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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 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

RCSB protein data Bank: Next‐generation advanced search for exploration of experimental structures and computed structure models

Abstract The Protein Data Bank (PDB), established in 1971, is the primary global, open‐access archive for experimentally determined 3D macromolecular structures (proteins, RNA, DNA). The research‐focused RCSB.org web‐portal provides access to these data alongside more than one million machine‐learning‐predicted structure models, greatly expanding the available structural landscape. Rapid growth of both experimental and computational structures has increased the need for powerful yet accessible search tools that serve a broad and diverse scientific community. Herein, we describe a redesigned RCSB Protein Data Bank RCSB.org Advanced Search capability that supports intuitive discovery of 3D structures through a unified interface. This interface integrates annotation‐, sequence‐, and 3D structure‐based searches, embeds an interactive 3D viewer, and incorporates curated biological knowledge, such as catalytic site definitions from Mechanism and Catalytic Site Atlas and ligand‐guided structural motifs, for constructing geometry‐driven queries. A new Chemical Search tool allows definition of chemical queries via an integrated drawing tool or standard identifiers, seamlessly combining them with annotation filters. By allowing query definition directly within spatial and chemical contexts, these search interfaces reduce the need for detailed knowledge of residue numbering, chain identifiers, or external cheminformatics software. This capability enables efficient exploration of structures, chemical diversity, and structure–function relationships across all life domains. The redesigned interfaces can be accessed directly at rcsb.org/search/advanced for Advanced Search and rcsb.org/search/chemical for Chemical Search.

Rose, Yana [Research Collaboratory for Structural ↗
DOE OSTI2022

Novel approaches in hadron spectroscopy

The last two decades have witnessed the discovery of a myriad of new and unexpected hadrons. The future holds more surprises for us, thanks to new-generation experiments. Understanding the signals and determining the properties of the states requires a parallel theoretical effort. To make full use of available and forthcoming data, a careful amplitude modeling is required, together with a sound treatment of the statistical uncertainties, and a systematic survey of the model dependencies. We review the contributions made by the Joint Physics Analysis Center to the field of hadron spectroscopy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Investigation of the Solution Chemistry of Hybrid Organic–Inorganic Indium Halides for New Material Discovery

Recently, metal halide perovskites (MHPs) have emerged as a new class of materials for optical and electronic applications such as solar cells and ionizing radiation detectors. Although the solution-processability of MHPs is among their greatest advantages, the solution chemistries of most metal halide systems and their relationship with the observed structural and chemical diversity are poorly understood. In this work, we study the solution chemistry of a model indium halide system, methylammonium (MA)–In–Br, using a combination of the UV–vis spectroscopy, electrospray ionization mass spectrometry (ESI-MS) measurements, small-angle X-ray scattering (SAXS), and density functional theory (DFT) calculations. Our results show that indium could form either octahedral [InBr 6 3– ] or tetrahedral [InBr 4 – ] anions in solution or a combination of both, depending on the loading ratios of MABr and InBr 3 reactants. Understanding the solution chemistry of this system and recognizing the optical fingerprints of these polyanions allow for targeted crystallization of two novel compounds: MAInBr 4 featuring tetrahedral [InBr 4 – ] anions and MA 2 InBr 5 containing both octahedral [InBr 6 3– ] and tetrahedral [InBr 4 – ] anions. Further increase of the MABr content leads to the formation of previously reported MA 4 InBr 7 , containing only octahedral [InBr 6 3– ] anions separated by Br – anions. Furthermore, our results suggest that understanding the solution chemistry of multinary metal halide systems could be a valuable tool for discovering functional materials for practical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Majorana modes with side features in magnet-superconductor hybrid systems

Magnet-superconductor hybrid (MSH) systems represent promising platforms to host Majorana zero modes (MZMs), the elemental building blocks for fault-tolerant quantum computers. Theoretical description of such MSH structures is mostly based on simplified models, not accounting for the complexity of real materials. Here, based on density functional theory, we derive a superconducting 80-band model to study an MSH system consisting of a magnetic manganese chain on the s wave superconductor niobium. For a wide range of values of the superconducting order parameter, the system is a topological superconductor, with MZMs exhibiting non-universal spatial patterns and a drastic accumulation of spectral weight on both sides along the magnetic chain. These side feature states can be explained by an effective model which is guided by the ab initio results. Performing scanning tunneling spectroscopy experiments on the same system, we observe a spatial structure in the low-energy local density of states that is consistent with the theoretical findings. Our results open a first-principle approach to the discovery of topological superconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multiscale Patterning from Competing Interactions and Length Scales

We live in an era of complexity marked by impressive new tools powering the scientific method to accelerate discovery, prediction, and control of increasingly complex systems. In common with many disciplines and societal challenges and opportunities, materials and condensed matter sciences are beneficiaries. The volume and fidelity of experimental, computational, and visualization data available, and tools to rapidly interpret them, are remarkable. Conceptual frameworks, including multiscale, multiphysics modeling of this complexity, are fueled by the data and, in turn, guide directions for future experimental and computational strategies. In this spirit, we discuss the importance of competing interactions, length scales, and constraints as pervasive sources of spatio-temporal complexity. We use representative examples drawn from materials and condensed matter, including the important role of elasticity in some technologically important quantum materials.

36 MATERIALS SCIENCE↗
NASA NTRS1973

A review of processable high temperature resistant addition-type laminating resins

An important finding that resulted from research that was conducted to develop improved ablative resins was the discovery of a novel approach to synthesize processable high temperature resistant polymers. Low molecular weight polyimide prepolymers end-capped with norbornene groups were polymerized into thermo-oxidatively stable modified polyimides without the evolution of void producing volatile materials. This paper reviews basic studies that were performed using model compounds to elucidate the polymerization mechanism of the so-called addition-type polyimides. The fabrication and properties of polyimide/graphite fiber composites using A-type polyimide prepolymer as the matrix are described. An alternate method for preparing processable A-type polyimides by means of in situ polymerization of monomeric reactants on the fiber reinforcement is also described. Polyimide/graphite fiber composite performance at elevated temperatures is presented for A-type polyimides.

Serafini, T. T.↗
NASA NTRS1982

Jovimagnetic secular variation

The history of Jupiter's planetary magnetic field is of great interest, in particular, because it represents the first opportunity to observe secular variations of a planetary dynamo other than the earth's. Indirect observations of the Jovian magnetic field began with the discovery of nonthermal radio emissions by Burke and Franklin (1955). In situ observations of the Jovian planetary magnetic field are limited to the Pioneer 10 and 11 flybys at epochs 1973.9 and 1974.9 and the Voyager 1 and 2 flyby encounters at epochs 1979.2 and 1979.5. Jovian magnetic field models are presented in a table, and a graph is provided in which the secular decrease of the earth's main dipole term is compared with estimates of Jupiter's main dipole term. The in situ magnetic field observations are consistent with no secular variation of the jovimagnetic dipole field from 1974.9 to 1979.2.

Connerney, J. E. P.↗
NASA NTRS1988

Laboratory studies of sticking coefficients and heterogeneous reactions important in the stratosphere

The discovery of ozone depletion during spring in the Antarctic stratosphere has received wide spread attention. Both meteorological and chemical mechanisms have been used in attempts to explain this observation. The chemical theory focused on the chlorofluoromethanes released into the atmosphere. However, gas-phase, homogeneous reactions alone in the model can not adequately explain such a depletion. Heterogeneous reactions on ice surfaces could be important in the Antarctic stratosphere. These reactions are thought to convert ClONO2 and N2O5 into HNO3 in the solid phase while Cl2, HOCl, and ClNO2 are released into the stratosphere as gas-phase products. The photolysis of Cl2, HOCl, and ClNO2, then produces active chlorine which subsequently removes ozone through several catalytic cycles, including the Cl2O2 mechanism. The polar stratospheric clouds are thought to consist of mixtures of water ice, nitric acid, and sulfuric acid. Condensation of HCl onto the PSC's could provide active surfaces for heterogeneous reactions.

Leu, Ming-Taun↗
NASA NTRS1992

Discovery of X-ray emission associated with the Gum Nebula

The Gum Nebula was observed by the A-2 LED proportional counters on the HEAO-1 satellite as part of the all-sky survey. The first detection of X-ray emission associated with the Gum Nebula is reported. Soft X-ray spectra were constructed from the A-2 LED PHA data. Single temperature Raymond-Smith models were fitted to the observed spectra to yield temperature, column density and emission measure. The temperature is 6 x 10 exp 5 K, the column density 4 x 10 exp 20/sq cm, and the emission measure 5 cm exp-6 pc. The X-ray and optical properties of the Gum Nebula are consistent with a supernova remnant in the shell stage of evolution, which was the product of an energetic (3 x 10 exp 51 ergs) supernova explosion which occurred about 2 x 10 exp 6 yr ago.

Leahy, D. A.↗
NASA NTRS1993

MARIKA - A model revision system using qualitative analysis of simulations

This paper describes portions of a novel system called MARIKA (Model Analysis and Revision of Implicit Key Assumptions) to automatically revise a model of the normal human orientation system. The revision is based on analysis of discrepancies between experimental results and computer simulations. The discrepancies are calculated from qualitative analysis of quantitative simulations. The experimental and simulated time series are first discretized in time segments. Each segment is then approximated by linear combinations of simple shapes. The domain theory and knowledge are represented as a constraint network. Incompatibilities detected during constraint propagation within the network yield both parameter and structural model alterations. Interestingly, MARIKA diagnosed a data set from the Massachusetts Eye and Ear Infirmary Vestibular Laboratory as abnormal though the data was tagged as normal. Published results from other laboratories confirmed the finding. These encouraging results could lead to a useful clinical vestibular tool and to a scientific discovery system for space vestibular adaptation.

Groleau, Nicolas↗
NASA NTRS1999

Discovery of a New Soft Gamma Repeater, SGR 1627-41

We report the discovery of a new soft gamma repeater (SGR), SGR 1627-41, and present BATSE observations of the burst emission and BeppoSAX NFI observations of the probable persistent X-ray counterpart to this SGR. All but one burst spectrum are well fit by an optically thin thermal bremsstrahlung (OTTB) model with kT values between 25 and 35 keV. The spectrum of the X-ray counterpart, SAX J1635.84736, is similar to that of other persistent SGR X-ray counterparts. We find weak evidence for a periodic signal at 6.41 s in the light curve for this source. Like other SGRs, this source appears to be associated with a young supernova remnant G337.0-0.1. Based upon the peak luminosities of bursts observed from this SGR, we find a lower limit on the dipole magnetic field of the neutron star B_dipole greater than 5e10(exp 14) Gauss.

Woods, Peter↗
NASA NTRS2017

The Role of KREEP in the Production of Mg-Suite Magmas and Its Influence on the Extent of Mg-Suite Magmatism in the Lunar Crust

The lunar magnesian-suite, or Mg-suite, is a series of ancient plutonic rocks from the lunar crust. They have received a considerable amount of attention from lunar scientists since their discovery for three primary reasons: 1) their ages and geochemistry indicate they represent pristine magmatic samples that crystallized very soon after the formation of the Moon; 2) their ages often overlap with ages of the ferroan anorthosite (FAN) crust; and 3) planetary-scale processes are needed in formation models to account for their unique geochemical features. Taken as a whole, the Mg-suite samples, as magmatic cumulate rocks, approximate a fractional crystallization sequence in the low-pressure forsterite-anorthite-silica system, and thus these samples are generally thought to be derived from layered mafic intrusions which crystallized very slowly from magmas that intruded the anorthositic crust. However, no direct linkages have been established between different Mg-suite samples based either on field relationships or geochemistry.The model for the origin of the Mg-suite, which best fits the limited available data, is one where Mg-suite magmas form from melting of a hybrid cumulate package consisting of deep mantle dunite, crustal anorthosite, and KREEP (potassium-rare earth elements-phosphorus) at the base of the crust under the Procellarum KREEP Terrane (PKT). In this model, these three LMO (Lunar Magma Ocean) cumulate components are brought into close proximity by the cumulate overturn process. Deep mantle dunitic cumulates with an Mg number of approximately 90 rise to the base of the anorthositic crust due to their buoyancy relative to colder, more dense Fe- and Ti-rich cumulates. This hybridized source rock melts to form Mg-suite magmas, saturated in Mg-rich olivine and anorthitic plagioclase, that have a substantial KREEP component.

Elardo, S. M.↗
DOE OSTI2025

VISION: a modular AI assistant for natural human-instrument interaction at scientific user facilities

Scientific user facilities, such as synchrotron beamlines, are equipped with a wide array of hardware and software tools that require a codebase for human-computer-interaction. This often necessitates developers to be involved to establish connection between users/researchers and the complex instrumentation. The advent of generative AI presents an opportunity to bridge this knowledge gap, enabling seamless communication and efficient experimental workflows. Here we present a modular architecture for the Virtual Scientific Companion by assembling multiple AI-enabled cognitive blocks that each scaffolds large language models (LLMs) for a specialized task. With VISION, we performed LLM-based operation on the beamline workstation with low latency and demonstrated the first voice-controlled experiment at an x-ray scattering beamline. The modular and scalable architecture allows for easy adaptation to new instruments and capabilities. Development on natural language-based scientific experimentation is a building block for an impending future where a science exocortex—a synthetic extension to the cognition of scientists—may radically transform scientific practice and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A goldilocks computational protocol for inhibitor discovery targeting DNA damage responses including replication-repair functions

While many researchers can design knockdown and knockout methodologies to remove a gene product, this is mainly untrue for new chemical inhibitor designs that empower multifunctional DNA Damage Response (DDR) networks. Here, we present a robust Goldilocks (GL) computational discovery protocol to efficiently innovate inhibitor tools and preclinical drug candidates for cellular and structural biologists without requiring extensive virtual screen (VS) and chemical synthesis expertise. By computationally targeting DDR replication and repair proteins, we exemplify the identification of DDR target sites and compounds to probe cancer biology. Our GL pipeline integrates experimental and predicted structures to efficiently discover leads, allowing early-structure and early-testing (ESET) experiments by many laboratories. By employing an efficient VS protocol to examine protein-protein interfaces (PPIs) and allosteric interactions, we identify ligand binding sites beyond active sites, leveraging in silico advances for molecular docking and modeling to screen PPIs and multiple targets. A diverse 3,174 compound ESET library combines Diamond Light Source DSI-poised, Protein Data Bank fragments, and FDA-approved drugs to span relevant chemotypes and facilitate downstream hit evaluation efficiency for academic laboratories. Two VS per library and multiple ranked ligand binding poses enable target testing for several DDR targets. This GL library and protocol can thus strategically probe multiple DDR network targets and identify readily available compounds for early structural and activity testing to overcome bottlenecks that can limit timely breakthrough drug discoveries. By testing accessible compounds to dissect multi-functional DDRs and suggesting inhibitor mechanisms from initial docking, the GL approach may enable more groups to help accelerate discovery, suggest new sites and compounds for challenging targets including emerging biothreats and advance cancer biology for future precision medicine clinical trials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Integrated High‐Throughput and Machine Learning Methods to Accelerate Discovery of Molten Salt Corrosion‐Resistant Alloys

Insufficient availability of molten salt corrosion-resistant alloys severely limits the fruition of a variety of promising molten salt technologies that could otherwise have significant societal impacts. To accelerate alloy development for molten salt applications and develop fundamental understanding of corrosion in these environments, here an integrated approach is presented using a set of high-throughput (HTP) alloy synthesis, corrosion testing, and modeling coupled with automated characterization and machine learning. By using this approach, a broad range of Cr-Fe-Mn-Ni alloys are evaluated for their corrosion resistances in molten salt simultaneously demonstrating that corrosion-resistant alloy development can be accelerated by 2 to 3 orders of magnitude. Based on the obtained results, a sacrificial protection mechanism is unveiled in the corrosion of Cr-Fe-Mn-Ni alloys in molten salts which can be applied to protect the less unstable elements in the alloy from being depleted, and provided new insights on the design of high-temperature molten salt corrosion-resistant alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer

Natural language processing (NLP), which aims to convert human language into expressions that can be analyzed by computers, is one of the most rapidly developing and widely used technologies in the field of artificial intelligence. Natural language processing algorithms convert unstructured free text data into structured data that can be extracted and analyzed at scale. In medicine, this unlocking of the rich, expressive data within clinical free text in electronic medical records will help untap the full potential of big data for research and clinical purposes. Recent major NLP algorithmic advances have significantly improved the performance of these algorithms, leading to a surge in academic and industry interest in developing tools to automate information extraction and phenotyping from clinical texts. Thus, these technologies are poised to transform medical research and alter clinical practices in the future. Radiation oncology stands to benefit from NLP algorithms if they are appropriately developed and deployed, as they may enable advances such as automated inclusion of radiation therapy details into cancer registries, discovery of novel insights about cancer care, and improved patient data curation and presentation at the point of care. However, challenges remain before the full value of NLP is realized, such as the plethora of jargon specific to radiation oncology, nonstandard nomenclature, a lack of publicly available labeled data for model development, and interoperability limitations between radiation oncology data silos. Successful development and implementation of high quality and high value NLP models for radiation oncology will require close collaboration between computer scientists and the radiation oncology community. Here, we present a primer on artificial intelligence algorithms in general and NLP algorithms in particular; provide guidance on how to assess the performance of such algorithms; review prior research on NLP algorithms for oncology; and describe future avenues for NLP in radiation oncology research and clinics.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Probabilistic Deep Learning Approach to Automate the Interpretation of Multi-phase Diffraction Spectra

Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase mixtures. At the core of this algorithm lies an ensemble convolutional neural network trained on simulated diffraction spectra, which are systematically augmented with physics-informed perturbations to account for artifacts that can arise during experimental sample preparation and synthesis. In this work, larger perturbations associated with off-stoichiometry are also captured by supplementing the training set with hypothetical solid solutions. Spectra containing mixtures of materials are analyzed with a newly developed branching algorithm that utilizes the probabilistic nature of the neural network to explore suspected mixtures and identify the set of phases that maximize confidence in the prediction. Our model is benchmarked on simulated and experimentally measured diffraction spectra, showing exceptional performance with accuracies exceeding those given by previously reported methods based on profile matching and deep learning. We envision that the algorithm presented here may be integrated in experimental workflows to facilitate the high-throughput and autonomous discovery of inorganic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Deep Gaussian process-based cost-aware batch Bayesian optimization for complex materials design campaigns

The accelerating pace and expanding scope of materials discovery demand optimization frameworks that efficiently navigate vast design spaces with complex response surfaces while judiciously allocating limited evaluation resources. We present a cost-aware, batch Bayesian optimization scheme powered by deep Gaussian process (DGP) surrogates and a heterotopic querying strategy. Our DGP surrogate, formed by stacking GP layers, models complex hierarchical relationships among high-dimensional compositional features and captures correlations across multiple target properties, propagating uncertainty through successive layers. We integrate evaluation cost into an upper-confidence-bound acquisition extension, which, together with heterotopic querying, proposes small batches of candidates in parallel, balancing exploration of under-characterized regions with exploitation of high-mean, low-variance predictions across correlated properties. Applied to refractory high-entropy alloys for high-temperature applications, our framework converges to optimal formulations in fewer iterations with cost-aware queries than conventional GP-based BO, highlighting the value of deep, uncertainty-aware, cost-sensitive strategies in materials campaigns.

36 MATERIALS SCIENCE↗