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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Benchmarks and Reliable DFT Results for Spin Gaps of Small Ligand Fe(II) Complexes

Here, all-electron fixed-node diffusion Monte Carlo provides benchmark spin gaps for four Fe(II) octahedral complexes. Standard quantum chemical methods (semilocal DFT and CCSD(T)) fail badly for the energy difference between their high- and low-spin states. Density-corrected DFT is both significantly more accurate and reliable and yields a consistent prediction for the Fe-Porphyrin complex

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

Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices

Recent developments in machine learning interatomic potentials (MLIPs) have empowered even nonexperts in machine learning to train MLIPs for accelerating materials simulations. However, reproducibility and independent evaluation of presented MLIP results is hindered by a lack of clear standards in current literature. In this Perspective, we aim to provide guidance on best practices for documenting MLIP use while walking the reader through the development and deployment of MLIPs including hardware and software requirements, generating training data, training models, validating predictions, and MLIP inference. We also suggest useful plotting practices and analyses to validate and boost confidence in the deployed models. Finally, we provide a step-by-step checklist for practitioners to use directly before publication to standardize the information to be reported. Altogether, we hope that our work will encourage the reliable and reproducible use of these MLIPs, which will accelerate their ability to make a positive impact in various disciplines including materials science, chemistry, and biology, among others.

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

The Underlying Chemical Mechanism of Selective Chemical Etching in CsPbBr 3 Nanocrystals for Reliably Accessing Near-Unity Emitters

Reliably accessing nanocrystal luminophores with near-unity efficiencies aids in the ability to understand the upper performance limits in optoelectronic applications that require minimal nonradiative losses. Constructing structure-function relationships at the atomic level, while accounting for inevitable defects, allows for the development of robust strategies to achieve near-unity quantum yield luminophores. For CsPbBr 3 perovskite nanocrystals, bromine vacancies leave behind undercoordinated lead atoms that act as traps, limiting the achievable optical performance of the material. Here, we show that selective etching represents a promising path for mitigating the consequences of optical defects in CsPbBr 3 nanocrystals. A mechanistic understanding of the etching reaction is essential for developing strategies to finely control the reaction. We report a study of the selective etching mechanism of CsPbBr3 nanocrystal cubes by controlling the etchant chemical potential. We observe optical absorption and luminescence trajectories while varying the extent and rate of lead removal, removing in some cases up to 75% of the lead from the original nanocrystal ensemble. At modest etchant chemical potentials, the size and shape uniformity of the nanocrystal ensemble improves in addition to the quantum yield, proceeding through a layer-by-layer etching mechanism. Operating with excessively high etchant chemical potentials is detrimental to the overall optical performance as the etching transitions to nonselective, while too low of a chemical potential results in incomplete etching. Through this general approach, we show how to finely control selective etching to consistently access a steady state or chemical stability zone of near-unity quantum yield CsPbBr 3 nanocrystals postsynthetically, suggesting a practical framework to extend this treatment to other perovskite compositions and sizes.

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

Declining Reservoir Reliability and Increasing Reservoir Vulnerability: Long-Term Observations Reveal Longer and More Severe Periods of Low Reservoir Storage for Major United States Reservoirs

Hydrological drought is a pervasive and reoccurring challenge in managing water resources. Reservoirs are critical for lessening the impacts of drought on water available for many uses. We use a novel and generalized approach to identify periods of unusually low reservoir storage—via comparisons to operational rule curves and historical patterns—to investigate how droughts affect storage in 250 reservoirs across the conterminous U.S. (CONUS). We find that the maximum amount of water stored in reservoirs is decreasing, and that periods of unusually low storage are becoming longer, more severe, and more variable in (a) western and central CONUS reservoirs, and (b) reservoirs with primarily over-year storage. Results suggest that reservoir storage has become less reliable and more vulnerable to larger deviations from desired storage patterns. These changes have coincided with ongoing shifts to the hydroclimate of CONUS, and with sedimentation further reducing available reservoir storage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

AI Improves the Accuracy, Reliability, and Economic Value of Continental‐Scale Flood Predictions

Accurate flood early warnings are critical to minimize damage and loss of life. Current large‐scale operational forecasting systems, however, have limited accuracy, description of uncertainty, and computational efficiency. While Artificial intelligence (AI) can address these limitations in principle, the accuracy and reliability of AI forecasts have thus far proven insufficient. Here we present a novel hybrid framework that integrates AI‐based machinery termed Errorcastnet (ECN) with the National Water Model (NWM) to showcase the potential of ensemble AI flood forecasts over the contiguous U.S. ECN boosts prediction accuracy four‐ to six‐fold across lead times of 1–10 days, while providing uncertainty quantification. It also outperforms Google's state‐of‐the‐art global AI model. ECN‐based forecasts offer superior economic value (up to four‐fold) for decision‐making as compared to those from NWM alone. ECN performs well in varied ecoregions, physiography, and land management conditions. The framework is computationally efficient, enabling national‐scale ensemble forecasts in minutes.

artificial intelligence↗
DOE OSTI2017

Potential for natural evaporation as a reliable renewable energy resource

About 50% of the solar energy absorbed at the Earth’s surface drives evaporation, fueling the water cycle that affects various renewable energy resources, such as wind and hydropower. Recent advances demonstrate our nascent ability to convert evaporation energy into work, yet there is little understanding about the potential of this resource. Here in this paper we study the energy available from natural evaporation to predict the potential of this ubiquitous resource. We find that natural evaporation from open water surfaces could provide power densities comparable to current wind and solar technologies while cutting evaporative water losses by nearly half. We estimate up to 325 GW of power is potentially available in the United States. Strikingly, water’s large heat capacity is sufficient to control power output by storing excess energy when demand is low, thus reducing intermittency and improving reliability. Our findings motivate the improvement of materials and devices that convert energy from evaporation.

13 HYDRO ENERGY↗
DOE OSTI2019

Reliable electrochemical phase diagrams of magnetic transition metals and related compounds from high-throughput ab initio calculations

Abstract Magnetic transition metals (mTM = Cr, Mn, Fe, Co, and Ni) and their complex compounds (oxides, hydroxides, and oxyhydroxides) are highly important material platforms for diverse technologies, where electrochemical phase diagrams with respect to electrode potential and solution pH can be used to effectively understand their corrosion and oxidation behaviors in relevant aqueous environments. Many previous decades-old mTM–Pourbaix diagrams are inconsistent with various direct electrochemical observations, because experimental complexities associated with extracting reliable free energies of formation (Δ f G ) lead to inaccuracies in the data used for modeling. Here, we develop a high-throughput simulation approach based on density-functional theory (DFT), which quickly screens structures and compounds using efficient DFT methods and calculates accurate Δ f G values, using high-level exchange-correlation functions to obtain ab initio Pourbaix diagrams in comprehensive and close agreement with various important electrochemical, geological, and biomagnetic observations reported over the last few decades. We also analyze the microscopic mechanisms governing the chemical trends among the Δ f G values and Pourbaix diagrams to further understand the electrochemical behaviors of mTM-based materials. Last, we provide probability profiles at variable electrode potential and solution pH to show quantitatively the likely coexistence of multiple-phase areas and diffuse phase boundaries.

Huang, Liang-Feng (ORCID:0000000329375183)↗
DOE OSTI2017

Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression

Currently, Constraint-Based Reconstruction and Analysis (COBRA) is the only methodology that permits integrated modeling of Metabolism and macromolecular Expression (ME) at genome-scale. Linear optimization computes steady-state flux solutions to ME models, but flux values are spread over many orders of magnitude. Data values also have greatly varying magnitudes. Furthermore, standard double-precision solvers may return inaccurate solutions or report that no solution exists. Exact simplex solvers based on rational arithmetic require a near-optimal warm start to be practical on large problems (current ME models have 70,000 constraints and variables and will grow larger). We also developed a quadrupleprecision version of our linear and nonlinear optimizer MINOS, and a solution procedure (DQQ) involving Double and Quad MINOS that achieves reliability and efficiency for ME models and other challenging problems tested here. DQQ will enable extensive use of large linear and nonlinear models in systems biology and other applications involving multiscale data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Reliably obtaining white light from layered halide perovskites at room temperature

The recent observation of broadband white-light emission from the inorganic sheets of certain layered lead-bromide perovskites has instigated a multitude of studies on this unusual phenomenon. However, the vast majority of layered bromide perovskites have flat (001) inorganic sheets and display a narrow photoluminescence at room temperature. A handful of heavily distorted (001) perovskites display broad emission, but to date, there is no method of predicting which perovskites will produce white light at room temperature prior to screening different organic molecules that can template 2D perovskites and crystallizing and analyzing the material. By studying ten Pb–Cl perovskites, we find that they all exhibit a broad yellow emission, which is strikingly invariant despite different distortions in the inorganic framework seen across the series. We postulate that this broad emission is intrinsic to all layered Pb–Cl perovskites. Although broad, the emission is not white. By adding Br to the Pb–Cl perovskites we obtain both the narrow emission and the broad emission such that the combined emission color smoothly varies from yellow to warm white to cold white as a function of the halide ratio. Thus, alloying Br to Pb–Cl perovskites appears to be a simple and general strategy for reliably obtaining white light at room temperature from (001) perovskites, regardless of the templating effects of the organic molecules, which should greatly expand the number of white-light-emitting layered perovskites.

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

CIRCLEZ : Reliable photometric redshifts for active galactic nuclei computed solely using photometry from Legacy Survey Imaging for DESI

Photometric redshifts for galaxies hosting an accreting supermassive black hole in their center, known as active galactic nuclei (AGNs), are notoriously challenging. At present, they are most optimally computed via spectral energy distribution (SED) fittings, assuming that deep photometry for many wavelengths is available. However, for AGNs detected from all-sky surveys, the photometry is limited and provided by a range of instruments and studies. This makes the task of homogenizing the data challenging, presenting a dramatic drawback for the millions of AGNs that wide surveys such as SRG/eROSITA are poised to detect. This work aims to compute reliable photometric redshifts for X-ray-detected AGNs using only one dataset that covers a large area: the tenth data release of the Imaging Legacy Survey (LS10) for DESI. LS10 provides deep grizW1-W4 forced photometry within various apertures over the footprint of the eROSITA-DE survey, which avoids issues related to the cross-calibration of surveys. We present the results from CIRCLEZ, a machine-learning algorithm based on a fully connected neural network. CIRCLEZ is built on a training sample of 14 000 X-ray-detected AGNs and utilizes multi-aperture photometry, mapping the light distribution of the sources. The accuracy (σNMAD) and the fraction of outliers (η) reached in a test sample of 2913 AGNs are equal to 0.067 and 11.6%, respectively. The results are comparable to (or even better than) what was previously obtained for the same field, but with much less effort in this instance. We further tested the stability of the results by computing the photometric redshifts for the sources detected in CSC2 and Chandra-COSMOS Legacy, reaching a comparable accuracy as in eFEDS when limiting the magnitude of the counterparts to the depth of LS10. The method can be applied to fainter samples of AGNs using deeper optical data from future surveys (for example, LSST, Euclid), granting LS10-like information on the light distribution beyond the morphological type. Along with this paper, we have released an updated version of the photometric redshifts (including errors and probability distribution functions) for eROSITA/eFEDS.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

A two-in-one process for reliable graphene transistors processed with photo-lithography

Research on graphene field-effect transistors (GFETs) has mainly relied on devices fabricated using electron-beam lithography for pattern generation, a method that has known problems with polymer contaminants. GFETs fabricated via photo-lithography suffer even worse from other chemical contaminations, which may lead to strong unintentional doping of the graphene. In this letter, we report on a scalable fabrication process for reliable GFETs based on ordinary photo-lithography by eliminating the aforementioned issues. The key to making this GFET processing compatible with silicon technology lies in a two-in-one process where a gate dielectric is deposited by means of atomic layer deposition. During this deposition step, contaminants, likely unintentionally introduced during the graphene transfer and patterning, are effectively removed. The resulting GFETs exhibit current-voltage characteristics representative to that of intrinsic non-doped graphene. Fundamental aspects pertaining to the surface engineering employed in this work are investigated in the light of chemical analysis in combination with electrical characterization.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Highly reliable spin-coated titanium dioxide dielectric

Dielectric degradation as low as 0.3 % has been observed for a highly reliable Titanium dioxide (TiO{sub 2}) film after constant voltage stressing (CVS) with – 4 V for 10{sup 5} second at room temperature (300 K). The film was fabricated by sol –gel spin – coating method on a lightly doped p-Si (~10{sup 15} cm{sup −3}) substrate. The equivalent oxide thickness (EOT) is 7 nm with a dielectric constant 33 (at 1 MHz). Metal – Oxide – Semiconductor (MOS) capacitors have been fabricated with an optimum annealing temperature of 800°C for one hour in a preheated furnace. The dielectric degradation is annealing temperature dependent. A degradation of 1.4 %, 1.2 % and 1.1 % has been observed for 400°C, 600°C and 1000°C temperature annealed MOS respectively. The dielectric degradation increases below or above the optimum temperature of annealing.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Reliable density functional calculations for the electronic structure of thermoelectric material ZnSb

In this paper, we present the results of systematic test calculations for the electronic structure of thermoelectric material ZnSb using a first-principles full-potential all electron computational method. We used a linear combination of atomic orbitals (LACO) formalism, based on density functional theory (DFT). The exchange-correlation interaction potential of the many electron system was described by using a generalized gradient approximation (GGA). We compared the calculated indirect and direct band gaps as well as the effective masses of holes and electrons in ZnSb with experimental measurement results. The calculated indirect band gap of ZnSb is 0.56 eV, which agrees very well with the experimentally measured values of 0.50 eV ~ 0.61 eV. The calculated direct band gap at X point is 0.89 eV. The calculated effective masses of electrons and holes in ZnSb also agree with experimental data. The systematical test calculations as well as the comparisons of the calculated results with experimental measurements show that the obtained electronic structure of ZnSb would be reliable. We did not observe a major deficiency of the first-principles DFT calculation for the electronic structure of ZnSb, using full-potential all electron LACO method. The reported electronic structure of single crystal ZnSb from this work may provide a fundamental knowledge base for further research and applications for this important thermoelectric material.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Near-zero-field magnetoresistance measurements: A simple method to track atomic-scale defects involved in metal-oxide-semiconductor device reliability

We demonstrate the ability of a relatively new analytical technique, near-zero-field magnetoresistance (NZFMR), to track atomic-scale phenomena involved in the high-field stressing damage of fully processed Si metal-oxide-semiconductor field-effect transistors. We show that the technique is sensitive to both the Pb0 and Pb1 dangling bond centers and that the presence of both centers can be inferred through NZFMR via hyperfine interactions with the central 29Si atoms of the dangling bonds. The NZFMR results also provide evidence for the redistribution of mobile hydrogen atoms at the Si/SiO2 interface and also a potential change in the average dipolar coupling constant between electrons in neighboring defects. This work shows that NZFMR offers significant analytical power for studying technologically relevant semiconductor device reliability problems and has advantages in experimental simplicity over comparable techniques.

Moxim, Stephen J. (ORCID:0000000309659904)↗
DOE OSTI2023

Optoelectronic and structural characterization of trapezoidal defects in 4H-SiC epilayers and the effect on MOSFET reliability

To this day, trapezoidal defects are found in clusters and high counts in wafers representing the industry standard in terms of material quality being produced. This study sheds light on the nature, origin, behavior, and impact of this defect on device yield and reliability. Trapezoidal defects in 4H-SiC epitaxial layers were investigated by photoluminescence (PL) imaging, scanning electron microscopy (SEM), cathodoluminescence spectrum imaging (CLSI), SEM electron beam induced current (EBIC) imaging, and by transmission electron microscopy (TEM) observation. The bar-shaped stacking faults were identified by the PL and CL measurements with a peak emission wavelength of 420 and 450 nm. An optoelectronic behavioral study based on the recombination enhanced dislocation glide mechanism revealed how expanding dislocations and stacking faults interact with each other. Combining the luminescence and microscopy results, the nature of the stacking faults was identified as being a combination of Shockley-type and Frank-type stacking faults. The TEM analysis showed that these defects originate from the substrate and the stacking sequences of some of the faults were determined as (…2, 4, 2…) and (…2, 3, 2…) in the Zhdanov's notation by high-resolution TEM. The origin of this defect is speculated based on our results and previous reports. The EBIC imaging showed that the high density of SFs in these towers is a strong site of carrier recombination, which presumably has an impact on the transfer characteristics of SiC devices. Furthermore, these defects have shown to impact metal oxide semiconductor field effect transistors electrical performance via an increase in the on-state resistance depending on the coverage percentage of the tower of defects in the active area of the device.

4H-SiC↗
DOE OSTI2025

Developing reliable machine learning interatomic potential for Fe–Cr–Ni austenitic alloys

Gaining atomistic understanding of mechanical behavior of heat-resistant structural materials such as Fe–Cr–Ni-based alloys requires an approach with an accuracy close to density functional theory (DFT) that considers the intrinsic properties of the bulk lattice and important defects such as stacking faults, grain boundaries, and surfaces. This work aims to develop reliable machine learning interatomic potential (MLIAP) at cross-scale for Fe–Cr–Ni ternary alloys with a focus on the face-centered-cubic (fcc) solid solution structure. Leveraging the advantages of moment tensor potentials, which typically necessitate a relatively small training dataset and enable rapid calculations using the large-scale atomic/molecular massively parallel simulator package, we ensure the stability and accuracy of the trained potentials. Important defects such as stacking faults, grain boundaries, and surfaces for wide-range compositions are investigated. Structural, thermal, elastic, and defect properties are determined from molecular dynamics simulations comprising several thousand atoms, generated via canonical Monte Carlo simulations guided by the trained potential. The trained potential allows efficient atomic simulations of structural, thermal, and mechanical properties of fcc Fe–Cr–Ni solid solution alloys as a function of composition and temperature. Therefore, the MLIAP approach represents a major advancement from DFT calculations that are limited to small simulation sizes and traditional molecular dynamics simulations using relatively low accuracy potentials. Furthermore, this work outlines a practical foundation for further investigating the structural evolution and mechanical behavior of austenitic stainless steel and nickel-based alloys in a wide array of applications in extreme environments.

Crystal structure↗