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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Structure-Based Design of a Covalent Inhibitor of the SET Domain-Containing Protein 8 (SETD8) Lysine Methyltransferase

Selective inhibitors of protein lysine methyltransferases, including SET domain-containing protein 8 (SETD8), are highly desired, as only a fraction of these enzymes are associated with high-quality inhibitors. From our previously discovered SETD8 inhibitor, we developed a more potent analog and solved a cocrystal structure, which is the first crystal structure of SETD8 in complex with a small-molecule inhibitor. This cocrystal structure allowed the design of a covalent inhibitor of SETD8 (MS453), which specifically modifies a cysteine residue near the inhibitor binding site, has an IC 50 value of 804 nM, reacts with SETD8 with near-quantitative yield, and is selective for SETD8 against 28 other methyltransferases. We also solved the crystal structure of the covalent inhibitor in complex with SETD8. In conclusiont, this work provides atomic-level perspective on the inhibition of SETD8 by small molecules and will help identify high-quality chemical probes of SETD8.

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

Kohn–Sham Density in a Slater Orbital Basis Set

Finite, atom-centered Slater basis sets are used to determine approximate Kohn–Sham molecular orbitals. This is achieved by minimizing the kinetic energy plus the sum-squared difference between the Kohn–Sham density and the full configuration interaction density. As a result of the finite basis, a weight factor is introduced to balance the two minimization components. Results herein show that this can be done systematically, without sensitive dependence on the choice of scaling factor. In addition, the algorithm is applied to the LiH diatomic for fractional electron counts, where stretching the bond introduces significant reorganization of the electron density. As a result, the analysis will show the correct KS orbital structure and reveal the effects of correlation and electron locality on the KS solutions.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2013

Determination of a Density Functional Tight Binding Model with an Extended Basis Set and Three-Body Repulsion for Carbon Under Extreme Pressures and Temperatures

In this paper, we report here on development of a density functional tight binding (DFTB) simulation approach for carbon under extreme pressures and temperatures that includes an expanded basis set and an environmentally dependent repulsive energy. We find that including d-orbital interactions in the DFTB Hamiltonian improves determination of the electronic states at high pressure–temperature conditions, compared to standard DFTB implementations that utilize s- and p-orbitals only for carbon. We then determine a three-body repulsive energy through fitting to diamond, BC8, and simple cubic cold compression curve data, as well pressures from metallic liquid configurations from density functional theory (DFT) simulations. Our new model (DFTB-p3b) yields approximately 2 orders of magnitude increase in computational efficiency over standard DFT while retaining its accuracy for condensed phases of carbon under a wide range of conditions, including the metallic liquid phase at conditions up to 2000 GPa and 30 000 K. Finally, our results provide a straightforward method by which DFTB can be extended to studies of covalently bonded materials under extremely high pressures and temperatures such as the interiors of planets and other large celestial bodies.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Meteorology and Snow Data Set From Adjacent Forested and Meadow Sites at Crested Butte, CO, USA

We present meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains (USA) over three consecutive water years with different amounts of snow water equivalent (SWE) accumulation: A year with above average SWE (2019), a year with average SWE (2020), and a year with below average SWE (2021). This data set is distinguished by its emphasis on paired open-forest sites in a continental snow climate. Approximately once a month during February–May, we collected data from 15 to 20 snow pits and took 8 to 19 snow depth transects. Our sampling sites were in open and adjacent forested areas at 3,100 m and in a lower elevation aspen (3,035 m) and higher elevation conifer stand (3,395 m). In total, we recorded 270 individual snow pit density and temperature profiles and over 4,000 snow depth measurements. These data are complimented by continuous meteorological measurements from two weather stations: One in the open and one in the adjacent forest. Meteorology data—including incoming shortwave and longwave radiation, outgoing shortwave radiation, relative humidity, wind speed, snow depth, and air and infrared surface temperature—were quality controlled and the forcing data were gap-filled. These data are available to download from Bonner, Smyth, et al. at three levels of processing, including a level with downscaled, adjusted precipitation based on data assimilation using observed snow depth and a process-based snow model. We demonstrate the utility of these data with a modeling experiment that explores open-forest differences and identifies opportunities for improvements in model representation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Capturing the Relative‐Humidity‐Sensitive Gas–Particle Partitioning of Organic Aerosols in a 2D Volatility Basis Set

Abstract Aerosol water affects the physicochemical properties and mass concentration of organic aerosols (OA), but it is typically omitted by air quality, weather, and climate models. We compare two classes of simplified models to estimate the OA water uptake and gas–particle partitioning of organic compounds. One class uses a single‐hygroscopicity‐parameter (κ) approach while the other is based on the reduced‐complexity Binary Activity Thermodynamics (BAT) model. We show that a BAT‐based two‐dimensional volatility basis set (VBS) model always predicts a higher OA mass concentration at elevated relative humidity (RH), for example, ∼16% at 80% RH, than any variation of theκ‐based method considered—even when BAT‐VBS predicts a lower water uptake. The main reason being that the BAT‐VBS model captures variations in effective saturation mass concentration of organics (C*) with RH, a feature that other VBS methods lack. The BAT‐VBS framework offers an efficient, RH‐sensitive treatment for reduced‐complexity OA modeling.

Geology↗
DOE OSTI2025

The System for Classification of Low-Pressure Systems (SyCLoPS): An All-In-One Objective Framework for Large-Scale Data Sets

We propose the first unified objective framework (SyCLoPS) for detecting and classifying all types of low-pressure systems (LPSs) in a given data set. We use the state-of-the-art automated feature tracking software TempestExtremes (TE) to detect and track LPS features globally in ERA5 and compute 16 parameters from commonly found atmospheric variables for classification. A Python classifier is implemented to classify all LPSs at once. The framework assigns 16 different labels (classes) to each LPS data point and designates four different types of high-impact LPS tracks, including tracks of tropical cyclone (TC), monsoonal system, subtropical storm and polar low. The classification process involves disentangling high-altitude and drier LPSs, differentiating tropical and non-tropical LPSs using novel criteria, and optimizing for the detection of the four types of high-impact LPS. A comparison of our labels with those in the International Best Track Archive for Climate Stewardship (IBTrACS) revealed an overall accuracy of 95% in distinguishing between tropical systems, extratropical cyclones, and disturbances. SyCLoPS produces a better TC detection skill compared to the previous algorithms, highlighted by an approximately 6% reduction in the false alarm rate compared to the previous TE algorithm. The vertical cross section composite of the four types of high-impact LPS we detect each shows distinct structural characteristics. Finally, we demonstrate that SyCLoPS is valuable for investigating various aspects of LPSs in climate data, such as the evolution of a single LPS track, patterns of LPS frequencies, and precipitation or wind influence associated with a particular LPS class.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Demonstration of qubit operations below a rigorous fault tolerance threshold with gate set tomography

Quantum information processors promise fast algorithms for problems inaccessible to classical computers. But since qubits are noisy and error-prone, they will depend on fault-tolerant quantum error correction (FTQEC) to compute reliably. Quantum error correction can protect against general noise if—and only if—the error in each physical qubit operation is smaller than a certain threshold. The threshold for general errors is quantified by their diamond norm. Until now, qubits have been assessed primarily by randomized benchmarking, which reports a different error rate that is not sensitive to all errors, and cannot be compared directly to diamond norm thresholds. Finally, we use gate set tomography to completely characterize operations on a trapped-Yb + -ion qubit and demonstrate with greater than 95% confidence that they satisfy a rigorous threshold for FTQEC (diamond norm ≤6.7 × 10 -4 ).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

In marine Bacteroidetes the bulk of glycan degradation during algae blooms is mediated by few clades using a restricted set of genes

We investigated Bacteroidetes during spring algae blooms in the southern North Sea in 2010–2012 using a time series of 38 deeply sequenced metagenomes. Initial partitioning yielded 6455 bins, from which we extracted 3101 metagenome-assembled genomes (MAGs) including 1286 Bacteroidetes MAGs covering ~120 mostly uncultivated species. We identified 13 dominant, recurrent Bacteroidetes clades carrying a restricted set of conserved polysaccharide utilization loci (PULs) that likely mediate the bulk of bacteroidetal algal polysaccharide degradation. The majority of PULs were predicted to target the diatom storage polysaccharide laminarin, alpha-glucans, alpha-mannose-rich substrates, and sulfated xylans. Metaproteomics at 14 selected points in time revealed expression of SusC-like proteins from PULs targeting all of these substrates. Analyses of abundant key players and their PUL repertoires over time furthermore suggested that fewer and simpler polysaccharides dominated early bloom stages, and that more complex polysaccharides became available as blooms progressed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Clustering huge protein sequence sets in linear time

Metagenomic datasets contain billions of protein sequences that could greatly enhance largescale functional annotation and structure prediction. Utilizing this enormous resource would require reducing its redundancy by similarity clustering. However, clustering hundreds of millions of sequences is impractical using current algorithms because their runtimes scale as the input set size N times the number of clusters K, which is typically of similar order as N, resulting in runtimes that increase almost quadratically with N. We developed Linclust, the first clustering algorithm whose runtime scales as N, independent of K. It can also cluster datasets several times larger than the available main memory. We cluster 1.6 billion metagenomic sequence fragments in 10 h on a single server to 50% sequence identity, >1000 times faster than has been possible before. Linclust will help to unlock the great wealth contained in metagenomic and genomic sequence databases.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing

Abstract Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and stacks whose variability makes it difficult to reproduce findings. Here, we establish a common reference frame for computations in digital neuromorphic systems, titled Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational and composable model primitives as hybrid systems combining continuous-time dynamics and discrete events. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the computational model, while bridging differences between the evaluated implementation and the underlying mathematical formalism. NIR supports an unprecedented number of neuromorphic systems, which we demonstrate by reproducing three spiking neural network models of different complexity across 7 neuromorphic simulators and 4 digital hardware platforms. NIR decouples the development of neuromorphic hardware and software, enabling interoperability between platforms and improving accessibility to multiple neuromorphic technologies. We believe that NIR is a key next step in brain-inspired hardware-software co-evolution, enabling research towards the implementation of energy efficient computational principles of nervous systems. NIR is available atneuroir.org

Science & Technology - Other Topics↗
DOE OSTI2020

Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm

Abstract We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a material’s composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm—namely, that crystal graph methods appear to outperform traditional machine learning methods given ~10 4 or greater data points. We encourage evaluating materials ML algorithms on the Matbench benchmark and comparing them against the latest version of Automatminer.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set

Abstract This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases ofd-block elements. In exhaustive detail, we contrast the performance of force, energy, and stress predictions across the transition metals for two leading MLFF models: a kernel-based atomic cluster expansion method implemented using sparse Gaussian processes (FLARE), and an equivariant message-passing neural network (NequIP). Early transition metals present higher relative errors and are more difficult to learn relative to late platinum- and coinage-group elements, and this trend persists across model architectures. Trends in complexity of interatomic interactions for different metals are revealed via comparison of the performance of representations with different many-body order and angular resolution. Using arguments based on perturbation theory on the occupied and unoccupieddstates near the Fermi level, we determine that the large, sharpddensity of states both above and below the Fermi level in early transition metals leads to a more complex, harder-to-learn potential energy surface for these metals. Increasing the fictitious electronic temperature (smearing) modifies the angular sensitivity of forces and makes the early transition metal forces easier to learn. This work illustrates challenges in capturing intricate properties of metallic bonding with current leading MLFFs and provides a reference data set for transition metals, aimed at benchmarking the accuracy and improving the development of emerging machine-learned approximations.

Chemistry↗
DOE OSTI2020

A near-term to net zero alternative to the social cost of carbon for setting carbon prices

The social cost of carbon (SCC) is commonly described and used as the optimal CO 2 price. However, the wide range of SCC estimates provides limited practical assistance to policymakers setting specific CO 2 prices. In this study we describe an alternate near-term to net zero (NT2NZ) approach, estimating CO 2 prices needed in the near term for consistency with a net-zero CO 2 emissions target. This approach dovetails with the emissions-target-focused approach that frames climate policy discussions around the world, avoids uncertainties in estimates of climate damages and long-term decarbonization costs, offers transparency about sensitivities and enables the consideration of CO 2 prices alongside a portfolio of policies. We estimate illustrative NT2NZ CO 2 prices for the United States; for a 2050 net-zero CO 2 emission target, prices are US $ \$34$ to US $ \$64$ per metric ton in 2025 and US $ \$77$ to US $ \$124$ in 2030. These results are most influenced by assumptions about complementary policies and oil prices.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evidence-based target setting informs blue carbon strategies for nationally determined contributions

The magnitude and pace of global climate change demand ambitious and effective implementation of nationally determined contributions (NDCs). Nature-based solutions present an efficient approach to achieving mitigation, adaptation and resilience goals. Yet few nations have quantified the diverse benefits of nature-based solutions to evaluate and select ecosystem targets for their NDCs. Here we report on Belize’s pursuit of innovative, evidence-based target setting by accounting for multiple benefits of blue carbon strategies. Through quantification of carbon storage and sequestration and optimization of co-benefits, we explore time-bound targets and prioritize locations for mangrove protection and restoration. We find increases in carbon benefits with larger mangrove investments, while fisheries, tourism and coastal risk-reduction co-benefits grow initially and then plateau. We identify locations, currently lacking protected status, where prioritizing blue carbon strategies would provide the greatest delivery of co-benefits to communities. These findings informed Belize’s updated NDCs to include an additional 12,000 ha of mangrove protection and 4,000 ha of mangrove restoration, respectively, by 2030. Our study serves as an example for the more than 150 other countries that have the opportunity to enhance greenhouse gas sequestration and climate adaptation by incorporating blue carbon strategies that provide multiple societal benefits into their NDCs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Building morphologies of the USA structures database; a gauntlet feature set

In recent years there has been a proliferation of methods and data to extract building footprints from satellite imagery. However there has been very little effort to provide additional insight about these buildings beyond their spatial location and shape. Features derived from their geometries can be used to better characterize these buildings which are critical for further research and development. In this work a set of 65 unique features for every building for more than 131 million buildings covering the US has been developed. This rich feature dataset will enable researchers, policymakers and various agencies to derive additional building characteristics like height, occupancy type, and help to gain valuable and new insights of the built environment.

Environmental sciences↗
DOE OSTI2022

Impact of chitin nanofibers and nanocrystals from waste shrimp shells on mechanical properties, setting time, and late-age hydration of mortar

Every year ~ 6–8 million tonnes of shrimp, crab, and lobster shell wastes are generated, requiring costly disposal procedures. In this study, the chitin content of shrimp shell waste was oxidized to produce chitin nanocrystals (ChNC) and mechanically fibrillated to obtain chitin nanofibers (ChNF) and evaluated as additives for mortar. ChNF (0.075 wt%) and ChNC (0.05 wt%) retarded the final setting time by 50 and 30 min, likely through cement dispersion by electrostatic repulsion. ChNF (0.05 wt%) with a larger aspect ratio than ChNC resulted in the greatest improved flexural strength and fracture energy by 24% and 28%. Elastic modulus increased by up to 91% and 43% with ChNC and ChNF. Solid-state nuclear magnetic resonance (NMR) showed ChNF (0.05 wt%) enhanced calcium–silicate–hydrate structure with a 41% higher degree of polymerization, 9% more silicate chain length, and a 15% higher degree of hydration at 28 days. Based on the findings, chitin seems a viable biomass source for powerful structural nanofibers and nanocrystals for cementitious systems to divert seafood waste from landfills or the sea.

36 MATERIALS SCIENCE↗