high performing transparent semiconductors, as well as the theoretical and experimental underpinnings of the corresponding research methods. We proceed to summarize progress in wide band gap (EG > 2 eV) chalcogenide materials—namely, II–VI MCh binaries, CuMCh 2 chalcopyrites, Cu 3 MCh 4 sulvanites, mixed-anion layered CuMCh(O,F), and 2D materials—and discuss computational predictions of potential new candidates in this family, highlighting their optical and electrical properties. Then, we finally review applications—for example, photovoltaic and photoelectrochemical solar cells, transistors, and light emitting diodes—that employ wide band gap chalcogenides as either an active or passive layer. Overall, by examining, categorizing, and discussing prospective directions in wide band gap chalcogenides, this Review aims to inspire continued research on this emerging class of transparent semiconductors and thereby enable future innovations for optoelectronic devices.

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

Beyond Idealized Models of Nanoscale Metal Hydrides for Hydrogen Storage

Metal hydrides are attractive for compact, low-pressure hydrogen storage, yet a foundational understanding of factors governing their thermodynamics and kinetics is still lacking. Predictive modeling from the atomic to the microstructural scale plays a critical role in addressing these gaps, particularly for nanoscale materials, which promise improved performance but are difficult to probe. In this paper, we summarize strategies being developed within the Hydrogen Materials—Advanced Research Consortium (HyMARC) for going beyond conventional models to incorporate more complex physics, more realistic structures, and better approximation of operation conditions in simulations of nanoscale metal hydrides. We highlight four beyond-ideal factors that influence predicted performance: (1) surface anharmonic dynamics, (2) interface and surface energy penalties, (3) mechanical stress under confinement, and (4) the presence of native surface oxide. Approaches for addressing these factors are demonstrated on model materials representative of high-capacity hydrogen storage systems, and implications for understanding performance under operating conditions are discussed.

08 HYDROGEN↗
DOE OSTI2025

Illuminating the Role of Alcohol Substrate in Nickel Photoredox Catalysis via Ground State and Transient Absorption Spectroscopy

Direct excitation of nickel photocatalysts has gained popularity over the last several years following the successful activation of nickel by Ir and Ru photosensitizers. The ability to pair these photosensitizers with Ni catalysts facilitates the formation of crucial chemical bonds under relatively mild conditions. However, there are still gaps in our understanding of what influences the performance of these Ni photocatalysts. Previous work from our group identified a nickel photocatalyst supported by a tridentate pyridinophane ligand that undergoes wavelength-dependent cross-coupling reactivity. Herein, we report detailed studies of the wavelength dependence of the C–O cross-coupling reactivity, illustrating that the catalytic activity has an optimal wavelength, and excitation on both the blue and red sides of this optimal wavelength leads to a reduction in product yield. We also reveal a crucial catalyst-alcohol preirradiation interaction that drastically alters the catalyst’s optical properties. Furthermore, we investigate both wavelength dependency and alcohol dependency via optical transient absorption spectroscopy and reveal a substrate binding process responsible for the observed wavelength-dependent cross-coupling catalysis.

alcohol↗
DOE OSTI2025

Deep Learning for Subsurface Flow: A Comparative Study of U‐Net, Fourier Neural Operators, and Transformers in Underground Hydrogen Storage

Subsurface flow research is essential for the sustainable management of natural resources and the environment. Deep learning (DL) has significantly advanced this field by developing efficient and accurate surrogate models to replace computationally expensive physics‐based simulations. These surrogate models are commonly used to predict the spatiotemporal evolution of state variables, such as gas saturation and reservoir pressure, in heterogeneous geological formations. Despite the various DL models applied to this task, there is a lack of studies systematically comparing their performance. This absence of comparative analysis leads to somewhat arbitrary DL model selection in subsurface flow research, resulting in suboptimal performance and potentially inaccurate predictions. To bridge this gap, we conduct a systematic comparison study of three popular DL architectures—U‐Net, Fourier Neural Operators (FNO), and Segmentation Transformer (SETR)—in surrogate modeling of underground hydrogen storage (UHS). We focus on UHS due to its promise of enhancing clean energy resilience and its cyclic operational conditions that represent common scenarios in various subsurface applications. We evaluate the models based on accuracy, training cost, and inference speed. The comparison shows that U‐Net achieves the highest accuracy, followed by SETR and FNO. Despite its lower accuracy, FNO has the highest inference speed. SETR offers competitive accuracy with the least training memory usage, demonstrating the potential of transformers in learning subsurface flow. Our results provide guidance for selecting DL models for surrogate modeling in a wide range of subsurface flow problems.

42 ENGINEERING↗
DOE OSTI2025

Measuring error rates of mid-circuit measurements

High-fidelity mid-circuit measurements, which read out the state of specific qubits in a multiqubit processor without destroying them or disrupting their neighbors, are a critical component for useful quantum computing. They enable fault-tolerant quantum error correction, dynamic circuits, and other paths to solving classically intractable problems. But there are few methods to assess their performance comprehensively. In this work, we address this gap by introducing the first randomized benchmarking protocol that measures the rate at which mid-circuit measurements induce errors in many-qubit circuits. Using this protocol, we detect and eliminate previously undetected measurement-induced crosstalk in a 20-qubit trapped-ion quantum computer. Then, we use the same protocol to measure the rate of measurement-induced crosstalk error on a 27-qubit IBM Q processor, and quantify how much of that error is eliminated by dynamical decoupling.

Hothem, Daniel [Sandia National Laboratories (SNL-↗
DOE OSTI2020

Metal–organic frameworks: possible new two-dimensional magnetic and topological materials

Finding new two-dimensional (2D) materials with novel quantum properties is highly desirable for technological innovations. In this work, we studied a series of metal–organic frameworks (MOFs) with different metal cores and discovered various attractive properties, such as room-temperature magnetic ordering, strong perpendicular magnetic anisotropy, huge topological band gap (>200 meV), and excellent spin-filtering performance. As many MOFs have been successfully synthesized in experiments, our results suggest realistic new 2D functional materials for the design of spintronic nanodevices.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Communication: Random phase approximation renormalized many-body perturbation theory

We derive a renormalized many-body perturbation theory (MBPT) starting from the random phase approximation (RPA). This RPA-renormalized perturbation theory extends the scope of single-reference MBPT methods to small-gap systems without significantly increasing the computational cost. The leading correction to RPA, termed the approximate exchange kernel (AXK), substantially improves upon RPA atomization energies and ionization potentials without affecting other properties such as barrier heights where RPA is already accurate. Thus, AXK is more balanced than second-order screened exchange [A. Grüneis et al., J. Chem. Phys. 131, 154115 (2009)], which tends to overcorrect RPA for systems with stronger static correlation. Similarly, AXK avoids the divergence of second-order Møller-Plesset (MP2) theory for small gap systems and delivers a much more consistent performance than MP2 across the periodic table at comparable cost. RPA+AXK thus is an accurate, non-empirical, and robust tool to assess and improve semi-local density functional theory for a wide range of systems previously inaccessible to first-principles electronic structure calculations.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2013

Design and validation of a high-voltage levitation circuit for electrostatic accelerometers

A simple high-voltage circuit with a voltage range of 0 to 900 V and an open-loop bandwidth of 11 kHz is realized by using an operational amplifier and a MOSFET combination. The circuit is used for the levitation of a test mass of 71 g, suspended below the top-electrodes with a gap distance of 57 μm, so that the performance of an electrostatic accelerometer can be tested on the ground. The translation noise of the accelerometer, limited by seismic noise, is about 4 × 10{sup −8} m/s{sup 2}/Hz{sup 1/2} at 0.1 Hz, while the high-voltage coupling noise is one-order of magnitude lower.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

Terahertz generation and detection with InGaAs-based large-area photoconductive devices excited at 1.55 μm

We report on scalable large-area terahertz emitters and detectors based on In{sub 0.53}Ga{sub 0.47}As/In{sub 0.52}Al{sub 0.48}As heterostructures for excitation with 1.55 μm radiation. Different geometries involving three different electrode gap sizes are compared with respect to terahertz (THz) emission, bias field distribution, and Joule heating. The field distribution becomes more favorable for THz emission as gap size increases, while Joule heating exhibits the opposite dependence. Devices with three different gap sizes, namely 3 μm, 5 μm, and 7.5 μm, have been investigated experimentally, the emitter with a gap size of 7.5 μm showed the best performance. The scalable devices are furthermore employed as detectors. The scalable electrode geometry enables spatially integrated detection, which is attractive for specific applications, e.g., where an unfocused THz beam has to be used.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Fluid temperature predictions of geothermal borefields using load estimations via state observers

Fluid temperature predictions of geothermal borefields usually involves temporal superposition of its characteristic g-function, using load aggregation schemes to reduce computational times. Assuming that the ground has linear properties, it can be modeled as a linear state space system where the states are the aggregated loads. However, the application and accuracy of these models is compromised when the borefield is already operating and its load history is not registered or there are gaps in the data. This paper assesses the performance of state observers to estimate the borefield load history and obtain accurate fluid predictions. Results show that both Time-Varying Kalman Filter (TVKF) and Moving Horizon Estimator (MHE) provide predictions with average and maximum errors below 0.1 $^\circ C$ and 1 $^\circ C$ respectively. MHE outperforms TVKF in terms of n-step ahead output predictions and load history profile at the expenses of about 5 times more computational time.

Figueroa, Iago C.↗
DOE OSTI2023

High rate studies of the ATLAS sTGC detector and optimization of the filter circuit on the input of the front-end amplifier

Abstract The Large Hadron Collider (LHC) at CERN is expected to be upgraded to the High-Luminosity LHC (HL-LHC) by 2029 and achieve instantaneous luminosity around 5–7.5 × 10 34 cm -2 s -1 . This represents a more than 3–4 fold increase in the instantaneous luminosity compared to what has been achieved in Run 2. The New Small Wheel (NSW) upgrade is designed to be able to operate efficiently in this high background rate environment. In this article, we summarize multiple performance studies of the small-strip Thin Gap Chamber (sTGC) at high rate using nearly final front-end electronics. We demonstrate that the efficiency versus rate distribution can be well described by an exponential decay with electronics dead-time being the primary cause of loss of efficiency at high rate. We then demonstrate several methods that can decrease the electronics dead-time and therefore minimize efficiency loss. One such method is to install either a pi-network input filter or pull-up resistor to minimize the charge input into the amplifier. We optimized the pi-network capacitance and pull-up resistor resistance using the results from our measurements. The results shown here were not only critical to finalizing the components on the front-end board, but also are critical for setting the optimal operating parameters of the sTGC detector and electronics in the ATLAS cavern.

Instruments & Instrumentation↗
DOE OSTI2026

Controlling Deformation in Al/Ti: How Interface Roughness and Orientation Drive Bimetal Mechanics

The microstructural characteristics and morphology of interfaces in metals can be crucial in governing the initiation of plasticity and early deformation mechanisms under extreme conditions. During high-strain-rate deformation, these interfaces significantly affect dislocation nucleation, twinning, and other mechanisms that directly impact material strength and failure. Despite their importance, a substantial knowledge gap remains between the observed macroscopic material behavior and the underlying role of bimetal interfaces in plasticity initiation. Here, to address this gap, large-scale molecular dynamics simulations are performed on Al/Ti bimetal structures to examine the effect of interface characteristics on the onset of plasticity under uniaxial compression. Specifically, this study investigates how interface roughness (flat vs waveform interfaces) modifies the initiation of plastic events in the microstructure. Atomistic simulations indicate that interface roughness (a microscopic behavior) reduces the stress required for dislocation nucleation, thereby reducing the peak stress relative to a flat interface. For the square interface, the step height strongly influences plasticity initiation by setting the separation of locally flat regions. Varying the interface rotation relative to the loading direction (macroscopic behavior) reveals that the peak stress for both flat and waveform interfaces initially decreases and then increases with rotation, accompanied by a shift in dominant mechanisms—from Al twinning (flat) to interface sliding and Ti-dominated phase transformation and twinning at lower angles. The change in the stress-strain slope during initial compression reflects how rotation alters the resolved shear stress and activates different slip systems. Overall, the study provides valuable insights into the role of bimetal interfaces in controlling plasticity initiation and early deformation pathways for Al/Ti under extreme loading conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Spin dynamics in the stripe-ordered buckled honeycomb lattice antiferromagnet Ba 2 NiTeO 6

We carried out inelastic neutron scattering experiments on a buckled honeycomb lattice antiferromagnet Ba 2 NiTeO 6 exhibiting a stripe structure at a low temperature. Magnetic excitations are observed in the energy range of ℏω≲10 meV having an anisotropy gap of 2 meV at 2 K. We perform spin-wave calculations to identify the spin model. The obtained microscopic parameters are consistent with the location of the stripe structure in the classical phase diagram. Furthermore, the Weiss temperature independently estimated from a bulk magnetic susceptibility is consistent with the microscopic parameters. The results reveal that a competition between the nearest-neighbor and next-nearest-neighbor interactions that together with a relatively large single-ion magnetic anisotropy stabilize the stripe magnetic structure.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling

Atomistic materials modeling is a critical task with wide-ranging applications, from drug discovery to materials science, where accurate predictions of the target material property can lead to significant advancements in scientific discovery. Graph Neural Networks (GNNs) represent the state-of-the-art approach for modeling atomistic material data thanks to their capacity to capture complex relational structures. While machine learning performance has historically improved with larger models and datasets, GNNs for atomistic materials modeling remain relatively small compared to large language models (LLMs), which leverage billions of parameters and terabyte-scale datasets to achieve remarkable performance in their respective domains. To address this gap, we explore the scaling limits of GNNs for atomistic materials modeling by developing a foundational model with billions of parameters, trained on extensive datasets in terabytescale. Our approach incorporates techniques from LLM libraries to efficiently manage large-scale data and models, enabling both effective training and deployment of these large-scale GNN models. This work addresses three fundamental questions in scaling GNNs: the potential for scaling GNN model architectures, the effect of dataset size on model accuracy, and the applicability of LLM-inspired techniques to GNN architectures. Specifically, the outcomes of this study include (1) insights into the scaling laws for GNNs, highlighting the relationship between model size, dataset volume, and accuracy, (2) a foundational GNN model optimized for atomistic materials modeling, and (3) a GNN codebase enhanced with advanced LLM-based training techniques. Our findings lay the groundwork for large-scale GNNs with billions of parameters and terabyte-scale datasets, establishing a scalable pathway for future advancements in atomistic materials modeling.

Li, Chaojian [ORNL] (ORCID:0000000340309777)↗
DOE OSTI2016

Temperature- and Intensity-Dependent Photovoltaic Measurements to Identify Dominant Recombination Pathways

In novel photovoltaic absorbers, it is often difficult to assess the root causes of low open-circuit voltages, which may be due to bulk recombination or sub-optimal contacts. In the present work, we discuss the role of temperature- and illumination-dependent device electrical measurements in quantifying and distinguishing these performance losses - in particular, for determining bounds on interface recombination velocities, band alignment, and minority carrier lifetime. We assess the accuracy of this approach by direct comparison to photoelectron spectroscopy. Then, we demonstrate how more computationally intensive model parameter fitting approaches can draw more insights from this broad measurement space. We apply this measurement and modeling approach to high-performance III-V and thin-film chalcogenide devices.

14 SOLAR ENERGY↗
DOE OSTI2018

Impact of Infrared Optical Properties on Crystalline Si and Thin Film CdTe Solar Cells

Photons with energies below the photovoltaic (PV) absorber band gap do not generate current and adversely impact performance when absorbed in other solar cell components to produce heat. Here we incorporate infrared (IR) optical response in simulations for understanding thermal losses. Spectroscopic ellipsometry is used to measure Al-Si interface optical properties in Si PV. Reflectance of an Si module has been analyzed to account for encapsulant and Al-Si interface contributions. IR extended quantum efficiency simulations calculate efficiency gains / losses arising from variations in current generated and total reflectance as functions of transparent front contact material in thin film CdTe PV.

14 SOLAR ENERGY↗
DOE OSTI2023

Climate change alters slug abundance but not herbivory in a temperate grassland

Climate change will significantly impact the world’s ecosystems, in part by altering species interactions and ecological processes, such as herbivory and plant community dynamics, which may impact forage quality and ecosystem production. Yet relatively few field experimental manipulations assessing all of these parameters have been performed to date. To help fill this knowledge gap, we evaluated the effects of increased temperature (+3°C day and night, year-round) and precipitation (+30% of mean annual rainfall) on slug herbivory and abundance and plant community dynamics biweekly in a pasture located in central Kentucky, U.S.A. Warming increased slug abundance once during the winter, likely due to improving conditions for foraging, whereas warming reduced slug abundance at times in late spring, mid-summer, and early fall (from 62–95% reduction depending on month). We found that warming and increased precipitation did not significantly modify slug herbivory at our site, despite altering slug abundance and affecting plant community composition and forage quality. Climate change will alter seasonal patterns of slug abundance through both direct effects on slug biology and indirect effects mediated by changes in the plant community, suggesting that pasture management practices may have to adapt.

59 BASIC BIOLOGICAL SCIENCES↗