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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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All sourcesNASA NTRSDOE OSTI
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 OSTI2020

JSONize: A Scalable Machine Learning Pipeline to Model Medical Notes as Semi-structured Documents

The Department of Veteran's Affairs (VA) archives the largest corpora of clinical notes in their corporate data warehouse (CDW) as unstructured text data. Unstructured text easily supports keyword searches and regular expressions. Often these simple searches do not adequately support the complex searches that need to be performed on notes. For example, a researcher may want all notes with a Duke Treadmill Score less than 5 or people that smoke more than 1 pack per day. Range queries like this and more can be supported by modelling text as semi-structured documents. In this paper, we implement a scalable machine learning pipeline that models plain medical text as useful semi-structured documents. We improve on existing models and achieve a F1-score of 0.912 and scale our methods to the entire VA corpus.

Rush III, Everett↗
DOE OSTI2020

Giant anisotropic magnetoresistance in oxygen-vacancy-ordered epitaxial La 0.5 Sr 0.5 CoO 3–δ films

Recent advances in complex oxide heterostructures have realized extraordinary control over oxygen vacancies ($V_\text{O}$), including strain-tuned $V_\text{O}$ order, and electric-field-controlled transformations between perovskite and $V_\text{O}$-ordered structures. Perovskite cobaltites such as La 1– x Sr x CoO 3–δ provide a prime example, recent work demonstrating that strain engineering of $V_\text{O}$ ordering induces large (~10 7 erg/cm 3 ) perpendicular magnetic anisotropy. Here we show that $V_\text{O}$-ordered epitaxial La 0.5 Sr 0.5 CoO 3–δ films exhibit not only strong magnetic anisotropy, but also a giant form of anisotropic magnetoresistance (AMR). This has magnetic field, temperature, and angular dependencies in quantitative accord with conventional AMR, but with AMR ratios up to an extraordinary 40.3%, 20 times enhanced over bulk cobaltites, and ~10–100 times larger than typical transition metals. This giant AMR has no strong dependence on heteroepitaxial strain (between –2.1% and +1.8%) or thickness, and is instead ascribed to symmetry lowering associated with $V_\text{O}$ ordering. The AMR ratios thus obtained in this work are among the largest reported in the over 160-year history of this phenomenon, despite the absence of heavy elements.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Towards Content Authenticity: Multimodal Fake News Detection and AI-Generated Text Identification

In today’s digital world, the spread of fake news and the rise of AI-generated text have become major threats to content authenticity and public trust. This thesis addresses both challenges through two complementary research directions: detecting fake news using multimodal features, and identifying AI-generated text using semantic and structural reasoning. The first part of the work focuses on fake news detection by introducing a novel model that combines text and image features through a unique rotational attention mechanism. Unlike traditional attention methods, this approach rotates the roles of query, key, and value across modalities to capture deeper interactions. Additionally, the model incorporates external domain information by linking news posts to top-ranked websites from Google search results, which helps assess the credibility of content based on its broader web context. This results in a more reliable and accurate fake news detection system that outperforms existing state-of-the-art methods. The second part presents SGG-ATD, a new framework for detecting AI-generated text. It uses masked language modeling to measure sentence coherence, followed by constructing a graph where keywords—both original and predicted—are connected based on semantic and contextual similarity. A Graph Convolutional Network (GCN) is then used to learn structural relationships within the text for final classification. Experimental results demonstrate that SGG-ATD achieves high F1-scores and consistently outperforms strong baselines. This method contributes to robust AI text detection, supporting accountability and resilience against AI-driven misinformation.

Gupta, Nidhi↗
DOE OSTI2022

Path integral Monte Carlo approach to the structural properties and collective excitations of liquid $$^3{\text {He}}$$ without fixed nodes

Abstract Due to its nature as a strongly correlated quantum liquid, ultracold helium is characterized by the nontrivial interplay of different physical effects. Bosonic $$^4{\text {He}}$$ 4 He exhibits superfluidity and Bose-Einstein condensation. Its physical properties have been accurately determined on the basis of ab initio path integral Monte Carlo (PIMC) simulations. In contrast, the corresponding theoretical description of fermionic $$^3{\text {He}}$$ 3 He is severely hampered by the notorious fermion sign problem, and previous PIMC results have been derived by introducing the uncontrolled fixed-node approximation. In this work, we present extensive new PIMC simulations of normal liquid $$^3{\text {He}}$$ 3 He without any nodal constraints. This allows us to to unambiguously quantify the impact of Fermi statistics and to study the effects of temperature on different physical properties like the static structure factor $$S({\mathbf {q}})$$ S ( q ) , the momentum distribution $$n({\mathbf {q}})$$ n ( q ) , and the static density response function $$\chi ({\mathbf {q}})$$ χ ( q ) . In addition, the dynamic structure factor $$S({\mathbf {q}},\omega )$$ S ( q , ω ) is rigorously reconstructed from imaginary-time PIMC data. From simulations of $$^3{\text {He}}$$ 3 He , we derived the familiar phonon–maxon–roton dispersion function that is well-known for $$^4{\text {He}}$$ 4 He and has been reported previously for two-dimensional $$^3{\text {He}}$$ 3 He films (Nature 483:576–579 (2012)). The comparison of our new results for both $$S({\mathbf {q}})$$ S ( q ) and $$S({\mathbf {q}},\omega )$$ S ( q , ω ) with neutron scattering measurements reveals an excellent agreement between theory and experiment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Survey of Current State of the Art Entity-Relation Extraction Tools

In the realm of information extraction from text data, there exists a number of tools with the capability of extracting entities and their relationships with one another. Such information has endless uses in a number of domains, however, the solutions to getting this information is still in early stages and has room for improvement. The topic has been explored from a research perspective by academic institutions, as well as formal tool creation from corporations. Overall, entity extraction is common among these tools, though with varying degrees of accuracy, while relationship extraction is more difficult to find. In this report, we take a look at the top state of the art tools currently available and identify their capabilities, strengths, and weaknesses. We explore the common algorithms in the successful approaches and their ability to efficiently handle both structured and unstructured text data. Finally, we highlight some of the common issues among these tools and summarize the current status in the area of entity-relationship extraction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Addendum: Fermiology and Electron Dynamics of Trilayer Nickelate La 4 Ni 3 O 10 .

The DFT calculations in the main text use crystal structure information determined by x-ray diffraction (XRD) measurements of La 4 Ni 3 O 10 . The room temperature XRD measurements on all samples were consistent with mixed phases of orthorhombic and monoclinic symmetry. However, the in-plane lattice constants of these two phases differ by <1%, leading to minimal influence on band structure calculations. Furthermore, any impact on the experimental Fermi surface attributable to such deviations would be obscured by broadening of the bands measured by ARPES. Therefore, the DFT calculations adopt the higher symmetry orthorhombic crystal structure with space group Cmca and lattice constants a = 5.417 Å, c = 5.468 Å, b = 27.962 Å, with some additional relaxation of the internal coordinates. The detailed atomic coordinates are presented in Supplementary Table 1.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Vendor 0

Software uses a Format-preserving Feistel based encryption library to encrypt and decrypt text fields in structured files such currently JSON, TSV.

Wellman, LawrenceR.↗
NASA NTRS1988

Lisp Object State Saver (LOSS): A facility used to save partial schedules of the Hubble Space Telescope

Current research in the area of long term scheduling of the Hubble Space Telescope is being done using Common Lisp and Flavors on Lisp Machines. The planning tools manipulate memory-resident data structures which represent the many entities and relationships that represent planning states. The Lisp Object State Saver (LOSS), a general purpose utility, was constructed which allows one to take a snapshot of memory by storing a representation of the structures in a text file. This text file can later be loaded thus restoring the pre-existing and logically equivalent planning state. A LOSS template must be created for each datatype to be stored and a simple grammar governs the creation of such templates.

Sponsler, Jeffrey L.↗
DOE OSTI2020

Gaussian null coordinates, near-horizon geometry and conserved charges on the horizon of extremal nondilatonic black p-branes

In this paper, we examine the emergence of conserved charges on the horizon of a particular class of extremal nondilatonic black [Formula: see text]-branes (which reduce to extremal dilatonic black holes in [Formula: see text] dimensions upon toroidal compactification) in the presence of a probe massless scalar field in the bulk. This result is achieved by writing the black [Formula: see text]-brane geometry in a Gaussian null coordinate system which allows us to get a nonsingular near-horizon geometry description. We find that the near-horizon geometry is [Formula: see text] and that the [Formula: see text] section has an internal structure which can be seen as a warped product of [Formula: see text] in Gaussian null coordinates. We show that the bulk scalar field satisfying the field equations is expanded in terms of nonnormalizable and normalizable modes, which for certain suitable quantization conditions are well-behaved at the boundary of [Formula: see text] space. Furthermore, we show that picking the normalizable modes results in the existence of conserved quantities on the horizon. We discuss the impact of these conserved quantities in the late-time regime.

Astronomy & Astrophysics↗
NASA NTRS1991

MacPASCO - A Macintosh-based, interactive graphic preprocessor for structural analysis and sizing

MacPASCO, an interactive, graphic preprocessor for panel design is described. MacPASCO creates input for PASCO, an existing computer code for structural analysis and optimization of longitudinal stiffened composite panels. By using a graphical user interface, MacPASCO simplifies the specification of panel geometry and reduces user input errors, thus making the modeling and analysis of panel designs more efficient. The user draws the initial structural geometry on the computer screen, then uses a combination of graphic and text inputs to: refine the structural geometry, specify information required for analysis such as panel load conditions, and define design variables and constraints for minimum-mass optimization. Composite panel design is an ideal application because the graphical user interface can: serve as a visual aid, eliminate the tedious aspects of text-based input, and eliminate many sources of input errors.

Lucas, S. H.↗
NASA NTRS1993

NASA's online machine aided indexing system

This report describes the NASA Lexical Dictionary, a machine aided indexing system used online at the National Aeronautics and Space Administration's Center for Aerospace Information (CASI). This system is comprised of a text processor that is based on the computational, non-syntactic analysis of input text, and an extensive 'knowledge base' that serves to recognize and translate text-extracted concepts. The structure and function of the various NLD system components are described in detail. Methods used for the development of the knowledge base are discussed. Particular attention is given to a statistically-based text analysis program that provides the knowledge base developer with a list of concept-specific phrases extracted from large textual corpora. Production and quality benefits resulting from the integration of machine aided indexing at CASI are discussed along with a number of secondary applications of NLD-derived systems including on-line spell checking and machine aided lexicography.

Silvester, June P.↗
DOE OSTI2019

Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature

The number of published materials science articles has increased manyfold over the past few decades. Now, a major bottleneck in the materials discovery pipeline arises in connecting new results with the previously established literature. A potential solution to this problem is to map the unstructured raw text of published articles onto structured database entries that allow for programmatic querying. To this end, we apply text mining with named entity recognition (NER) for large-scale information extraction from the published materials science literature. The NER model is trained to extract summary-level information from materials science documents, including inorganic material mentions, sample descriptors, phase labels, material properties and applications, as well as any synthesis and characterization methods used. Our classifier achieves an accuracy (f 1 ) of 87%, and is applied to information extraction from 3.27 million materials science abstracts. We extract more than 80 million materials-science-related named entities, and the content of each abstract is represented as a database entry in a structured format. We demonstrate that simple database queries can be used to answer complex "meta-questions" of the published literature that would have previously required laborious, manual literature searches to answer. Finally, all of our data and functionality has been made freely available on our Github ( https://github.com/materialsintelligence/matscholar ) and website ( http://matscholar.com ), and we expect these results to accelerate the pace of future materials science discovery.

36 MATERIALS SCIENCE↗
NASA NTRS1992

User's manual for MacPASCO

A user's manual is presented for MacPASCO, which is an interactive, graphic, preprocessor for panel design. MacPASCO creates input for PASCO, an existing computer code for structural analysis and sizing of longitudinally stiffened composite panels. MacPASCO provides a graphical user interface which simplifies the specification of panel geometry and reduces user input errors. The user draws the initial structural geometry and reduces user input errors. The user draws the initial structural geometry on the computer screen, then uses a combination of graphic and text inputs to: refine the structural geometry; specify information required for analysis such as panel load and boundary conditions; and define design variables and constraints for minimum mass optimization. Only the use of MacPASCO is described, since the use of PASCO has been documented elsewhere.

Lucas, S. H.↗
DOE OSTI2021

Observation of new excited ${B} ^0_{s}$ states

A structure is observed in the ${B} ^{\pm }{K} ^{\mp }$ mass spectrum in a sample of proton–proton collisions at centre-of-mass energies of 7, 8, and 13 TeV, collected with the LHCb detector and corresponding to a total integrated luminosity of 9$\,\text {fb} ^{-1}$. The structure is interpreted as the result of overlapping excited ${B} ^0_{s}$ states. With high significance, a two-peak hypothesis provides a better description of the data than a single resonance. Under this hypothesis the masses and widths of the two states, assuming they decay directly to ${B} ^{\pm }{K} ^{\mp }$, are determined to be $\begin{aligned} m_1&= 6063.5 \pm 1.2 \text { (stat)} \pm 0.8\text { (syst)}\,\text {Me}\text {V}, \\ \Gamma _1&= 26 \pm 4 \text { (stat)} \pm 4\text { (syst)}\,\text {Me}\text {V}, \\ m_2&= 6114 \pm 3 \text { (stat)} \pm 5\text { (syst)}\,\text {Me}\text {V}, \\ \Gamma _2&= 66 \pm 18 \text { (stat)} \pm 21\text { (syst)}\,\text {Me}\text {V}. \end{aligned}$ Alternative values assuming a decay through ${B} ^{*\pm }{K} ^{\mp }$, with a missing photon from the $B^{*\pm } \rightarrow B^{\pm }\gamma $ decay, which are shifted by approximately 45 $\,\text {Me}$V, are also determined. The possibility of a single state decaying in both channels is also considered. The ratio of the total production cross-section times branching fraction of the new states relative to the previously observed $B_{s2}^{*0}$ state is determined to be $0.87 \pm 0.15 \text { (stat)} \pm 0.19 \text { (syst)}$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Process Engineering to Increase the Layered Phase Concentration in the Immediate Products of Flame Spray Pyrolysis

Flame-spray-pyrolysis (FSP) is a robust and scalable process to synthesize particles at the commodity-scale. FSP has been used to produce the precursor powders which were converted to the layered structure $(R\bar{3m} \text { phase})$ by a postannealing step in making nickel-rich cathode materials (NCMs). Theoretically, the high flame temperature (normally >1500 K) in FSP can provide adequate energy for the phase conversion from rock-salt to layered structures and potentially enables one-step synthesis. However, the high flame temperature is a critical issue to cause lithium loss and structural degradation, preventing the formation of the layered phase. Here, guided by the gaseous nucleation theory, we implemented several FSP processes with different solution recipes. The layered phase concentration in the as-burned products can be increased with the solution enthalpies. By adding a rapid quench step to suppress the lithium loss and phase degradation, the layered phase can be further increased. This work contributes new ideas to innovating process regarding the process efficiency and throughput of manufacturing cathode materials at a large scale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

Abstract We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine‐tuned large language models (LLMs) trained on a human‐readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive‐unlabeled learning model on a text‐embedding representation of the structure. An LLM‐based workflow can then be used to generate human‐readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non‐synthesizable hypothetical structures to make them more feasible for materials design.

Kim, Seongmin [Department of Chemical and Biologic↗