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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
NASA NTRS2017

Autonomous, Context-Sensitive, Task Management Systems and Decision Support Tools I: Human-Autonomy Teaming Fundamentals and State of the Art

Recent advances in artificial intelligence, machine learning, data mining and extraction, and especially in sensor technology have resulted in the availability of a vast amount of digital data and information and the development of advanced automated reasoners. This creates the opportunity for the development of a robust dynamic task manager and decision support tool that is context sensitive and integrates information from a wide array of on-board and off aircraft sourcesa tool that monitors systems and the overall flight situation, anticipates information needs, prioritizes tasks appropriately, keeps pilots well informed, and is nimble and able to adapt to changing circumstances. This is the first of two companion reports exploring issues associated with autonomous, context-sensitive, task management and decision support tools. In the first report, we explore fundamental issues associated with the development of an integrated, dynamic, flight information and automation management system. We discuss human factors issues pertaining to information automation and review the current state of the art of pilot information management and decision support tools. We also explore how effective human-human team behavior and expectations could be extended to teams involving humans and automation or autonomous systems.

context-sensitive↗
DOE OSTI2023

Towards Development of a Conceptual Model for Mercury in Bear Creek, Oak Ridge, Tennessee (FY23 Update)

Mercury concentrations in fish in Bear Creek are elevated and comparable to concentrations seen in fish in East Fork Poplar Creek (EFPC) on the Oak Ridge Reservation, even though aqueous inorganic mercury concentrations are orders of magnitude lower in Bear Creek than in EFPC. Acknowledging that the relationship between aqueous and fish tissue mercury concentrations is not linear, and that methylmercury (MeHg) concentrations are likely more related to fish tissue concentrations than aqueous total mercury (Hg T ) concentrations, MeHg production is not easily predicted or controlled. In Bear Creek, where aqueous Hg T concentrations are low, factors other than mercury loading drive the transformation of mercury to MeHg and subsequent trophic transfer to fish. Initial development of a waste disposal facility (Environmental Management Disposal Facility; EMDF) in Bear Creek Valley has begun, and operation of the EMDF has the potential to increase mercury inputs to Bear Creek. Consequently, understanding the factors contributing to elevated MeHg concentrations in water and fish in Bear Creek has increased relevance and importance. This report summarizes data from recent and historical compliance and investigatory studies with an eye toward building a conceptual model to understand the processes affecting mercury transport and transformation in the Bear Creek watershed, as well as to highlight key knowledge gaps in our understanding of these processes that warrant further investigation. The conceptual model will provide a strong technical basis for prioritizing and optimizing potential mitigation actions or best management practices to minimize potential negative effects of the EMDF related to mercury in Bear Creek.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Thermophysical Property Measurements of HTM and CM (Final Technical Report)

The goal of this project is to gather thermophysical property data – specifically, thermal conductivity, thermal diffusivity, and specific heat – of heat transfer media (HTMs) and containment materials (CMs) used in Topic Area 1 and 2A at high temperatures (>700°C). This goal was met by completing objectives such as qualifying high temperature measurement techniques, defining allowable uncertainty metrics, reporting properties in a public database, and providing ongoing support for prioritized materials. These are described in detail across three budget periods

36 MATERIALS SCIENCE↗
DOE OSTI2026

PRIME: An evaluation framework for protein representation inference and generalization in viral mutation space

Background Protein language models (PLMs) have revolutionized protein fitness prediction, yet their application to rapidly evolving viral pathogens is often confounded by extreme sequence homology. This homology leads to “data leakage” in standard random validation splits, yielding inflated performance metrics that fail to translate into real-world biosurveillance utility. Results We present Protein Representation Inference for Mutation Evaluation (PRIME), a framework that integrates domain-specific fine-tuning with a rigorous position-stratified validation protocol to evaluate viral threats. Using a dataset of 347,432 SARS-CoV-2 receptor binding domain (RBD) sequences, we demonstrate that while random training data split yields deceptive R 2 values (> 0.90), they fail to generalize to novel mutational sites. By benchmarking models up to 650 M parameters, we show that domain-specific fine-tuning of the ESM-C 600 M model with correctly stratified data provides an initial demonstration of predictive signal for binding affinity and expression at unseen mutational sites of binding affinity and expression on unseen sites (R 2 ~0.23), a significant advancement over base foundation models which exhibit no predictive power (R 2 <0). PRIME’s embedding-based clustering identified 3.03% of bat coronavirus sequences as candidates for further experimental prioritization based on their functional similarity to human-infective strains in embedding space, offering a perspective complementary to traditional phylogenetic methods. Conclusion PRIME establishes a new benchmark for the application of PLMs in pathogen surveillance. Our findings demonstrate that state-of-the-art models and fine-tuning, when paired with stratified validation, provide biologically meaningful insights into pathogen evolution and zoonotic risk.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Approach for energy efficient building design during early phase of design process

Energy consumption in the building sector is about 40% of total energy consumed globally and is trending upwards, along with its contribution to greenhouse gas (GHG) emissions. Given the adverse impacts of GHG emissions, it is crucial to integrate energy efficiency into building designs. The most significant opportunities for enhancing energy performance are present during the initial phases of building design, when there is less impact of other design constraints. Various tools exist for simulating different design options and providing feedback in terms of energy consumption and comfort parameters. These simulation outputs must then be analyzed to derive design solutions. This paper presents an innovative approach that utilizes user input parameters, processes them through cloud computing, and outputs easily understandable strategies for energy-efficient building design. The methodology employs Asynchronous Distributed Task Queues (DTQ) - a more scalable and reliable alternative to conventional speedup techniques-for conducting parametric energy simulations in the cloud. The goal of this approach is to assist design teams in identifying, visualizing, and prioritizing energy-saving design strategies from a range of possible solutions for each project. Furthermore, a tool ‘eDOT’ has been developed utilizing the discussed methodology. Unlike existing tools, eDOT leverages artificial intelligence to dynamically generate and provide design strategies during the early phases of design process. By simplifying the simulation process, eDOT enables design teams to make informed, data-driven decisions without needing to interpret complex simulation outputs. A case study simulated for two locations is provided in this paper to demonstrate the effectiveness of eDOT, further underscoring its practical impact on energy-efficient building design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Risk-based System Upgrade Planning for AOT-IC Presentation [Slides]

The number of deployed systems and different HW/SW form factors makes it difficult to maintain a complete understanding of their health which leads to a reduced level of certainty when assessing risk and trying to prioritize system upgrade efforts. AOT-IC must constantly plan and execute system upgrade and replacement projects to ensure the longevity of the control system. There are many more projects needed than the group can realistically accomplish given schedule and resource constraints. The group must prioritize their upgrade projects to choose only the highest priority projects to execute each year. Currently plans upgrade and replacement projects based on alignment with mission needs, group vision, resource availability, and qualitative risk assessments based on SME estimates. The group maintains a risk register. This is useful as a tool for shaping strategic planning but currently lacks the quantitative data and process needed to generate accurate risk assessments.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Enhancing Docking Accuracy with PECAN2, a 3D Atomic Neural Network Trained without Co-Complex Crystal Structures

Decades of drug development research have explored a vast chemical space for highly active compounds. The exponential growth of virtual libraries enables easy access to billions of synthesizable molecules. Computational modeling, particularly molecular docking, utilizes physics-based calculations to prioritize molecules for synthesis and testing. Nevertheless, the molecular docking process often yields docking poses with favorable scores that prove to be inaccurate with experimental testing. To address these issues, several approaches using machine learning (ML) have been proposed to filter incorrect poses based on the crystal structures. However, most of the methods are limited by the availability of structure data. Here, we propose a new pose classification approach, PECAN2 (Pose Classification with 3D Atomic Network 2), without the need for crystal structures, based on a 3D atomic neural network with Point Cloud Network (PCN). The new approach uses the correlation between docking scores and experimental data to assign labels, instead of relying on the crystal structures. We validate the proposed classifier on multiple datasets including human mu, delta, and kappa opioid receptors and SARS-CoV-2 Mpro. Our results demonstrate that leveraging the correlation between docking scores and experimental data alone enhances molecular docking performance by filtering out false positives and false negatives.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Expanding the Domain of Applicability of Machine Learning Models with Limited Data for Drug Property Prediction

Accurate machine learning models for predicting small molecule interactions with biological targets are essential for therapeutic discovery, biothreat response, and computational drug design, but their performance is often limited for understudied targets with sparse experimental data. To address this challenge, we developed and evaluated methods to improve molecular property prediction under low-data conditions, using the NimA-related kinase (NEK) family as a proof-of-concept. This work focused on two complementary goals within the ATOM Modeling PipeLine (AMPL) and the Generative Molecular Design (GMD) loop: expanding model applicability through transfer learning, representation learning, feature scaling, sampling strategies, and active-learning-inspired compound selection; and enabling efficient virtual screening to prioritize compounds that balance predicted activity, design objectives, and synthetic accessibility.

organic↗
DOE OSTI2026

The Backup Program of the Dark Energy Spectroscopic Instrument’s Milky Way Survey

The Milky Way Backup Program (MWBP), a survey currently underway with the Dark Energy Spectroscopic Instrument (DESI) on the Nicholas U. Mayall 4 m Telescope, works at the margins of the DESI Main surveys to obtain spectra of millions of additional stars from the Gaia catalog. Efficiently utilizing times between ∼12° and 18° twilight and poor weather conditions, the MWBP extends the range of stellar sources studied to both brighter magnitudes and lower Galactic latitude and declination than the stars studied in DESI’s Main Milky Way Survey. While the MWBP prioritizes candidate giant stars selected from the Gaia catalog (using color and parallax criteria), it also includes an unbiased sample of bright stars (i.e., 11.2 ≲ G < 16 mag) as well as fainter sources (to G ≲ 19 mag). As of 2025 March 1, the survey had obtained spectra of ∼7 million stars, approximately 1.2 million of which are included in the DESI Data Release 1. The DESI spectra cover the wavelength range from 3600 to 9800 Å at a resolution λ/Δλ varying from 2000 to 5000. The full survey, when completed, will cover an area of more than 21,000 deg 2 and include approximately 10 million Gaia sources, roughly equal to the number of stellar spectra obtained through the DESI Main Survey, while only utilizing ≈9% of all DESI observing time. This paper provides an overview of the MWBP, describing the target selection, observing strategy, and an introduction to the resulting data.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2002

Structural Weight Estimation for Launch Vehicles

This paper describes some of the work in progress to develop automated structural weight estimation procedures within the Vehicle Analysis Branch (VAB) of the NASA Langley Research Center. One task of the VAB is to perform system studies at the conceptual and early preliminary design stages on launch vehicles and in-space transportation systems. Some examples of these studies for Earth to Orbit (ETO) systems are the Future Space Transportation System [1], Orbit On Demand Vehicle [2], Venture Star [3], and the Personnel Rescue Vehicle[4]. Structural weight calculation for launch vehicle studies can exist on several levels of fidelity. Typically historically based weight equations are used in a vehicle sizing program. Many of the studies in the vehicle analysis branch have been enhanced in terms of structural weight fraction prediction by utilizing some level of off-line structural analysis to incorporate material property, load intensity, and configuration effects which may not be captured by the historical weight equations. Modification of Mass Estimating Relationships (MER's) to assess design and technology impacts on vehicle performance are necessary to prioritize design and technology development decisions. Modern CAD/CAE software, ever increasing computational power and platform independent computer programming languages such as JAVA provide new means to create greater depth of analysis tools which can be included into the conceptual design phase of launch vehicle development. Commercial framework computing environments provide easy to program techniques which coordinate and implement the flow of data in a distributed heterogeneous computing environment. It is the intent of this paper to present a process in development at NASA LaRC for enhanced structural weight estimation using this state of the art computational power.

Cerro, Jeff↗
NASA NTRS2009

Human Research Program Human Health Countermeasures Element Bone and Muscle Risk Standing Review Panel (SRP)

The Bone and Muscle Risk Standing Review Panel (SRP) met at the NASA Johnson Space Center (JSC) on October 4-6, 2009 to discuss the areas of current and future research targeted by the Human Health Countermeasures (HHC) Element of the Human Research Program (HRP). Using evidence-based knowledge as a background for identified risks to astronaut health and performance, NASA had identified gaps in knowledge to address those risks. Ongoing and proposed tasks were presented to address the gaps. The charge to the Bone and Muscle Risk SRP was to review the gaps, evaluate whether the tasks addressed these gaps and to make recommendations to NASA s HRP Science Management Office regarding the Panel's review. The Bone and Muscle Risk SRP consisted of scientists who are experts in muscle, bone, or both and could evaluate the existing evidence with sufficient knowledge of the potential effects of long duration exposure to microgravity. More important, although expertise in basic science is important, the SRP was requested to evaluate the practicality of the proposed efforts in light of the realistic demands placed on the HRP. In short, all tasks presented in the Integrated Research Plan (IRP) should address specific questions related to the challenges faced by the astronauts as a result of prolonged exposure to microgravity. All tasks proposed to fill the gaps in knowledge should provide applied, translational data necessary to answer the specific questions. Several presentations were made to the SRP during the site visit and the SRP spent sufficient time to address the panel charge, either as a group or in separate sessions for the Bone and Muscle Risk subgroups. The SRP made a final debriefing to the HRP Program Scientist, Dr. John B. Charles, on October 6, 2009. Taking the evidence and identified risks as givens, the SRP concluded that 1) integration of information should lead to a more comprehensive approach to identifying the gaps, 2) not all tasks addressed the gaps as stated in the IRP, 3) better access should be given to the SRP to existing data to include in its review, and 4) there were some missing gaps and tasks. As a result the SRP recommended 1) combining certain gaps, which in some cases were addressed with identical tasks, 2) additional gaps and tasks to address some of the gaps, 3) deleting some gaps and tasks to better focus the efforts of NASA s HRP in prioritizing their efforts, and 4) prioritizing gaps to address significant issues needing resolution in the short term while maintaining an awareness of long-term goals. The SRP commended the efforts by the HHC Element to integrate knowledge gaps and design tasks to address the higher order questions. This integration did not apply to the Bone and Muscle Risk alone, but the musculoskeletal system as its functions integrate with issues in

Glowacki, Julie↗
NASA NTRSDate not supplied

Long-Term Health Risk Quantification

Astronauts face hazards during spaceflight, including space radiation exposure, isolation and confinement, traveling far distances from Earth, reduced gravity levels, and closed and hostile environments. These hazards drive the definition of human health and performance risks associated with spaceflight. NASA’s Human System Risk Board maintains the human spaceflight risk posture for in-mission risks, as well as post-flight, Long-Term Health (LTH)risks potentially occurring later in the astronaut’s life. LTH risk encompasses the timeframe from immediately post-flight, through the rest of an astronaut’s career, through retirement, and until death. Possible LTH risk outcomes include the time and interventions needed for the astronaut to return to preflight physiological states after experiencing spaceflight hazards and recovery from any in-mission medical events that persist into the post-flight timeframe. It includes chronic complications that may arise due to experiencing in-flight medical conditions or injuries and medical conditions that occur later in life with a higher probability of occurrence or with more severity because of their spaceflight exposure. Finally, LTH risk outcomes can also include a reduction in life expectancy due to spaceflight exposures. There have been 144 medical conditions identified by NASA’s Lifetime Surveillance of Astronaut Health team to be associated with LTH risk. Epidemiological studies have been performed for some of these conditions to determine if astronauts suffer from an increased prevalence or severity of the condition due to their spaceflight experience compared to a comparable cohort .Differences in astronaut mortality or morbidity due to spaceflight experience were not detected in several of these studies. There were two cases where a modest increase in the incidence rate of astronaut LTH outcomes was detected. The first suggested an increase in the incidence of melanoma cases in astronauts, where the number of cases in astronauts were similar to the elevated number of cases observed in airplane pilots. The second provided some evidence of elevated numbers of cardiovascular disease events in astronauts compared to an appropriate healthy comparator cohort, which may warrant additional investigation. The lack of detection of LTH risk outcomes should not ease concerns about astronaut LTH. The studies highlighted here constitute only a small portion of the potential LTH conditions that could occur. Once epidemiological studies are performed on all conditions, significant findings may be detected. The analysis of astronaut LTH also suffers from limited numbers of data points because of the limited numbers of astronauts overall and the even fewer who have reached an age where LTH outcomes may begin to manifest. As shuttle and ISS astronauts begin to age and increase the feasibility of analysis, LTH outcomes may be detected. An application of risk quantification is the use of risk metrics within trade studies for resource prioritization and decision making. Trade studies regarding countermeasures to LTH risk outcomes would benefit from a quantification of LTH risk. NASA has ground-based processes in place such as astronaut screening and access to continuous medical monitoring and care during and after their astronaut career which are the main methods for mitigating LTH risk. In-mission countermeasures, such as acceptable levels of medical care and available countermeasures to counter spaceflight related physiological decrements, can mitigate a poor health and performance status immediately post-flight. Identifying appropriate risk metrics, obtaining valid quantities for them, and tying them to LTH countermeasures are necessary steps for realizing their use in trade studies. This presentation will highlight the challenges associated with the identification, quantification, and utilization of LTH risk metrics

Beth Lewandowski↗
NASA NTRS2002

Technology Assessments within NASA's Integrated Technology Assessment Center

NASA's Advanced Space Transportation Program (ASTP) founded the Integrated Technology Assessment Center (ITAC) to provide a comprehensive, systematic approach to identify long-term technology needs, to quantify payoffs for technology investments, and to assess the progress of ASTP-sponsored technology programs in the hypersonics/Earth-to-orbit area. To accomplish these goals, the ITAC has assembled an experienced team representing a broad sector of the aerospace community and developed a systematic assessment process complete with supporting tools. In the ITAC approach, concepts for transportation systems are first selected based on relevance to the ASTP. Models of these concepts are then developed and data on advanced technologies are collected. Projections of key technology characteristics with respect to the specific concepts of interest are made. Both the models and technology projections are then fed into the ITAC's probabilistic systems analysis framework. The probabilistic outputs are weighed against metrics of interest to ASTP and a multivariate decision making process is used to provide inputs for technology prioritization within the ASTP. At present, the ITAC program is working to evaluate a variety of technologies for three two-stage hypersonic vehicle concepts. Concepts include an all rocket, vertical take off-horizontal landing (VTHL) system, a horizontal take-off-horizontal landing (HTHL) RBCC-propelled first stage/all rocket second stage system, and an HTHL turbine-based first stage/all rocket second stage system. This paper will provide a status update of the ITAC program including current results and plans.

Taylor, J. L.↗
NASA NTRS2002

Technology Assessments within NASA's Integrated Technology Assessment Center

NASA's Advanced Space Transportation Program (ASTP) founded the Integrated Technology Assessment Center (ITAC) to provide a comprehensive, systematic approach to identify long-term technology needs, to quantify payoffs for technology investments, and to assess the progress of ASTP-sponsored technology programs in the hypersonics/Earth-to-orbit area. To accomplish these goals, the ITAC has assembled an experienced team representing a broad sector of the aerospace community and developed a systematic assessment process complete with supporting tools. In the ITAC approach, concepts for transportation systems are first selected based on relevance to the ASTP. Models of these concepts are then developed and data on advanced technologies are collected. Projections of key technology characteristics with respect to the specific concepts of interest are made. Both the models and technology projections are then fed into the ITAC's probabilistic systems analysis framework. The probabilistic outputs are weighed against metrics of interest to ASTP and a multivariate decision making process is used to provide inputs for technology prioritization within the ASTP. At present, the ITAC program is working to evaluate a variety of technologies for three two-stage hypersonic vehicle concepts. Concepts include an all rocket, vertical take off-horizontal landing (VTHL) system, a horizontal takeoff-horizontal landing (HTHL) RBCC-propelled first stage/all rocket second stage system, and an HTHL turbine-based first stage/all rocket second stage system. This paper will provide a status update of the ITAC program including current results and plans.

Taylor, J. L.↗
NASA NTRSDate not supplied

Defining Services, Functions, and Capabilities for an Advanced Air Mobility (AAM) In-time Aviation Safety Management System (IASMS)

NASA’s vision for Advanced Air Mobility (AAM) Mission is to help emerging aviation markets to safely develop an air transportation system that moves people and cargo between places previously not served or underserved by aviation. The integration of new operational paradigms and vehicle classes in this system requires a transformation of the National Airspace System (NAS) that includes substantive changes critical for assuring safety. These changes are compelled by unique challenges posed by AAM to the safety management system (SMS). These challenges were assessed by committees of the National Academies in their reports on a vision for an In-time Aviation Safety Management System (IASMS) and a blueprint for AAM [1,2]. In their description of an IASMS, the top recommendation was development of a concept of operations (ConOps) for IASMS. This paper describes the high-priority recommendations from the National Academies for its IASMS vision and how they are addressed through a distributed system-of-systems architecture. The IASMS architecture is structured on the services, functions, and capabilities (SFCs) necessary for In-time System-wide Safety Assurance (ISSA)initially developed for urban air mobility (UAM). The paper then posits where these SFCs would reside across vehicles, airspace, or service suppliers such as Supplemental Data Service Providers (SDSPs), and how SFCs scale with increasing complexity in design and operations of AAM. SFCs are foundational building blocks for a system that targets an individual or family of risks using a Monitor-Assess-Mitigate risk paradigm for anomalies, precursors and trends. An IASMS could be conceived that uses a portfolio of SFCs for AAM in general or prioritizes SFCs for a specific domain or operation.

K Ellis↗
DOE OSTI2023

Pillars of Cloud-Based Earth Observation Science Education

Earth observation (EO) is undergoing a paradigm shift with the development of cloud-based analytical platforms supporting EO data collection and access, parallel processing, easier communication of results, and expanded accessibility. As the global community of users and the diversity of applications grow, there is a clear need for expanded educational capacity to leverage these developments and increase the impact of EO research and teaching. Drawing upon extensive conversations between educators, practitioners, and researchers, we propose three pillars that must be prioritized to prepare students, researchers, and professionals to take full advantage of the cloud-based EO paradigm and guide future growth.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A framework to evaluate machine learning crystal stability predictions

The rapid adoption of machine learning in various scientific domains calls for the development of best practices and community agreed-upon benchmarking tasks and metrics. We present Matbench Discovery as an example evaluation framework for machine learning energy models, here applied as pre-filters to first-principles computed data in a high-throughput search for stable inorganic crystals. We address the disconnect between (1) thermodynamic stability and formation energy and (2) retrospective and prospective benchmarking for materials discovery. Alongside this paper, we publish a Python package to aid with future model submissions and a growing online leaderboard with adaptive user-defined weighting of various performance metrics allowing researchers to prioritize the metrics they value most. To answer the question of which machine learning methodology performs best at materials discovery, our initial release includes random forests, graph neural networks, one-shot predictors, iterative Bayesian optimizers and universal interatomic potentials. We highlight a misalignment between commonly used regression metrics and more task-relevant classification metrics for materials discovery. Accurate regressors are susceptible to unexpectedly high false-positive rates if those accurate predictions lie close to the decision boundary at 0 eV per atom above the convex hull. The benchmark results demonstrate that universal interatomic potentials have advanced sufficiently to effectively and cheaply pre-screen thermodynamic stable hypothetical materials in future expansions of high-throughput materials databases.

Riebesell, Janosh↗
DOE OSTI2023

Completeness of the NASA/IPAC Extragalactic Database (NED) Local Volume Sample

We introduce the NASA/IPAC Extragalactic Database (NED) Local Volume Sample (NED-LVS), a subset of ~1.9 million objects with distances out to 1000 Mpc. We use UV and IR fluxes available in NED from all-sky surveys to derive physical properties, and estimate the completeness relative to the expected local luminosity density. The completeness relative to near-IR luminosities (which traces a galaxy's stellar mass) is roughly 100% at D < 30 Mpc and remains moderate (70%) out to 300 Mpc. For brighter galaxies (≳L * ), NED-LVS is ~100% complete out to ~400 Mpc. When compared to other local Universe samples (GLADE and HECATE), all three are ~100% complete below 30 Mpc. At distances beyond ~80 Mpc, NED-LVS is more complete than both GLADE and HECATE by ~10%–20%. NED-LVS is the underlying sample for the NED gravitational-wave follow-up service (NED-GWF), which provides prioritized lists of host candidates for GW events within minutes of alerts issued by the LIGO–Virgo–KAGRA collaboration. We test the prioritization of galaxies in the volume of GW170817 by three physical properties, where we find that both stellar mass and inverse specific star formation rate place the correct host galaxy in the top 10. In addition, NED-LVS can be used for a wide variety of other astrophysical studies: galaxy evolution, star formation, large-scale structure, galaxy environments, and more. The data in NED are updated regularly, and NED-LVS will be updated concurrently. Consequently, NED-LVS will continue to provide an increasingly complete sample of galaxies for a multitude of astrophysical research areas for years to come.

79 ASTRONOMY AND ASTROPHYSICS↗