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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

HESS and Fermi-LAT discovery of γ-rays from the blazar 1ES 1312-423

In this study, a deep observation campaign carried out by the High Energy Stereoscopic System (HESS) on Centaurus A enabled the discovery of γ-rays from the blazar 1ES 1312-423, 2° away from the radio galaxy. With a differential flux at 1 TeV of (Φ1 TeV) = (1.9 ± 0.6stat ± 0.4sys) × 10 -13 cm -2 s -1 TeV -1 corresponding to 0.5 percent of the Crab nebula differential flux and a spectral index Γ = 2.9 ± 0.5stat ± 0.2sys, 1ES 1312-423 is one of the faintest sources ever detected in the very high energy (E > 100 GeV) extragalactic sky. A careful analysis using three and a half years of Fermi Large Area Telescope (Fermi-LAT) data allows the discovery at high energies (E > 100 MeV) of a hard spectrum (Γ = 1.4 ± 0.4stat ± 0.2sys) source coincident with 1ES 1312-423. Radio, optical, UV and X-ray observations complete the spectral energy distribution of this blazar, now covering 16 decades in energy. Lastly, the emission is successfully fitted with a synchrotron self-Compton model for the non-thermal component, combined with a blackbody spectrum for the optical emission from the host galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Discovery of two neighbouring satellites in the Carina constellation with MagLiteS

We report the discovery of two ultra-faint satellites in the vicinity of the Large Magellanic Cloud (LMC) in data from the Magellanic Satellites Survey (MagLiteS ). Situated 18 deg (∼20 kpc) from the LMC and separated from each other by only 18 arcmin, Carina II and III form an intriguing pair. By simultaneously modelling the spatial and the colour–magnitude stellar distributions, we find that both Carina II and Carina III are likely dwarf galaxies, although this is less clear for Carina III. There are in fact several obvious differences between the two satellites. While both are well described by an old and metal poor population, Carina II is located at ∼36 kpc from the Sun, with M_V ∼ −4.5 and r_h ∼ 90 pc, and it is further confirmed by the discovery of 3 RR Lyrae at the right distance. In contrast, Carina III is much more elongated, measured to be fainter (M_V ∼ −2.4), significantly more compact (r_h ∼ 30 pc), and closer to the Sun, at ∼28 kpc, placing it only 8 kpc away from Car II. Together with several other systems detected by the Dark Energy Camera, Carina II and III form a strongly anisotropic cloud of satellites in the vicinity of the Magellanic Clouds.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

PDB Structure Data Impacted Discovery and Development of Recently FDA‐Approved Drugs

Protein Data Bank (PDB) was established in 1971 as the 1 st open access digital data resource in biology and medicine. Today, the PDB currently houses >145,000 atomic level biomolecular structures determined by crystallography, NMR spectroscopy, and 3D electron microscopy. Managed by the Worldwide Protein Data Bank partnership (wwPDB; wwpdb.org ), PDB data are used by researchers and educators to understand the role molecules play in health and disease of humans, animals, and plants, food and energy production, and other topics of concern to global prosperity and sustainability. RCSB PDB operates the US data center for the global PDB archive, and makes PDB data available at no charge to all data consumers without limitations on usage. During calendar 2017, >679 million structure data files were downloaded from the PDB by Data Consumers working worldwide. RCSB PDB served >1 million RCSB.org users worldwide with PDB data integrated with ~40 external data resources providing rich structural views of fundamental biology, biomedicine, and energy sciences, and supported >600,000 PDB101.rcsb.org educational website users around the globe. RCSB PDB examined the impact of PDB archiving and open access availability on the discovery of 210 new molecular entities (NMEs or drugs) approved by the United States (US) Food and Drug Administration (FDA) between 2010 and 2016. Biological targets for 90% of NMEs are known. Approximately 6,000 PDB structures containing one of these drug targets and/or one approved NME were identified, providing 85% coverage of known targets and 88% coverage of 2010–2016 approved NMEs. Coverage was comparable across all therapeutic areas. Most structures were both published and freely accessible from the PDB long before the NMEs were approved. Additional analyses revealed these PDB structures were cited by more than 200,000 PubMed‐indexed publications reporting pre‐competitive research on the would‐be drug targets. PDB structures and citing publications contributed substantially to understanding of the target biology, and led to biopharmaceutical company investment in successful drug discovery/development programs. Overall, access to PDB data contribute to patent applications, drug discovery and development, publication of scientific studies, innovations that can lead to new product development and company formation, and STEM education. Support or Funding Information RCSB PDB is funded by a grant (DBI‐1338415) from the National Science Foundation , the National Institutes of Health , and the US Department of Energy . This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Zardecki, Christine↗
DOE OSTI2026

SODAs: sparse optimization for the discovery of differential and algebraic equations

Differential-algebraic equations (DAEs) integrate ordinary differential equations (ODEs) with algebraic constraints, providing a fundamental framework for developing models of dynamical systems characterized by time-scale separation, conservation laws and physical constraints. While sparse optimization has revolutionized model development by allowing data-driven discovery of parsimonious models from a library of possible equations, existing approaches for dynamical systems assume DAEs can be reduced to ODEs by eliminating variables before model discovery. This assumption limits the applicability of such methods for DAE systems with unknown constraints and time scales. We introduce sparse optimization for differential-algebraic systems (SODAs), a data-driven method for the identification of DAEs in their explicit form. By discovering the algebraic and dynamic components sequentially without prior identification of the algebraic variables, this approach leads to a sequence of convex optimization problems. It has the advantage of discovering interpretable models that preserve the structure of the underlying physical system. To this end, SODAs improves since SODAs is singular numerical stability when handling high correlations between library terms, caused by near-perfect algebraic relationships, by iteratively refining the conditioning of the candidate library. We demonstrate the performance of our method on biological, mechanical and electrical systems, showcasing its robustness to noise in both simulated time series and real-time experimental data.

DAE↗
DOE OSTI2010

Supersymmetric discovery potential and benchmarks for early runs at {radical}(s)=7 TeV at the LHC

We carry out an analysis of the potential of the Large Hadron Collider (LHC) to discover supersymmetry in runs at {radical}(s)=7 TeV with an accumulated luminosity of (0.1-2) fb{sup -1} of data. The analysis is done with both minimal supergravity and supergravity models with nonuniversal soft breaking. Benchmarks for early discovery with (0.1-2) fb{sup -1} of data are given. We provide an update of b-tagging efficiencies in PGS 4 appropriate for LHC analyses. A large number of signature channels are analyzed, and it is shown that each of the models exhibited are discoverable at the 5{sigma} level or more above the standard model background in several signature channels which would provide cross checks for a discovery of supersymmetry. It is shown that some of the benchmarks are discoverable with 0.1 fb{sup -1} of data again with detectable signals in several channels.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

Nonuniversal gaugino masses and seminatural supersymmetry in view of the Higgs boson discovery

I consider models with non-universal gaugino masses at the gauge coupling unification scale, taking into account the Higgs boson discovery. Viable regions of parameter space are mapped and studied in the case of non-universality following from an F-term in a linear combination of singlet and adjoint representations of SU(5). I consider, in particular, "semi-natural" models that have small \mu, with gaugino masses dominating the supersymmetry breaking terms at high energies. Higgsino-like particles are then much lighter than all other superpartners, and the prospects for discovery at the Large Hadron Collider can be extremely challenging.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2012

Directional Bias and Pheromone for Discovery and Coverage on Networks

Natural multi-agent systems often rely on “correlated random walks” (random walks that are biased toward a current heading) to distribute their agents over a space (e.g., for foraging, search, etc.). Our contribution involves creation of a new movement and pheromone model that applies the concept of heading bias in random walks to a multi-agent, digital-ants system designed for cyber-security monitoring. We examine the relative performance effects of both pheromone and heading bias on speed of discovery of a target and search-area coverage in a two-dimensional network layout. We found that heading bias was unexpectedly helpful in reducing search time and that it was more influential than pheromone for improving coverage. We conclude that while pheromone is very important for rapid discovery, heading bias can also greatly improve both performance metrics.

Computer Security↗
DOE OSTI2011

Structure Discovery in Large Semantic Graphs Using Extant Ontological Scaling and Descriptive Statistics

As semantic datasets grow to be very large and divergent, there is a need to identify and exploit their inherent semantic structure for discovery and optimization. Towards that end, we present here a novel methodology to identify the semantic structures inherent in an arbitrary semantic graph dataset. We first present the concept of an extant ontology as a statistical description of the semantic relations present amongst the typed entities modeled in the graph. This serves as a model of the underlying semantic structure to aid in discovery and visualization. We then describe a method of ontological scaling in which the ontology is employed as a hierarchical scaling filter to infer different resolution levels at which the graph structures are to be viewed or analyzed. We illustrate these methods on three large and publicly available semantic datasets containing more than one billion edges each. Keywords-Semantic Web; Visualization; Ontology; Multi-resolution Data Mining;

Multiresolution Data Mining↗
DOE OSTI2024

Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization

Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often nondifferentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces. Often these systems are expensive or time consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest toward active learning methods such as Bayesian optimization (BO) where the adaptive exploration occurs based on human learning (discovery) objective. However, classical BO is based on a predefined optimization target, and policies balancing exploration and exploitation are purely data driven. In practical settings, the domain expert can pose prior knowledge of the system in the form of partially known physics laws and exploration policies often vary during the experiment. Here, we propose an interactive workflow building on multifidelity BO (MFBO), starting with classical (data-driven) MFBO, then expand to a proposed structured (physics-driven) structured MFBO (sMFBO), and finally extend it to allow human-in-the-loop interactive interactive MFBO (iMFBO) workflows for adaptive and domain expert aligned exploration. These approaches are demonstrated over highly nonsmooth multifidelity simulation data generated from an Ising model, considering spin–spin interaction as parameter space, lattice sizes as fidelity spaces, and the objective as maximizing heat capacity. Detailed analysis and comparison show the impact of physics knowledge injection and real-time human decisions for improved exploration with increased alignment to ground truth. Here, the associated notebooks allow to reproduce the reported analyses and apply them to other systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

A Simple, Multidimensional Approach to High-Throughput Discovery of Catalytic Reactions

Transition metal complexes catalyze many important reactions that are employed in medicine, materials science, and energy production. Although high-throughput methods for the discovery of catalysts that would mirror related approaches for the discovery of medicinally active compounds have been the focus of much attention, these methods have not been sufficiently general or accessible to typical synthetic laboratories to be adopted widely. We report a method to evaluate a broad range of catalysts for potential coupling reactions with the use of simple laboratory equipment. Specifically, we screen an array of catalysts and ligands with a diverse mixture of substrates and then use mass spectrometry to identify reaction products that, by design, exceed the mass of any single substrate. Lastly, with this method, we discovered a copper-catalyzed alkyne hydroamination and two nickel-catalyzed hydroarylation reactions, each of which displays excellent functional-group tolerance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Structure-guided discovery of ancestral CRISPR-Cas13 ribonucleases

The RNA-guided ribonuclease CRISPR-Cas13 enables adaptive immunity in bacteria and programmable RNA manipulation in heterologous systems. Cas13s share limited sequence similarity, hindering discovery of related or ancestral systems. Here, to address this, we developed an automated structural-search pipeline to identify an ancestral clade of Cas13 (Cas13an) and further trace Cas13 origins to defense-associated ribonucleases. Despite being one-third the size of other Cas13s, Cas13an mediates robust programmable RNA depletion and defense against diverse bacteriophages. However, unlike its larger counterparts, Cas13an uses a single active site for both CRISPR RNA processing and RNA-guided cleavage, revealing that the ancestral nuclease domain has two modes of activity. Discovery of Cas13an deepens our understanding of CRISPR-Cas evolution and expands opportunities for precision RNA editing, showcasing the promise of structure-guided genome mining.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Discovery of GuaB inhibitors with efficacy against Acinetobacter baumannii infection

ABSTRACT Guanine nucleotides are required for growth and viability of cells due to their structural role in DNA and RNA, and their regulatory roles in translation, signal transduction, and cell division. The natural antibiotic mycophenolic acid (MPA) targets the rate-limiting step inde novoguanine nucleotide biosynthesis executed by inosine-5´-monophosphate dehydrogenase (IMPDH). MPA is used clinically as an immunosuppressant, but whetherin vivoinhibition of bacterial IMPDH (GuaB) is a valid antibacterial strategy is controversial. Here, we describe the discovery of extremely potent small molecule GuaB inhibitors (GuaBi) specific to pathogenic bacteria with a low frequency of on-target spontaneous resistance and bactericidal efficacyin vivoagainstAcinetobacter baumanniimouse models of infection. The spectrum of GuaBi activity includes multidrug-resistant pathogens that are a critical priority of new antibiotic development. Co-crystal structures ofA. baumannii, Staphylococcus aureus, andEscherichia coliGuaB proteins bound to inhibitors show comparable binding modes of GuaBi across species and identifies key binding site residues that are predictive of whole-cell activity across both Gram-positive and Gram-negative clades of Bacteria. The clearin vivoefficacy of these small molecule GuaB inhibitors in a model ofA. baumanniiinfection validates GuaB as an essential antibiotic target. IMPORTANCE The emergence of multidrug-resistant bacteria worldwide has renewed interest in discovering antibiotics with novel mechanism of action. For the first time ever, we demonstrate that pharmacological inhibition ofde novoguanine biosynthesis is bactericidal in a mouse model ofAcinetobacter baumanniiinfection. Structural analyses of novel inhibitors explain differences in biochemical and whole-cell activity across bacterial clades and underscore why this discovery may have broad translational impact on treatment of the most recalcitrant bacterial infections.

Microbiology↗
DOE OSTI2020

Discovery of Quark-Gluon Plasma: Strangeness Diaries

Abstract We look from a theoretical perspective at the new phase of matter, quark-gluon plasma (QGP), the new form of nuclear matter created at high temperature and pressure. Here I retrace the path to QGP discovery and its exploration in terms of strangeness production and strange particle signatures. We will see the theoretical arguments that have been advanced to create interest in this determining signature of QGP. We explore the procedure used by several experimental groups making strangeness production an important tool in the search and discovery of this primordial state of matter present in the Universe before matter in its present form was formed. We close by looking at both the ongoing research that increases the reach of this observable to LHC energy scale pp collisions, and propose an interpretation of these unexpected results.

Physics↗
DOE OSTI2012

Domain-Specific Languages for Composing Signature Discovery Workflows

Domain-agnostic signature discovery entails investigation across multiple scientific disciplines. The breadth and cross-disciplinary nature of this work requires that existing executables be integrated with new capabilities into workflows, representing a wide range of user tasks. An algorithm may be written in multiple programming languages for various hardware platforms, and so workflow composition requires integrating executables from any number of remote hosts. This raises an engineering issue on how to generate web service wrappers for these heterogeneous executables and to compose them into a scientific workflow environment (e.g., Taverna). In this paper, we introduce two simple Domain-Specific Languages (DSLs) to automate these processes. Our Service Description Language (SDL) describes key elements of a signature discovery service and automatically generates its implementation code. The Workflow Description Language (WDL) describes the pipeline of services and generates deployable artifacts for the Taverna workflow management system. We demonstrate our approach with a real-world workflow composed of services wrapping remote executables.

Jacob, Ferosh↗
DOE OSTI2020

GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research

Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experimental drug screening is expensive and time consuming, and it is desirable to carry out large scale docking calculations in a high-throughput manner to narrow the experimental search space. Few of the existing computational docking tools were designed with high performance computing in mind. Therefore, optimizations to maximize use of high-performance computational resources available at leadership-class computing facilities enables these facilities to be leveraged for drug discovery. Here we present the porting, optimization, and validation of the AutoDock-GPU program for the Summit supercomputer, and its application to initial compound screening efforts to target proteins of the SARS-CoV-2 virus responsible for the current COVID-19 pandemic.

LeGrand, Scott↗
DOE OSTI2022

Machine Learning for the Discovery, Design, and Engineering of Materials

Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward ( a) the discovery of new materials through large-scale enumerative screening, ( b) the design of materials through identification of rules and principles that govern materials properties, and ( c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design.

Chemistry↗
DOE OSTI2024

Miniaturize the Redox Flow Battery for Accelerated Materials Discovery and Development

Redox flow batteries are a promising technology for grid-scale energy storage. The aqueous organic redox flow battery is of particular interest for its potentially low material cost and sustainability. Developing novel organic active material for flow battery electrolytes typically entails molecular engineering toward desired properties, necessitating organic synthesis. In a research laboratory setting, the synthesis of specifically designed organic molecules featuring targeted functional groups is time and resources intensive. In the past, synthesizing materials required for battery testing has often required gram-scale production, presenting considerable constraints on the pace of novel organic material discovery. In this report, we introduce a miniaturized cell design that mandates only milligram-scale material synthesis while yielding testing outcomes equivalent or superior to those reported with other commercially available or homemade flow cells in the literature. The test results under various pH conditions validate the scale-down strategy to accelerate the flow battery material discovery and development using the newly designed mini cell. This approach offers researchers an efficient means to notably reduce the time and resources required to develop novel materials for flow batteries.

25 ENERGY STORAGE↗
DOE OSTI2024

Methods for Causal Discovery

SAND2025-11742O Methods for Causal Discovery is a software tool that is used for causal discovery from data, including predicting and visualizing directed acyclic graphs from data using traditional machine learning techniques. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗