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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 OSTI2025

Development of a Western U.S. Fusion Energy Commercialization Hub

Fusion energy, if successfully commercialized and globally adopted, could substantially increase U.S. energy dominance and provide essentially unlimited baseload power for the national grid. It also stands to provide the U.S. with a strong source of economic growth through the 2030s and 2040s, giving the U.S. a new and highly internationally competitive industry for export. Fusion can also provide the increased electrical power generation capacity required to power current and future artificial intelligence (AI) activities. In October 2024, the CleanTech Alliance held a fusion energy commercialization regional hub workshop alongside its annual Seattle Fusion Week conference. The event brought together fusion companies, researchers, economic development experts, federal and state government representatives, and power utilities from the western states of Washington, California, Colorado and Nevada. Attendees discussed the potential of regionally focused commercialization of fusion energy in three pillars: technology, workforce, and economic development. Mel Clark, President & CEO, CleanTech Alliance, and Dr. Javier Garay, Associate Dean for Research and Professor of Mechanical and Aerospace Engineering, at the University of California San Diego (UCSD) and Founding Director of the UCSD Fusion Engineering institute, are co-PIs for this project. The project is sponsored by the U.S. Department of Energy’s Office of Fusion Energy Sciences (FES) through a Field Work Proposal to Pacific Northwest National Laboratory (PNNL) with Karl Mueller as point-of-contact. Chris Ajemian (Principal, Ajemian Consulting, LLC) and Dr. Christopher Keane (Professor of Physics and former Vice-President for Research, Washington State University) co-organized the workshop and were the editors for this report. This report details the current state of the work of the four western states to develop industry-led collaboration with national laboratories, universities, and government at all levels to hasten the commercialization of fusion energy. It provides a summary of fusion energy R&D needs, presents the ideas the attendees at the workshop identified for deepening regional collaboration, and makes findings and recommendations for next steps.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Search for the jet-induced diffusion wake in the quark-gluon plasma via measurements of jet-track correlations in photon-jet events in Pb+Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV with the ATLAS detector

This paper presents a measurement of jet-track correlations in photon-jet events, using 1.72 nb −1 of Pb+Pb data at $\sqrt{s_{NN}}$ = 5.02TeV recorded with the ATLAS detector at the LHC. Events with energetic photon-jet pairs are selected, where the photon and jet are approximately back-to-back in azimuth. The angular correlation between jets and charged-particle tracks with transverse momentum (𝑝 T ) in the range 0.5–2.0 GeV in the hemisphere opposite to the jet, |Δ⁢𝜙(jet,track)|>𝜋/2, is measured as a function of their relative pseudorapidity difference, |Δ⁢𝜂(jet,track)|. In central Pb+Pb collisions, these correlations are predicted to be sensitive to the diffusion wake in the quark-gluon plasma resulting from the lost energy of high-𝑝T partons traversing the plasma, with a characteristic modification as a function of |Δ⁢𝜂(jet,track)|. The correlations are examined with different selections on the jet-to-photon 𝑝 T ratio to select events with different degrees of energy loss. No diffusion wake signal is observed within the current sensitivity and upper limits at 95% confidence level on the diffusion wake amplitude are reported.

heavy flavor physics↗
DOE OSTI2024

Beyond the Hype: Navigating the Promise and Pitfalls of Multi-Modal Models for Materials Science

Multi-modal models offer great potential for accelerating discovery in materials and chemical systems, but their adoption raises crucial questions: What materials science challenges are best addressed by multi-modal approaches? How do we weigh the benefits against the resource investment required for multi-modal data acquisition? And critically, how can we optimize experimental workflows to leverage these models effectively? In this presentation, I will delve into the development of multi-modal characterization and analytics, focusing on their application in the demanding fields of next-generation microelectronics and energy storage materials. I will share challenges encountered in designing these workflows, highlighting lessons learned and posing questions that remain unanswered.

AI↗
NASA NTRSDate not supplied

RadLab Platform: Investigating Space Radiation

The RadLab Project, initiated by the Open Science Data Repository (OSDR) for Space Biology at NASA Ames Research Center (osdr.nasa.gov), aims to be an ongoing compilation of radiation data relevant to human space flight. Sponsored by the NASA Human Research Program, RadLab serves as the latest database in this domain. RadLab is intended to serve the needs of both the space radiation detector and space radiation biology communities. The RadLab architecture is being designed to accommodate data both from low Earth orbit (LEO) and beyond low Earth orbit (BLEO). Our long-term vision entails the establishment of a sustainable database receiving continuous updates through APIs connecting to other databases, as well as individual investigator contributions via a RadLab submission portal, modeled on the OSDR (https://osdr.nasa.gov/bio/submission-sso-login.html). In addition to serving as a resource for space biologists and space radiation physicists, such a database would facilitate the deployment of AI algorithms to study the impact of location in space and within spacecraft on the ambient space radiation field. The ultimate goal is to develop predictive dosimetry algorithms for future BLEO missions.

Sylvain V Costes↗
NASA NTRS2003

MAPGEN : mixed initiative planning and scheduling for the Mars '03 MER mission

The Mars Exploration Rovers Mars '03 mission is one of NASA's most ambitious science missions to date. The rovers will be launched in the summer of 2003 with each rover carrying instruments to conduct remote and in-situ observation to elucidate the planet's past climate, water activity, and habitability. Science is the primary driver of MER and, as a consequence, making best use of the scientific instruments, within the available resources, is a crucial aspect of the mission. To address this critically, the MER project has selected MAPGEN (Mixed-Initiative Activity Plan GENerator) as an activity planning tool. MAPGEN combines two exiting systems, each with a strong heritage: APGEN the Activity Planning tool from the Jet Propulsion Laboratory and the Europs Planning/Scheduling system from NASA Ames Research Center. This paper discusses the issues arising from combining these tools in the context of this mission.

planning and scheduling↗
DOE OSTI2025

Dataset for Leveraging CryoEM and AI-Driven Morphological Feature Analysis for Insights on Bacterial Structures

This repository hosts an AI-assisted image segmentation and analysis pipeline for Pantoea sp. YR343 cryo-electron microscopy (cryoEM) datasets. The workflow automates membrane thickness measurements, flagella detection, and field-of-view (FOV) screening from low-dose, high-resolution cryoEM micrographs eliminating the need for slow manual annotation. By integrating deep-learning based segmentation (YOLOv11) with quantitative post-processing, this toolkit provides a scalable and reproducible way to study bacterial morphology under hydrated, near-native conditions. The GitHub repository for AI-based tools for cryoEM bacteria ultrastructures can be found here: https://github.com/Sireesiru/Cryo-EM-Ultrastructures/tree/main

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Examples of Mission-driven Data Science from Jefferson Lab and ACES

This presentation details mission-driven data science initiatives at Jefferson Lab and the Joint Institute for Advanced Computing on Environmental Studies (ACES). JLab, a U.S. Department of Energy Office of Science national laboratory, operates the Continuous Electron Beam Accelerator Facility (CEBAF), and is the lead institute for the new High Performance Data Facility (HPDF) Hub. The Joint Institute for ACES brings together interdisciplinary teams in health informatics, climate modeling, computer science, and physics to address environmental challenges, including flood modeling. The Hampton Roads region, particularly Norfolk and Virginia Beach, faces increasing flood risks, motivating the need for rapid, reliable, and risk-aware decision support. ACES’s flooding work has a focus on uncertainty quantification (UQ) and machine learning (ML) for coastal flood management. The work is motivated by the increasing vulnerability of communities such as Norfolk and Virginia Beach, Virginia, to frequent coastal flooding events, and the need for rapid, reliable decision support. The research develops computationally efficient ML surrogate models to forecast water levels and flooding risk. A central theme is the quantification and calibration of predictive uncertainty, especially for out-of-distribution (OOD) scenarios, using techniques such as Monte Carlo Dropout, Deep Ensembles, Gaussian Processes, and Deep Quantile Regression (DQR). The study demonstrates that distance-aware UQ is critical for reliable scientific AI, particularly in high-dimensional, safety-critical, and real-time applications.

McSpadden, Diana [Thomas Jefferson National Accele↗
DOE OSTI2025

Artificial intelligence-based predictive modeling for imaging neutral particle analyzers on the DIII-D tokamak

The Imaging Neutral Particle Analyzer (INPA) at DIII-D is a diagnostic system used to accurately resolve the energy and spatial distributions of fast ions in fusion plasmas. A novel artificial intelligence (AI) technique named INPA-net is based on Reservoir Computing Networks and developed here to predict active and passive signals produced by charge-exchange reactions from injected and edge-cold neutrals, respectively, in magnetically confined fusion plasmas. This model is trained using a set of 21 time domain signals between 0 s to 3.35 s that includes injected beam and thermal plasma information, and 6444 real 2D experimental images of the INPA in 12 plasma discharges at DIII-D. The trained neural network is able to forecast experimental images in real-time. The model achieves an R-squared value of 0.91, which is higher than the 0.83 value achieved by a simple linear regression model. This improvement highlights the model's enhanced predictive accuracy for measured images from the validation set. This AI approach is valuable due to its rapid response times and potential for integration into real-time plasma control systems. A version of this model capable of generating syntehic images would be useful for the real-time monitoring of fast-ion transport. A comprehensive sensitivity study reveals that INPA-net maintains high performance even with variations in the input parameters, indicating the model's robustness and reliability. While developed for the INPA, the underlying architecture is adaptable and may be applied to various 2D imaging diagnostics in fusion research.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Exascale Computing and Data Handling: Challenges and Opportunities for Weather and Climate Prediction

The emergence of exascale computing and artificial intelligence offer tremendous potential to significantly advance Earth system prediction capabilities. However, enormous challenges must be overcome to adapt models and prediction systems to use these new technologies effectively. A 2022 WMO report on exascale computing recommends “urgency in dedicating efforts and attention to disruptions associated with evolving computing technologies that will be increasingly difficult to overcome, threatening continued advancements in weather and climate prediction capabilities.” Further, the explosive growth in data from observations, model and ensemble output, and postprocessing threatens to overwhelm the ability to deliver timely, accurate, and precise information needed for decision-making. Artificial intelligence (AI) offers untapped opportunities to alter how models are developed, observations are processed, and predictions are analyzed and extracted for decision-making. Given the extraordinarily high cost of computing, growing complexity of prediction systems, and increasingly unmanageable amount of data being produced and consumed, these challenges are rapidly becoming too large for any single institution or country to handle. This paper describes key technical and budgetary challenges, identifies gaps and ways to address them, and makes a number of recommendations.

Atmosphere↗
NASA NTRSDate not supplied

Modern Scientific Data Governance Framework

Science has entered the era of Big Data with new challenges related to data governance, stewardship, and management. The existing data governance practices must catch up to ensure proper data management. Existing data governance policies and stewardship best practices tend to be disconnected from operational data management practices and enforcement and mainly exist in well-meaning documents or reports. These governance policies are, at best, partially implemented and rarely monitored or audited. In addition, existing governance policies keep adding additional data management steps that require a human, ‘a data steward’, in the loop, and the cost of data management can no longer scale proportionately with the current and future increased data volume and complexity. The goal for developing an updated data governance framework is to modernize scientific data governance to the reality of Big data and align it with the current technology trends such as cloud computing and AI. The goals of this framework are two folds. One is to ensure thoroughness that the governance adequately covers the entire data life cycle. Two, provide a practical approach that offers a consistent and repeatable process for different projects. Three core principles ground this framework. First, focus on just enough governance and prevent data governance from becoming a roadblock toward the scientific process. Remove any unnecessary processes and steps. Second, automate data management steps where possible. Actively remove steps that require ‘human in the loop’ within the management process to be efficient and scale with increasing data. Third, all the processes should continually be optimized using quantified metrics to streamline the monitoring and auditing workflows.

Rahul Ramachandran↗
DOE OSTI2025

Advances in relaxation and memory effects of magnetic nanoparticles for biomedical applications

Functionalized magnetic nanoparticles are pivotal in magnetic resonance imaging, computed tomography, controlled drug delivery, and hyperthermia treatments due to their exceptional magnetic relaxation and functional properties. The magnetic core composition and structure significantly affects the complex magnetic properties of these nanoparticles necessitating a thorough examination of magnetism fundamentals related to these systems. One important aspect is the ability of magnetic nanoparticles to retain previous magnetic state configurations known as memory effect, primarily governed by domain structure and magnetic anisotropy. Despite its relevance to advanced applications, comprehensive studies on magnetic relaxation and memory effects remain limited. Here, the present review aims to bridge this gap by investigating relaxation mechanisms, synthesis strategies, and applications, fostering further innovation. It investigates the memory effects and their dependence on particle composition and morphology along with key synthesis techniques for large-scale production in industrial adoption. Structured into focused sections on magnetic properties and their influence on biomedical and technological applications, this review provides essential insights into memory effects, magneto-relaxation mechanisms, influencing factors, and both experimental and theoretical methodologies. It also delves into computational modelling and AI-driven design, which are revolutionizing the prediction, discovery, and optimization of materials with tailored properties.

36 MATERIALS SCIENCE↗
DOE OSTI2026

The ARM Precipitation Best Estimate (PrecipBE) Value-Added Product Report

The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility’s Precipitation Best-Estimate (PrecipBE) Value-Added Product integrates multiple precipitation datastreams, accounting for data quality and instrument limitations, to deliver comprehensive per-precipitation event properties alongside ancillary ARM data set data. PrecipBE bundles all valid surface rainfall samples into artificial intelligence (AI)-ready tabular and time-series formats, reporting bundle means and uncertainty ranges. This per-event structure provides an insightful and easy-to-use resource for researchers analyzing precipitation characteristics.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Atlas: Navigating NASA’s Knowledge Universe with AI-Powered Natural Language Queries

NASA has a vast archive of engineering guidelines, standards, and best practices collected over decades. This encompasses a breadth of topics from rocketry and engineering standards to risk management and space-related health issues. This wealth of information, while invaluable to NASA engineers, staff, and the public, is too extensive for any individual to fully comprehend. To address this challenge, we have developed Atlas, a tool within NASA's Mission Cloud Platform that enables users to query these diverse sources effectively. Atlas allows users to ask natural language questions and receive answers grounded in factual information from source documents. The tool provides responses with direct quotations and links to original documents, ensuring transparency and accuracy. It can address a wide range of queries, from specific technical details like safe distances for rocket launches from lightning to broader topics such as crew health requirements for long-duration space missions, corrosion protection in low Earth orbit, and NASA's agreements with various entities. In developing Atlas, we encountered and overcame several technical challenges. Large Language Models often struggle with consistently providing accurate information, especially for highly specialized topics. We implemented strategies to prevent hallucinations and ensure the reliability of responses, even for complex questions on topics ranging from NASA Mission Classes to intricate rocket science concepts. Additionally, we addressed the challenges of delivering quick responses while maintaining cost-effectiveness. Our presentation will detail the innovative approaches we employed to optimize performance and efficiency, making Atlas a powerful and practical tool for accessing NASA's extensive knowledge base.

Artificial Intelligence↗
DOE OSTI2025

Emulation of Synaptic Plasticity in WO 3 ‐Based Ion‐Gated Transistors

Neuromorphic systems, inspired by the human brain, promise significant advancements in computational efficiency and power consumption by integrating processing and memory functions, thereby addressing the von Neumann bottleneck. This paper explores the synaptic plasticity of a WO3-based ion-gated transistor (IGT) in [EMIM][TFSI] and a 0.1 mol L −1 LiTFSI in [EMIM][TFSI] for neuromorphic computing applications. Cyclic voltammetry (CV), transistor characteristics, and atomic force microscopy (AFM) force–distance (FD) profiling analyses reveal that Li + brings about ion intercalation, together with higher mobility and conductance, and slower response time (τ). WO 3 IGTs exhibit spike amplitude-dependent plasticity (SADP), spike number-dependent plasticity (SNDP), spike duration-dependent plasticity (SDDP), frequency-dependent plasticity (FDP), and paired-pulse facilitation (PPF), which are all crucial for mimicking biological synaptic functions and understanding how to achieve different types of plasticity in the same IGT. The findings underscore the importance of selecting the appropriate ionic medium to optimize the performance of synaptic transistors, enabling the development of neuromorphic systems capable of adaptive learning and real-time processing, which are essential for applications in artificial intelligence (AI).

36 MATERIALS SCIENCE↗
DOE OSTI2025

Development of Automated Atom Probe Tomography capability to study the influence of applied voltage and laser power on the final apparent composition of the analyzed specimen

This study presents the development and implementation of an autonomous Bayesian optimization (BO) framework for controlling and optimizing experimental parameters in Atom Probe Tomography (APT). Using commercial silicon needle samples as a benchmark system, we demonstrate that BO can efficiently navigate the complex parameter space of voltage and laser power to achieve target charge state ratios (specifically Si + /(Si + +Si 2+ )) with minimal experimental evaluations. Our implementation integrates Gaussian Process modeling with the CAMECA atom probe control framework, enabling autonomous adjustment of experimental conditions in real-time. Results show that the algorithm successfully converges to target ratios under different scenarios: maintaining a reference ratio, increasing the ratio (favoring Si 1+ ), and decreasing the ratio (favoring Si 2+ ). The system adapts to specimen evolution during analysis, compensating for changes in apex geometry while maintaining optimization targets. This work establishes a proof of concept for AI-driven optimization in APT, addressing the traditional challenges of manual parameter tuning and paving the way for applications to more complex materials where compositional accuracy is critical.

36 MATERIALS SCIENCE↗
NASA NTRS2005

ESTIMATING THE BENEFIT OF TRMM TROPICAL CYCLONE DATA IN SAVING LIVES

The Tropical Rainfall Measuring Mission (TRMM) is a joint NASA/JAXA research mission launched in late 1997 to improve our knowledge of tropical rainfall processes and climatology (Kummerow et ai., 2000; Adler et ai., 2003). In addition to being a highly successful research mission, its data are available in real time and operational weather agencies in the U.S. and internationally are using TRMM data and images to monitor and forecast hazardous weather (tropical cyclones, floods, etc.). For example, in 2004 TRMM data were used 669 times for determining tropical cyclone location fixes (National Research Council, 2004). TRMM flies at a relatively low altitude, 400 km, and requires orbit adjustment maneuvers to maintain altitude against the small drag of the atmosphere. There is enough fuel used for these maneuvers remaining on TRMM for the satellite to continue flying until 2011-12. However, most of the remaining fuel may be used to perform a controlled re-entry of the satellite into the Pacific Ocean. The fuel threshold for this operation will be reached in the summer of 2005, although the maneuver would actually occur in late 2006 or 2007. The full science mission would end in 2005 under the controlled re-entry option. This re-entry option is related to the estimated probability of injury (1/5,000) that might occur during an uncontrolled re-entry of the satellite. If the estimated probability of injury exceeds 1/10,000 a satellite is a candidate for a possible controlled re-entry. In the TRMM case the NASA Safety Office examined the related issues and concluded that, although TRMM exceeded the formal threshold, the use of TRMM data in the monitoring and forecasting of hazardous weather gave a public safety benefit that compensated for TRMM slightly exceeding the orbital debris threshold (Martin, 2002). This conclusion was based in part on results of an independent panel during a workshop on benefits of TRMM data in concluded that the benefit of TRMM data in saving lives through its use in operational forecasting could not be quantified. The objective of this paper is to describe a possible technique to estimate the number of lives saved per year and apply it to the TRMM case and the use of its data in monitoring and forecasting tropical cyclones.

Adler, Robert F.↗
DOE OSTI2025

2025 Workshop on Envisioning Frontiers in AI and Computing for Biological Research: Position Papers

This workshop aims to identify key research directions for transforming biology using artificial intelligence (AI), machine learning (ML) and computational methods to facilitate the discovery of new behaviors, mechanisms, and designs of biological processes relevant to DOE missions, underpinning a broader U.S. bioeconomy. By developing novel AI/ML technologies to analyze and interpret complex biological data, researchers can organize and simulate biological processes at various scales as well as advance predictive understanding and manipulation of biological systems. This integration of computation, experimentation, and next-generation experimental technologies can lead to discoveries in new biological behaviors and mechanisms relevant to DOE missions. The focus is on how advanced computational and mathematical methods can impact this mission by exploring digital twins, foundation models, automated laboratory experiments, modeling of complex living systems, and data-driven approaches for the biodesign of plants and microbial systems. While data management is important, it is not the primary focus of this workshop, which will assess the current state, trends, and AI/ML challenges at the interface between biology and computational science to identify opportunities for high-impact research at their intersection. The goal is to define research needs and opportunities that align with biological sciences, computational sciences, and applied mathematics research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

AI-Driven Accelerated Inclusion Analysis for Energy Efficient Steelmaking (Final CRADA Report)

This was a collaborative effort between Lawrence Livermore National Security, LLC (LLNS) as manager and operator of Lawrence Livermore National Laboratory (LLNL) and ArcelorMittal USA Research LLC (“ArcelorMittal” as the Participant), to use scanning electron microscopy (SEM) images, computer vision and machine learning methods, and high-performance computing to accelerate the inclusion analysis process of liquid steel so that new methods can be used for near-real time process control on the shop floor.

36 MATERIALS SCIENCE↗