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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1960

An Exploratory Investigation of Some Factors Influencing the Room-Temperature Ductility of Tungsten

Specimens having an initial diameter of 0.125 inch were cut from commercially pure sintered and swaged tungsten rods. The effect of various surface treatments on the ductility of tungsten was evaluated by the use of a bend test. The criterion used to determine the relative ductilities was the final bend angle of the specimens. Results showed that the bend ductility of the tungsten specimens increased with increasing depths of the surface removed by electropolishing. When specimens electropolished to a depth sufficient to produce a marked increase in the ductility over that of the as-received surface condition were subsequently scratched with emery paper, the ductility achieved by electropolishing was greatly reduced. Removal of similar depths of the surface by grinding as were removed by electropolishing did not produce any appreciable increase in the ductility of the specimens. The ductility of specimens tested in the as-received surface condition and those having an electropolished surface exhibited a great sensitivity to deflection rate, the electropolished specimens being more ductile over the range of deflection rates tested. These exploratory results illustrated qualitatively the importance of surface condition on the room-temperature ductility of tungsten and indicated the importance of relatively fine scratches on the ductility of tungsten.

Stephens, Joseph R.↗
NASA NTRS1996

Protective, Abrasion-Resistant Coatings With Tailorable Properties

Because of their light weight and impact resistance, transparent plastic structures are becoming increasingly desirable for use not only on aircraft but also in terrestrial applications such as automotive windshields and ophthalmic lenses. However, plastics are typically soft and scratch readily, reducing their transparency with use. At the NASA Lewis Research Center, reactively deposited aluminum oxide coatings as thin as 12,000 angstroms have been demonstrated to provide improved resistance to most scratches encountered during normal use. The properties of the coating can be adjusted to tailor the surface to meet other needs, such as water shedding. These adjustments can be made during the deposition process so that multiple manufacturing steps are eliminated.

Source record↗
NASA NTRS2010

Computer Software Configuration Item-Specific Flight Software Image Transfer Script Generator

A K-shell UNIX script enables the International Space Station (ISS) Flight Control Team (FCT) operators in NASA s Mission Control Center (MCC) in Houston to transfer an entire or partial computer software configuration item (CSCI) from a flight software compact disk (CD) to the onboard Portable Computer System (PCS). The tool is designed to read the content stored on a flight software CD and generate individual CSCI transfer scripts that are capable of transferring the flight software content in a given subdirectory on the CD to the scratch directory on the PCS. The flight control team can then transfer the flight software from the PCS scratch directory to the Electronically Erasable Programmable Read Only Memory (EEPROM) of an ISS Multiplexer/ Demultiplexer (MDM) via the Indirect File Transfer capability. The individual CSCI scripts and the CSCI Specific Flight Software Image Transfer Script Generator (CFITSG), when executed a second time, will remove all components from their original execution. The tool will identify errors in the transfer process and create logs of the transferred software for the purposes of configuration management.

Bolen, Kenny↗
NASA NTRS2010

Three-Body Abrasion Testing Using Lunar Dust Simulants to Evaluate Surface System Materials

Numerous unexpected operational issues relating to the abrasive nature of lunar dust, such as scratched visors and spacesuit pressure seal leaks, were encountered during the Apollo missions. To avoid reoccurrence of these unexpected detrimental equipment problems on future missions to the Moon, a series of two- and three-body abrasion tests were developed and conducted in order to begin rigorously characterizing the effect of lunar dust abrasiveness on candidate surface system materials. Two-body scratch tests were initially performed to examine fundamental interactions of a single particle on a flat surface. These simple and robust tests were used to establish standardized measurement techniques for quantifying controlled volumetric wear. Subsequent efforts described in the paper involved three-body abrasion testing designed to be more representative of actual lunar interactions. For these tests, a new tribotester was developed to expose samples to a variety of industrial abrasives and lunar simulants. The work discussed in this paper describes the three-body hardware setup consisting of a rotating rubber wheel that applies a load on a specimen as a loose abrasive is fed into the system. The test methodology is based on ASTM International (ASTM) B611, except it does not mix water with the abrasive. All tests were run under identical conditions. Abraded material specimens included poly(methyl methacrylate) (PMMA), hardened 1045 steel, 6061-T6 aluminum (Al) and 1018 steel. Abrasives included lunar mare simulant JSC- 1A-F (nominal size distribution), sieved JSC-1A-F (<25 m particle diameter), lunar highland simulant NU-LHT-2M, alumina (average diameter of 50 m used per ASTM G76), and silica (50/70 mesh used per ASTM G65). The measured mass loss from each specimen was converted using standard densities to determine total wear volume in cm3. Abrasion was dominated by the alumina and the simulants were only similar to the silica (i.e., sand) on the softer materials of aluminum and PMMA. The nominal JSC- 1A-F consistently showed more abrasion wear than the sieved version of the simulant. The lunar dust displayed abrasivity to all of the test materials, which are likely to be used in lunar landing equipment. Based on this test experience and pilot results obtained, recommendations are made for systematic abrasion testing of candidate materials intended for use in lunar exploration systems and in other environments with similar dust challenges.

Kobrick, Ryan L.↗
NASA NTRS2017

Corrosion on Mars: An Investigation of Corrosion Mechanisms Under Relevant Simulated Martian Environments

This one-year project was selected by NASA's Science Innovation Fund in FY17 to address Corrosion on Mars which is a problem that has not been addressed before. Corrosion resistance is one of the most important properties in selecting materials for landed spacecraft and structures that will support surface operations for the human exploration of Mars. Currently, the selection of materials is done by assuming that the corrosion behavior of a material on Mars will be the same as that on Earth. This is understandable given that there is no data regarding the corrosion resistance of materials in the Mars environment. However, given that corrosion is defined as the degradation of a metal that results from its chemical interaction with the environment, it cannot be assumed that corrosion is going to be the same in both environments since they are significantly different. The goal of this research is to develop a systematic approach to understand corrosion of spacecraft materials on Mars by conducting a literature search of available data, relevant to corrosion in the Mars environment, and by performing preliminary laboratory experiments under relevant simulated Martian conditions. This project was motivated by the newly found evidence for the presence of transient liquid brines on Mars that coincided with the suggestion, by a team of researchers, that some of the structural degradation observed on Curiosity's wheels may be caused by corrosive interactions with the brines, while the most significant damage was attributed to rock scratching. An extensive literature search on data relevant to Mars corrosion confirmed the need for further investigation of the interaction between materials used for spacecraft and structures designed to support long-term surface operations on Mars. Simple preliminary experiments, designed to look at the interaction between an aerospace aluminum alloy (AA7075-T73) and the gases present in the Mars atmosphere, at 20degC and a pressure of 700 Pa, showed that there is an interaction between the small amount of oxygen present in the Mars gas and the alloy when there is a scratch that removes the protective aluminum oxide film. Further studies are needed to consider many other important components of the Mars environment that can affect this interaction such as: the effect of oxidants, the effect of radiation on their oxidizing properties and the possible catalytic effects of the clays present in the Martian regolith. The results of this one-year project provide strong justification for further investigation of the corrosion mechanism of materials relevant to long-term surface operations in support of future human exploration missions on Mars.

Perchlorates↗
NASA NTRS2019

Corrosion on Mars: Effect of the Mars Environment on Spacecraft Materials

This report presents the results of a one-year project, funded by NASA’s Kennedy Space Center Innovation Fund in FY18, to conduct a theoretical study on the effect of the Mars environment on spacecraft materials. Corrosion resistance is one of the most important properties in selecting materials for landed spacecraft and structures that will support surface operations for the human exploration of Mars. Currently, the selection of materials is done by assuming that the corrosion behavior of a material on Mars will be the same as that on Earth. This is understandable since there is no data on the corrosion resistance of materials in the Mars environment. However, given that corrosion is defined as the degradation of a metal that results from its chemical interaction with the environment, it cannot be assumed that corrosion is going to be the same in both environments since they are significantly different. The goal of this research was to develop a systematic approach to understand corrosion of spacecraft materials on Mars by conducting a literature search of available data, relevant to corrosion in the Mars environment. This project was motivated by the suggestion, by a team of researchers, that some of the structural degradation observed on Curiosity's wheels may have been caused by corrosive interactions with the transient liquid brines, reported to be present on Mars, while the most significant damage was attributed to rock scratching. An extensive literature search, on data relevant to corrosion on Mars, confirmed the need to investigate the interaction between materials, used for spacecraft and structures designed to support long-term surface operations on Mars, and the Mars environment. Previous preliminary experiments, designed to look at the interaction between aerospace aluminum alloy (AA7075-T73) and the gases present in the Mars atmosphere, at 20 degC and a pressure of 700 Pa, showed that there is an interaction between the small amount of oxygen present in the Mars gas and the alloy, when there is a scratch that removes the protective aluminum oxide film. Further studies are needed to consider many other important components of the Mars environment that can affect this interaction such as: the presence of brines, the interaction between these brines and materials, the effect of radiation on these interactions, and the possible catalytic effects of the clays present in the Martian regolith. This theoretical study provides strong justification to conduct experimental work to investigate the interaction between spacecraft materials with simulated Martian environments to reduce Mars exploration costs.

Calle, Luz M.↗
DOE OSTI2026

Evaluation of the interfacial bonding strength between hafnia and silica layers deposited by electron-beam evaporation

Factors contributing to the mechanical strength of multilayer optical coatings involving alternating layers of electron-beam evaporated hafnia and silica is investigated. It is observed that upon rupture resulting from nanosecond laser damage, fragments consisting of layer pairs are produced. These pairs are formed as a result of a strong asymmetry in adhesion strength between interfaces, leading to preferential delamination at interfaces where silica is deposited on hafnia, and not the reverse, despite the fact that both interface types consist of the same pair of materials. The observed disparity in adhesion strength was further studied using nanomechanical scratch tests, revealing loads at which fracture occurs, and fracture morphology, both of which are highly dependent on deposition order. Based on these measurements, along with the morphological analysis of the nanostructure of the coatings, we conclude that the surface microstructure in each coating layer affects its adhesion strength with the succeeding layer. The findings outlined here are particularly relevant to the production of large aperture, high-performance optics designed for use in next-generation lasers.

adhesion strength↗
DOE OSTI2025

A New Method for Polishing Laminated Dissimilar Metals

Here, this paper presents a novel method for preparing metallographic samples of laminated dissimilar metals, specifically a layered structure of zirconium (Zr), vanadium (V), and tungsten (W), which pose a challenge due to their differing crystal structures and hardness levels. The work was motivated by the need to develop a cladding for tungsten targets in spallation neutron sources, where a zirconium face is applied over an interlayer of either vanadium or titanium to prevent the formation of brittle intermetallic compounds such as ZrW 2 . Traditional polishing techniques prove inadequate because each constituent metal requires a different polishing approach based on its unique material properties. The methodology outlined includes a sequential process involving conductive epoxy mounting, successive grinding and polishing with specific abrasives, and a final chemical-mechanical polish using a colloidal silica-based solution. Optical microscopy confirmed that this multi-step process successfully produced scratch-free surfaces and minimized surface relief across the different metal interfaces. Although developed for refractory metals, this approach shows promise for other layered structures comprised of transition metals, offering a meticulous yet effective technique for high-quality metallographic sample preparation.

Newberry, Daniel [Oak Ridge National Laboratory (O↗
DOE OSTI2025

BBN-simple: How to bake a universe-sized cake

Big Bang Nucleosynthesis (BBN), the process of creation of lightest elements in the early universe, is a highly robust, precise, and ultimately successful theory that forms one of the three pillars of the standard hot-Big-Bang cosmological model. Existing theoretical treatments of BBN and the associated computer codes are accurate and flexible, but are typically highly technical and opaque, and not suitable for pedagogical understanding of the BBN. Here we present BBN-simple - a from-scratch numerical calculation of the lightest element abundances pitched at an advanced undergraduate or beginning graduate level. We review the physics of the early universe relevant for BBN, provide information about the reaction rates, and discuss computational-mathematics background that is essential in setting up a BBN calculation. Here, we calculate the abundances of the principal nuclear species in a standard cosmological model, and find a reasonably good agreement with public precision-level BBN codes.

Big bang nucleosynthesis↗
DOE OSTI2026

Sim-to-real supervised domain adaptation for radioisotope identification

Machine learning has the potential to improve the speed and reliability of radioisotope identification using gamma spectroscopy. However, meticulously labeling an experimental dataset for training is often prohibitively expensive, while training models purely on synthetic data is risky due to the domain gap between simulated and experimental measurements. In this research, we demonstrate that supervised domain adaptation can substantially improve the performance of radioisotope identification models by transferring knowledge between synthetic and experimental data domains. We consider two domain adaptation scenarios: (1) a simulation-to-simulation adaptation, where we perform multi-label proportion estimation using simulated high-purity germanium detectors, and (2) a simulation-to-experimental adaptation, where we perform multi-class, single-label classification using measured spectra from handheld lanthanum bromide (LaBr) and sodium iodide (NaI) detectors. We begin by pretraining a spectral classifier on synthetic data using a custom transformer-based neural network. After subsequent fine-tuning on just 64 labeled experimental spectra, we achieve a test accuracy of 96% in the sim-to-real scenario with a LaBr detector, far surpassing a synthetic-only baseline model (75%) and a model trained from scratch (80%) on the same 64 spectra. Furthermore, we demonstrate that domain-adapted models learn more human-interpretable features than experiment-only baseline models. Overall, our results highlight the potential for supervised domain adaptation techniques to bridge the sim-to-real gap in radioisotope identification, enabling the development of accurate and explainable classifiers even in real-world scenarios where access to experimental data is limited.

Lalor, Peter W.↗
DOE OSTI2025

Point cloud-based diffusion models for the Electron-Ion Collider

At high-energy collider experiments, generative models can be used for a wide range of tasks, including fast detector simulations, unfolding, searches of physics beyond the Standard Model, and inference tasks. In particular, it has been demonstrated that score-based diffusion models can generate high-fidelity and accurate samples of jets or collider events. This work expands on previous generative models in three distinct ways. First, our model is trained to generate entire collider events, including all particle species with complete kinematic information. We quantify how well the model learns event-wide constraints such as the conservation of momentum and discrete quantum numbers. We focus on the events at the future Electron-Ion Collider, but we expect that our results can be extended to proton-proton and heavy-ion collisions. Second, previous generative models often relied on image-based techniques. The sparsity of the data can negatively affect the fidelity and sampling time of the model. We address these issues using point clouds and a novel architecture combining edge creation with transformer modules called Point Edge Transformers. Third, we adapt the foundation model OmniLearn, to generate full collider events. This approach may indicate a transition toward adapting and fine-tuning foundation models for downstream tasks instead of training new models from scratch.

Araz, Jack Y. [Stony Brook Univ., NY (United State↗
DOE OSTI2025

Hybrid classical-quantum communication networks

Over the past several decades, the proliferation of global classical communication networks has transformed various facets of human society. Concurrently, quantum networking has emerged as a dynamic field of research, driven by its potential applications in distributed quantum computing, quantum sensor networks, and secure communications. This prompts a fundamental question: rather than constructing quantum networks from scratch, can we harness the widely available classical fiber-optic infrastructure to establish hybrid quantum–classical networks? This paper aims to provide a comprehensive review of ongoing research endeavors aimed at integrating quantum communication protocols, such as quantum key distribution, into existing lightwave networks. This approach offers the substantial advantage of reducing implementation costs by allowing classical and quantum communication protocols to share optical fibers, communication hardware, and other network control resources—arguably the most pragmatic solution in the near term. In the long run, classical communication will also reap the rewards of innovative quantum communication technologies, such as quantum memories and repeaters. Accordingly, our vision for the future of the Internet is that of heterogeneous communication networks thoughtfully designed for the seamless support of both classical and quantum communications.

Fiber-optic communication↗
DOE OSTI2026

Foundation models for atomistic simulation of chemistry and materials

Conventional computational methods for modeling chemical and materials systems are limited by system size and timescale, forcing a trade-off between quantum-mechanical accuracy and the sampling needed for realistic observables. Large language and vision foundation models — pre-trained on massive datasets using transformer architectures — have revolutionized many fields. It is thus interesting to ask whether a foundation model — subject to suitable data, parameter scaling and training — could enable learned simulations of chemistry and materials. Here, in this study, we review the field of machine-learned interatomic potentials (MLIPs) and posit that scaling up large and diverse chemical and materials datasets and highly expressive architectures using advanced training strategies should result in models that are: more efficient, transferable, robust to out-of-distribution scenarios, and easier to fine-tune to a variety of downstream physical observables than models trained from scratch on small datasets corresponding to specific, targeted atomistic simulation tasks. We provide specific criteria for creating such large-scale MLIP foundation models, coordinated strategies for their development, evaluation and deployment, and highlight potential emergent capabilities that could transform predictive simulations in chemistry and materials science and accelerate discovery across multiple technological domains.

Yuan, Eric C.-Y. [University of California, Berkel↗
DOE OSTI2024

Neural network representations of multiphase Equations of State

Abstract Equations of State model relations between thermodynamic variables and are ubiquitous in scientific modelling, appearing in modern day applications ranging from Astrophysics to Climate Science. The three desired properties of a general Equation of State model are adherence to the Laws of Thermodynamics, incorporation of phase transitions, and multiscale accuracy. Analytic models that adhere to all three are hard to develop and cumbersome to work with, often resulting in sacrificing one of these elements for the sake of efficiency. In this work, two deep-learning methods are proposed that provably satisfy the first and second conditions on a large-enough region of thermodynamic variable space. The first is based on learning the generating function (thermodynamic potential) while the second is based on structure-preserving, symplectic neural networks, respectively allowing modifications near or on phase transition regions. They can be used either “from scratch” to learn a full Equation of State, or in conjunction with a pre-existing consistent model, functioning as a modification that better adheres to experimental data. We formulate the theory and provide several computational examples to justify both approaches, highlighting their advantages and shortcomings.

Science & Technology - Other Topics↗
DOE OSTI2026

SmileyLlama: modifying large language models for directed chemical space exploration

Here we show that large language models (LLMs) can be transformed via supervised fine-tuning of engineered prompts into SmileyLlama for exploring the chemical space of drug molecules. We benchmark SmileyLlama against pretrained LLMs and chemical language models trained from scratch for generating valid and novel drug-like molecules, and use direct preference optimization to both improve SmileyLlama’s adherence to a prompt and as part of the iMiner reinforcement learning framework to predict molecules with optimized three-dimensional conformations and high binding affinity to drug targets. By training an LLM to speak directly as a chemical language model, while retaining most of its natural language capabilities, we show that SmileyLlama can reliably generate molecules with user-specified properties rather than acting only as a chatbot with knowledge of chemistry or as a virtual assistant. While SmileyLlama is geared toward drug discovery, the supervised fine-tuning/direct preference optimization/LLM framework can be extended to other chemical, biological and materials applications.

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

Enhancing generative molecular design via uncertainty-guided fine-tuning of variational autoencoders

In recent years, deep generative models have been successfully applied to various molecular design tasks, particularly in the life and materials sciences. One critical challenge for pre-trained generative molecular design (GMD) models is to fine-tune them to be better suited for downstream design tasks that aim at optimizing specific molecular properties. However, redesigning and training an existing effective generative model from scratch for each new design task are impractical. Furthermore, the black-box nature of typical downstream tasks that involve property prediction makes it nontrivial to optimize the generative model in a task-specific manner. In this work, we propose an uncertainty-guided fine-tuning strategy that can effectively enhance a pre-trained variational autoencoder (VAE) for GMD through performance feedback in an active learning setting. The strategy begins by quantifying the model uncertainty of the generative model using an efficient active subspace-based UQ (uncertainty quantification) scheme. Next, the decoder diversity within the characterized model uncertainty class is explored to expand the viable space of molecular generation. The low-dimensionality of the active subspace makes this exploration tractable using a black-box optimization scheme, which in turn enables us to identify and leverage a diverse set of high-performing models to generate enhanced molecules. Empirical results across six target molecular properties using multiple VAE-based generative models demonstrate that our uncertainty-guided fine-tuning strategy consistently leads to improved models that outperform the original pre-trained models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Charting the chemical space of Zintl phases with graph neural networks and bonding insights

A large number of Zintl phases have been discovered by solid-state chemists driven by empirical knowledge, chemical intuition and in some cases, through serendipitous accidents. These discoveries have only scratched the surface, given the vast compositional and structural diversity that Zintl phases can accommodate. The large chemical space of Zintl phases, as well as intermetallic compounds in general, remain under-explored. Here, we use graph neural networks and the upper bound energy minimization approach to efficiently scan a large chemical space of >90 000 hypothetical Zintl phases and accurately discover 1810 new thermodynamically stable phases with 90% precision, as validated with first-principles calculations. We show that our approach is more than 2× more accurate in predicting DFT stability than M3GNet (40% precision) on the same dataset. Using a random forest model and SHAP analysis, we demonstrate the critical role of ionic bonding in the thermodynamic stability of Zintl phases. Our results not only expand the known chemical landscape of Zintl phases but also highlight the efficacy of machine learning frameworks combined with domain knowledge in uncovering chemically meaningful insights across complex intermetallics.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Automatic Lane-Level Road Network Extraction from Aerial Imagery for Transportation Digital Twins

Accurate road networks are essential for credible traffic microsimulation and transportation digital twins, yet high-definition maps are often difficult to obtain due to limited availability, high cost, or proprietary restrictions. Some build networks from crowdsourced data, such as OpenStreetMap, but these sources often contain geometric and semantic inconsistencies. Others create networks manually, a process that is labor-intensive and difficult to scale. To address these limitations, this work presents an end-to-end pipeline that automatically extracts georeferenced, lane-level road networks from publicly available high-resolution satellite imagery and converts them into simulation-ready assets. The developed end-to-end pipeline has three primary modules: (1) A computer-vision-based module first detects directed lane geometries and intersection layouts. (2) A heuristic-based topology construction module then identifies approach and exit legs and establishes conflict-free lane-to-lane connections. (3) Finally, an automatic simulation-building module converts the extracted network into standard formats, e.g., OpenDRIVE, and generates routable SUMO networks. The framework supports both complete network construction from scratch and local-scale refinement of existing networks through lane-count correction, transition recovery, and geometric regularization. The proposed pipeline provides a practical pathway to generate traffic simulation networks from satellite imagery, significantly reducing manual reconstruction effort and enabling scalable, continuously updated transportation digital twins.

Guo, Hetian [University of Georgia, Athens] (ORCID↗