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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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NASA NTRS2002

ISAIA: Interoperable Systems for Archival Information Access

The ISAIA project was originally proposed in 1999 as a successor to the informal AstroBrowse project. AstroBrowse, which provided a data location service for astronomical archives and catalogs, was a first step toward data system integration and interoperability. The goals of ISAIA were ambitious: '...To develop an interdisciplinary data location and integration service for space science. Building upon existing data services and communications protocols, this service will allow users to transparently query hundreds or thousands of WWW-based resources (catalogs, data, computational resources, bibliographic references, etc.) from a single interface. The service will collect responses from various resources and integrate them in a seamless fashion for display and manipulation by the user.' Funding was approved only for a one-year pilot study, a decision that in retrospect was wise given the rapid changes in information technology in the past few years and the emergence of the Virtual Observatory initiatives in the US and worldwide. Indeed, the ISAIA pilot study was influential in shaping the science goals, system design, metadata standards, and technology choices for the virtual observatory. The ISAIA pilot project also helped to cement working relationships among the NASA data centers, US ground-based observatories, and international data centers. The ISAIA project was formed as a collaborative effort between thirteen institutions that provided data to astronomers, space physicists, and planetary scientists. Among the fruits we ultimately hoped would come from this project would be a central site on the Web that any space scientist could use to efficiently locate existing data relevant to a particular scientific question. Furthermore, we hoped that the needed technology would be general enough to allow smaller, more-focused community within space science could use the same technologies and standards to provide more specialized services. A major challenge to searching for data across a broad community is that information that describe some data products are either not relevant to other data or not applicable in the same way. Some previous metadata standard development efforts (e.g., in the earth science and library communities) have produced standards that are very large and difficult to support. To address this problem, we studied how a standard may be divided into separable pieces. Data providers that wish to participate in interoperable searches can support only those parts of the standard that are relevant to them. We prototyped a top-level metadata standard that was small and applicable to all space science data.

Hanisch, Robert J.↗
DOE OSTI2025

MS25: Materials Science-Focused Benchmark Data Set for Machine Learning Interatomic Potentials

Here, we present MS25, a benchmark data set for evaluating machine learning interatomic potentials (MLIPs) across diverse materials-relevant systems including MgO surfaces, liquid water, zeolites, a catalytic Pt surface reaction, high-entropy alloys (HEAs), and disordered Zr-oxides. Five MLIP architectures (MACE, NequIP, Allegro, MTP, and Torch-ANI) are trained and tested, focusing not only on traditional metrics (energies, forces, and stresses) but also explicitly validating derived physical observables such as lattice constants, volumes, and reaction barriers. We find that most models reach comparable accuracy on standard error metrics across the simple systems, although equivariant MLIPs offer 1.5–2× improvements over nonequivariant MLIPs in energy and force error for structurally complex or compositionally disordered environments such as HEAs and Zr–O systems. Our analysis highlights that low errors in energy and force predictions do not guarantee reliable observables, emphasizing the necessity of explicit validation. We demonstrate limitations in cross-framework transferability, as models trained on one zeolite framework (CHA) fail to reliably generalize to predictions of structurally distinct frameworks (e.g., MFI). Size-extensive tests show some dependence on system size for MgO, resulting from forced periodicity. The HEA and Zr–O data sets are identified as challenging tests for future benchmarks and MLIP model architecture developments as they show significant differentiation in error between MLIP architectures and are still relatively difficult at 1000 training images. Moving forward, we recommend that benchmarking efforts shift their focus from marginal accuracy improvements in energy and force errors toward identifying and understanding model failure modes, rigorously assessing transferability, and evaluating how their errors affect observable predictions. For researchers looking to choose an MLIP architecture, we suggest selecting equivariant MLIP architectures if the complexity of the system is a challenge. For simple materials problems, auxiliary features such as integration with molecular dynamics engines, trade-offs between computational data set generation cost vs MLIP inference speed, and framework integration may play a more important decision factor than small differences in error metrics that are unlikely to matter for production-level research.

chemical structure↗
NASA NTRS2016

A Comparison of Van Allen Belt Radiation Environment Modeling Programs: AE8/AP8 Legacy, AE9/AP9, and SPENVIS

In the space surrounding Earth there exists an active radiation environment consisting mostly of electrons and protons that have been trapped by Earths magnetic field. This radiation, also known as the Van Allen Belts, has the potential to damage man-made satellites in orbit; thus, proper precautions must be taken to shield NASA assets from this phenomenon. Data on the Van Allen Belts has been collected continuously by a multitude of space-based instruments since the beginning of space exploration. Subsequently, using theory to fill in the gaps in the collected data, computer models have been developed that take in the orbital information of a hypothetical mission and output the expected particle fluence and flux for that orbit. However, as new versions of the modeling system are released, users are left wondering how the new version differs from the old. Therefore, we performed a comparison of three different editions of the modeling system: AE8/AP8 (legacy), which is included in the model 9 graphical user interface as an option for ones calculations, AE9/AP9, and the Space Environment Information System (SPENVIS), which is an online-based form of AE8/AP8 developed by NASA and the European Space Agency that changed the code to allow the program to extrapolate data to predict fluence and flux at higher energies. Although this evaluation is still ongoing, it is predicted that the model 8 (legacy) and SPENVIS version will have identical outputs with the exception of the extended energy levels from SPENVIS, while model 9 will provide different fluences than model 8 based on additional magnetic field descriptions and on-orbit data.

Modeling Programs↗
NASA NTRS1985

An algorithm for computing the number of distinct spectral vectors in thematic mapper data

A computationally efficient method was developed to compute the number of distinct spectral vectors and their frequency of occurrence in Landsat-4 Thematic Mapper (TM) data. The algorithm first partitions the image into spectrally disjoint subsets and then computes the frequency distribution of distinct spectral vectors within each subset from a multidimensional histogram. The overall frequency distribution is tabulated by accumulating the results from each subset. The number of distinct spectral vectors could be used as a measure of potential storage compaction of alternate data representations for data compression, or as a measure of information content in the comparison of spectral band combinations and/or spatial resolutions for an image. Results from processing three 512 x 512 pixel Landsat-4 TM images and one Landsat-4 Multispectral Scanner (MSS) image are presented as examples. An algorithm for computing the frequency distribution of distinct spectral vectors in MSS data is given in the Appendix.

Wharton, S. W.↗
DOE OSTI2017

In vitro cardiotoxicity assessment of environmental chemicals using an organotypic human induced pluripotent stem cell-derived model

An important target area for addressing data gaps through in vitro screening is the detection of potential cardiotoxicants. Despite the fact that current conservative estimates relate at least 23% of all cardiovascular disease cases to environmental exposures, the identities of the causative agents remain largely uncharacterized. Here, we evaluate the feasibility of a combinatorial in vitro/in silico screening approach for functional and mechanistic cardiotoxicity profiling of environmental hazards using a library of 69 representative environmental chemicals and drugs. Human induced pluripotent stem cell-derived cardiomyocytes were exposed in concentration-response for 30 min or 24 h and effects on cardiomyocyte beating and cellular and mitochondrial toxicity were assessed by kinetic measurements of intracellular Ca{sup 2+} flux and high-content imaging using the nuclear dye Hoechst 33342, the cell viability marker Calcein AM, and the mitochondrial depolarization probe JC-10. More than half of the tested chemicals exhibited effects on cardiomyocyte beating after 30 min of exposure. In contrast, after 24 h, effects on cell beating without concomitant cytotoxicity were observed in about one third of the compounds. Concentration-response data for in vitro bioactivity phenotypes visualized using the Toxicological Prioritization Index (ToxPi) showed chemical class-specific clustering of environmental chemicals, including pesticides, flame retardants, and polycyclic aromatic hydrocarbons. For environmental chemicals with human exposure predictions, the activity-to-exposure ratios between modeled blood concentrations and in vitro bioactivity were between one and five orders of magnitude. These findings not only demonstrate that some ubiquitous environmental pollutants might have the potential at high exposure levels to alter cardiomyocyte function, but also indicate similarities in the mechanism of these effects both within and among chemicals and classes. - Highlights: • The cardiotoxic potential of a broad range of environmental chemicals was evaluated. • Human iPSC cardiomyocytes were used for multiparametric in vitro profiling. • Dose-response relationships were established for functional and cytotoxicity assays. • Computational data integration provided chemical-specific bioactivity profiles. • An assessment of the profiles revealed chemical class-specific similarities.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

End-to-end optimization for battery materials and molecules by combining graph neural networks and reinforcement learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to design new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗
DOE OSTI2022

End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to the design of new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗
DOE OSTI2026

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.

Aubourg, Eric [APC, Paris] (ORCID:000000025592023X↗
NASA NTRS2014

TBS (Trabecular Bone Score) Expands Understanding of Spaceflight Effects on the Lumbar Spine of Long-Duration Astronauts

Bone loss due to long‐duration spaceflight has been characterized by both DXA and QCT serial scans. It is unclear if these spaceflight‐induced changes in bone mineral density (BMD) and structure result in increased fracture incidence. NASA astronauts currently fly 5 to 6‐month missions on the International Space Station (ISS) and at least one 12‐month mission is planned. While NASA has measured areal BMD (by DXA) and volumetric BMD (by QCT) and has estimated hip strength (by finite element models of QCT data, no method has yet been used to examine bone micro‐architecture from lumbar spine (LS). DXA scans are routinely performed pre‐ and postflight on all ISS astronauts to follow BMD changes associated with spaceflight. Trabecular Bone Score (TBS) is a relatively new method that measures grey‐scale‐level texture information extracted from LS DXA images and correlates with 3D parameters of bone micro‐architecture. We evaluated the ability of LS TBS to discriminate changes in astronauts who have flown on ISS missions and to determine if TBS can provide additional information compared to DXA. Methods: Lumbar Spine (L1‐4) DXA scans from 51 astronauts (mean age, 47 +/- 4 yrs) were divided into 3 groups based on the exercise regimens performed onboard the ISS. "Pre‐ARED" (exercise using a load‐limited resistive exercise device, <300 lb), "ARED" (exercise with a high‐load resistive exercise device, up to 600 lb) and "Bisphos+ARED" group (ARED exercise and a 70‐mg alendronate tablet once a week before and during flight, starting 17 days before launch). DXA scans were performed and analyzed on a Hologic Discovery W using the same technician for the pre‐ and post‐flight scans. LSC for the LS in our laboratory is 0.025 g/sq. cm. TBS was performed at the Mercy Hospital, Cincinnati, Ohio on a similar Hologic computer. Data were analyzed using a paired, 2‐tailed Student's t‐test for the difference between pre‐ and postflight means. Percent change and % change per month are noted. Interpretation: Our data suggest that: TBS and DXA both detected significant decrements in the LS in these pre‐ ARED astronauts, not unexpected given the insufficient loads provided by this early exercise device. TBS did not detect significant changes in the ARED or Bisphos+ARED groups while DXA did detect significant changes in the ARED astronauts. These findings suggest that DXA and TBS are detecting independent effects of bone loss interventions tested in ISS astronauts in space, which may be due to distinct effects of interventions on mineral content of separate cortical vs. trabecular bone. Conclusion: TBS, in conjunction with DXA BMD, may provide additional insight into the nature of changes (or lack thereof) in the microstructure of trabecular bone and the areal BMD of vertebral bodies.

Smith, Scott A.↗
DOE OSTI2025

Vision Foundation Models in Remote Sensing: A survey

Artificial intelligence (AI) technologies have profoundly transformed the field of remote sensing (RS), revolutionizing data collection, processing, and analysis. Traditionally reliant on manual interpretation and task-specific models, RS research has been significantly enhanced by the advent of foundation models (FMs)—large-scale pretrained AI models capable of performing a wide array of tasks with unprecedented accuracy and efficiency. This article provides a comprehensive survey of FMs in the RS domain. We categorize these models based on their architectures, pretraining datasets, and methodologies. Through detailed performance comparisons, we highlight emerging trends and the significant advancements achieved by those FMs. Additionally, we discuss technical challenges, practical implications, and future research directions, addressing the need for high-quality data, computational resources, and improved model generalization. Our research also finds that pretraining methods, particularly self-supervised learning (SSL) techniques like contrastive learning (CL) and masked autoencoders (MAEs), remarkably enhance the performance and robustness of FMs. This survey aims to serve as a resource for researchers and practitioners by providing a panorama of advances and promising pathways for the continued development and application of FMs in RS.

data models↗
NASA NTRS1995

Energization and transport of ions of ionospheric origin in the terrestrial magnetosphere

The work of this grant has been predominantly focused on ion outflows from two data sets: Prognoz 7 and Dynamics Explorer. The Prognoz analysis studied ion densities, temperatures, and flow velocities in the magnetotail. The work performed under this contract consisted of developing a program to load the raw data, computing the background subtraction of a strong sun pulse, and using the net count to calculate the low order moments of the distribution function. The study confirms the results of ISEE with regard to the supply of plasma from the cusp as a major source of plasmasheet plasma and goes beyond this to discuss the use of ion velocities as a way to examine the motions of the magnetotail. The abstract of the work to be reported is included as an appendix. The work on the DE/Retarding Ion Mass Spectrometer is separated into two categories: (1) classification of low-energy ion flows from high-latitudes, and (2) studies of the polar wind. Major publications resulting from this work are also included as an appendix to this report. The polar wind is in a category by itself as a result of the thermal escape of hydrogen and helium because of ambipolar diffusion through the heavier, oxygen-dominated topside ionosphere. The analysis of the polar wind reports the flux variability as a function of season, magnetic activity, etc. Much effort has been expended under this grant to complete a follow on study of the thermal structure of the polar wind. Extensive display tools and analysis software have been developed and used in an attempt to carry out this thermal analysis. The present work uses a constrained fit scheme that combines the ion densities and flow velocities derived from Chandler et al. and a spacecraft potential derived from an empirical relation to the total ion density to determine the remaining fit parameter, the ion temperature, via a least squares fit to the RIMS data.

Waite, J. Hunter JR.↗
DOE OSTI2019

Rigorous Data Fusion for Computationally Expensive Simulations

This manuscript comprises the final report for the 1-year, FY19 LDRD project "Rigorous Data Fusion for Computationally Expensive Simulations," wherein an alternative approach to Bayesian calibration was developed based a new sampling technique called VoroSpokes. Vorospokes is a novel quadrature and sampling framework defined with respect to Voronoi tessellations of bounded domains in $R^d$ developed within this project. In this work, we first establish local quadrature and sampling results on convex polytopes using randomly directed rays, or spokes, to approximate the quantities of interest for a specified target function. A theoretical justification for both procedures is provided along with empirical results demonstrating the unbiased convergence in the resulting estimates/samples. The local quadrature and sampling procedures are then extended to global procedures defined on more general domains by applying the local results to the cells of a Voronoi tessellation covering the domain in consideration. We then demonstrate how the proposed global sampling procedure can be used to define a natural framework for adaptively constructing Voronoi Piecewise Surrogate (VPS) approximations based on local error estimates. Finally, we show that the adaptive VPS procedure can be used to form a surrogate model approximation to a specified, potentially unnormalized, density function, and that the global sampling procedure can be used to efficiently draw independent samples from the surrogate density in parallel. The performance of the resulting VoroSpokes sampling framework is assessed on a collection of Bayesian inference problems and is shown to provide highly accurate posterior predictions which align with the results obtained using traditional methods such as Gibbs sampling and random-walk Markov Chain Monte Carlo (MCMC). Importantly, the proposed framework provides a foundation for performing Bayesian inference tasks which is entirely independent from the theory of Markov chains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Formal Nickelate(−I) Complexes Supported by Group 13 Ions

Abstract Formal nickelate(−I) complexes bearing Group 13 metalloligands (M=Al and Ga) were isolated. These 17 e − complexes were synthesized by one‐electron reduction of the corresponding Ni 0 →M III precursors, and were investigated by single‐crystal X‐ray diffraction, EPR spectroscopy, and quantum chemical calculations. Collectively, the experimental and computational data support: 1) the strengthening of the Ni−M bond upon one‐electron reduction, and 2) the delocalization of the unpaired spin across the Ni and M atoms. An intriguing electronic configuration is revealed where three valence electrons occupy two σ‐type bonding interactions: Ni(3d ) 2 →M and σ‐(Ni−M) 1 . The latter is an unusual Ni−M σ‐bonding molecular orbital that comprises primarily the Ni 4p z and M np z /ns atomic orbitals.

Vollmer, Matthew V.↗
DOE OSTI2019

Directed Gas‐Phase Synthesis of Triafulvene under Single‐Collision Conditions

Abstract The triafulvene molecule (c‐C 4 H 4 )—the simplest representative of the fulvene family—has been synthesized for the first time in the gas phase through the reaction of the methylidyne radical (CH) with methylacetylene (CH 3 CCH) and allene (H 2 CCCH 2 ) under single‐collision conditions. The experimental and computational data suggest triafulvene is formed by the barrierless cycloaddition of the methylidyne radical to the π‐electron density of either C 3 H 4 isomer followed by unimolecular decomposition through elimination of atomic hydrogen from the CH 3 or CH 2 groups of the reactants. The dipole moment of triafulvene of 1.90 D suggests that this molecule could represent a critical tracer of microwave‐inactive allene in cold molecular clouds, thus defining constraints on the largely elusive hydrocarbon chemistry in low‐temperature interstellar environments, such as that of the Taurus Molecular Cloud 1 (TMC‐1).

Thomas, Aaron M.↗
DOE OSTI2019

Directed Gas-Phase Synthesis of Triafulvene under Single-Collision Conditions

We report the triafulvene molecule (c-C 4 H 4 )—the simplest representative of the fulvene family—has been synthesized for the first time in the gas phase through the reaction of the methylidyne radical (CH) with methylacetylene (CH 3 CCH) and allene (H 2 CCCH 2 ) under single-collision conditions. The experimental and computational data suggest triafulvene is formed by the barrierless cycloaddition of the methylidyne radical to the π-electron density of either C 3 H 4 isomer followed by unimolecular decomposition through elimination of atomic hydrogen from the CH 3 or CH 2 groups of the reactants. The dipole moment of triafulvene of 1.90 D suggests that this molecule could represent a critical tracer of microwave-inactive allene in cold molecular clouds, thus defining constraints on the largely elusive hydrocarbon chemistry in low-temperature interstellar environments, such as that of the Taurus Molecular Cloud 1 (TMC-1).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗