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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 OSTI2026

Genesis Mission Data cards

As data-intensive research and artificial intelligence become central to DOE mission science, the need for machine-actionable dataset documentation has grown accordingly. However, many DOE-aligned communities, including the Office of Science, NNSA, and cross-laboratory collaborations, have developed independent metadata practices. This fragmentation creates friction for discovery, federation, and reuse across programs. To address these challenges, this talk introduces the Genesis Data Card: a shared metadata artifact developed in collaboration with a broad DOE community (Jefferson Lab and the National Lab of the Rockies, Oak Ridge, Sandia, Idaho, Berkeley, and Los Alamos). The Genesis Data Card aims to standardize dataset documentation across DOE-aligned initiatives while remaining extensible to discipline-specific needs. This talk will describe the data card template and the supporting code to validate completed data cards, using a companion LinkML schema. I'll walk through the design decisions behind the template, its alignment with existing standards, its treatment of sensitivity and governance metadata, and the phased roadmap toward lifecycle-integrated "xCards" that support autonomous discovery and reuse. The talk closes with current gaps, ongoing work, and how others can contribute datasets and feedback to the shared repository.

McSpadden, Helen [Thomas Jefferson National Accele↗
DOE OSTI2026

The forward physics facility: Physics opportunities and conceptual design

The Forward Physics Facility (FPF) is a proposed extension of the HL-LHC program designed to exploit the unique scientific opportunities offered by the intense flux of high energy neutrinos, and possibly new particles, in the far-forward direction. Located in a well-shielded cavern 627 m downstream of one of the LHC interaction points, the facility will support a broad and ambitious physics program that significantly expands the discovery potential of the HL-LHC. Equipped with four complementary detectors -- FLArE, FASER$ν$2, FASER2, and FORMOSA -- the FPF will enable breakthrough measurements that will advance our understanding of neutrino physics, quantum chromodynamics, and astroparticle physics, and will search for dark matter and other new particles. With this Letter of Intent, we propose the construction of the FPF cavern and the construction, integration, and installation of its experiments. We summarize the physics case, the facility design, the layout and components of the detectors, as well as the envisioned collaboration structure, cost estimate, and implementation timeline.

Astroparticle physics↗
DOE OSTI2021

New high-sensitivity searches for neutrons converting into antineutrons and/or sterile neutrons at the HIBEAM/NNBAR experiment at the European Spallation Source

The violation of baryon number, , is an essential ingredient for the preferential creation of matter over antimatter needed to account for the observed baryon asymmetry in the Universe. However, such a process has yet to be experimentally observed. The HIBEAM/NNBAR program is a proposed two-stage experiment at the European Spallation Source to search for baryon number violation. The program will include high-sensitivity searches for processes that violate baryon number by one or two units: free neutron–antineutron oscillation () via mixing, neutron–antineutron oscillation via regeneration from a sterile neutron state (), and neutron disappearance (n → n′); the effective process of neutron regeneration () is also possible. The program can be used to discover and characterize mixing in the neutron, antineutron and sterile neutron sectors. The experiment addresses topical open questions such as the origins of baryogenesis and the nature of dark matter, and is sensitive to scales of new physics substantially in excess of those available at colliders. A goal of the program is to open a discovery window to neutron conversion probabilities (sensitivities) by up to three orders of magnitude compared with previous searches. The opportunity to make such a leap in sensitivity tests should not be squandered. The experiment pulls together a diverse international team of physicists from the particle (collider and low energy) and nuclear physics communities, while also including specialists in neutronics and magnetics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Accelerating Manufacturing of Powder-Processed Strong, Lightweight and Thermally Stable Bulk Nanocrystalline Al Alloys via Engineered Interface States

The overarching goal of this program is to develop an integrated materials by design framework enabling a simple, scalable, and efficient manufacturing route for thermally stable nanocrystalline Al-based alloys with high specific strength over a range of service temperatures. The central scientific principle underpinning this program is the elucidation, and ultimately implementation in novel alloy design, of unique interfacial structures and chemistries driven by segregation in multi-component systems that give rise to unprecedented processability and properties. This program targeted nanocrystalline Al-based ternary alloys produced by mechanical alloying of powders followed by compaction and sintering with hot pressing, which are inherently scalable and inexpensive manufacturing routes, but when applied to nanocrystalline alloys are usually plagued by low density and/or poor retention of the intended nanostructures during processing. This program successfully demonstrated manufacturing of a bulk (>1 cm 3 ) nanocrystalline Al-based alloy with a combination of thermal stability, high temperature strength retention, and specific strength that exceeds the current-state-of-the-art of Al alloys. It is anticipated that the greatest technological impact will occur by demonstration of rapid and scalable manufacturing of bulk nanocrystalline Al alloys with exceptional thermal stability and mechanical performance, thus enabling increased energy efficiency in high performance power generation systems via: (i) replacement of existing components with lightweight Al-based alloys and (ii) increasing operating temperatures with property retention. The program focused on the development of a simple and efficient manufacturing route for thermally stable nanocrystalline Al-based alloys with high specific strength over a range of service temperatures. The program is expected to have direct impact on scalable and economic manufacturing of bulk nanocrystalline alloys. This program profited from the recent discovery by the project team of segregation-driven amorphous intergranular films in nanocrystalline metallic alloys that can be kinetically frozen-in at sub-solidus temperatures. These interfacial states (also known as amorphous complexions) comprise a large fraction of the total material in nanocrystalline alloys and are responsible for two beneficial characteristics that anchor the proposed work: (1) activated sintering leading to high density and (2) retained nanostructures leading to high strengths that approach a substantial fraction of the theoretical limit. More specifically, these engineered interfaces led to a three-fold benefit: (i) high-throughput materials processing with low energy budget and cost, (ii) exceptional thermal stability, and (iii) high specific strength at both room temperature and intermediate temperatures experienced during service.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

The SciDAC QuantOm Framework: A composable Workflow

As part of the Scientific Discovery through Advanced Computing (SciDAC) program, the Quantum Chromodynamics Nuclear Tomography (QuantOM) project aims to analyze data from Deep Inelastic Scattering (DIS) experiments conducted at Jefferson Lab and the upcoming Electron Ion Collider. The DIS data analysis is performed on an event-level by leveraging nuclear theory models and accounting for experimental conditions. In order to efficiently run multiple analyses under varying conditions, a composable workflow was designed where each section (theory, experiment, objective minimization, etc.) has its own dedicated module. The optimization, i.e. the fit of theory to experimental data is carried out by deep learning techniques, such as Generative Adversarial Networks (GANs) or Reinforcement Learning (RL). This presentation gives an overview of the current status of the workflow, highlights present and future challenges, and highlights possible extensions to other projects with similar requirements.

Lersch, Daniel↗
DOE OSTI2024

Scaling the SciDAC QuantOm Workflow

As part of the Scientific Discovery through Advanced Computing (SciDAC) program, the Quantum Chromodynamics Nuclear Tomography (QuantOM) project aims to analyze data from Deep Inelastic Scattering (DIS) experiments conducted at Jefferson Lab and the upcoming Electron Ion Collider. The DIS data analysis is performed on an event-level by combining the input from theoretical and experimental nuclear physics into a single, composable workflow. The optimization itself (I.e. fitting the experimental data with theoretical predictions) is carried out by a machine / deep learning algorithm. The size of the acquired DIS data as well as the complexity of the workflow itself require that the analysis is performed across multiple GPUs on high performance computing systems, such as Polaris at Argonne National Laboratory. This presentation discusses the novelties and challenges that came along with parallelizing this workflow. Recent results are compared to common distributed training techniques.

Lersch, Daniel↗
DOE OSTI2025

SAGIPS: A scalable Framework for scidac quantom

As part of the Scientific Discovery through Advanced Computing (SciDAC) program, the Quantum Chromodynamics Nuclear Tomography (QuantOM) project aims to analyze data from Deep Inelastic Scattering (DIS) experiments conducted at Thomas Jefferson National Accelerator Facility and the upcoming Electron Ion Collider. The DIS data analysis is performed on an event level by taking into leveraging nuclear theory models and accounting for experimental conditions. In order to efficiently run multiple analyses under varying conditions, a composable workflow was designed where each section (theory, experiment, objective minimization, etc.) has its own dedicated module. This presentation gives an overview over of the current status of this workflow, highlights present and future challenges, and highlights possible extensions to other projects with similar requirements.

Lersch, Daniel [Thomas Jefferson National Accelera↗
DOE OSTI2022

Celeritas: GPU-accelerated particle transport for detector simulation in High Energy Physics experiments

Within the next decade, experimental High Energy Physics (HEP) will enter a new era of scientific discovery through a set of targeted programs recommended by the Particle Physics Project Prioritization Panel (P5), including the upcoming High Luminosity Large Hadron Collider (LHC) HL-LHC upgrade and the Deep Underground Neutrino Experiment (DUNE). These efforts in the Energy and Intensity Frontiers will require an unprecedented amount of computational capacity on many fronts including Monte Carlo (MC) detector simulation. In order to alleviate this impending computational bottleneck, the Celeritas MC particle transport code is designed to leverage the new generation of heterogeneous computer architectures, including the exascale computing power of U.S. Department of Energy (DOE) Leadership Computing Facilities (LCFs), to model targeted HEP detector problems at the full fidelity of Geant4. This paper presents the planned roadmap for Celeritas, including its proposed code architecture, physics capabilities, and strategies for integrating it with existing and future experimental HEP computing workflows.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Fusion Energy Sciences Network Requirements Review: Mild-cycle Update

The US Department of Energy (DOE) Office of Science (SC) world-class research infrastructure provides the research community with premier observational, experimental, computational, and network capabilities. Each user facility is designed to provide unique capabilities to advance core DOE mission science for its sponsor SC program and to stimulate a rich discovery and innovation ecosystem. Research communities gather and flourish around each user facility, bringing together diverse perspectives. The continual reinvention of the practice of science — as users and staff forge novel approaches expressed in research workflows — unlocks new discoveries and propels scientific progress.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware

One of the major objectives of the experimental programs at the LHC is the discovery of new physics. This requires the identification of rare signals in immense backgrounds. Using machine learning algorithms greatly enhances our ability to achieve this objective. With the progress of quantum technologies, quantum machine learning could become a powerful tool for data analysis in high energy physics. In this study, using IBM gate-model quantum computing systems, we employ the quantum variational classifier method and the quantum kernel estimator method in two recent LHC flagship physics analyses: $t\bar{t}H$ (Higgs boson production in association with a top quark pair) and $H\rightarrow\mu\mu$ (Higgs boson decays to two muons). We have obtained early results with 10 qubits on the IBM quantum simulator and the IBM quantum hardware. On the quantum simulator, the quantum machine learning methods perform similarly to classical algorithms such as SVM (support vector machine) and BDT (boosted decision tree), which are often employed in LHC physics analyses. On the quantum hardware, the quantum machine learning methods have shown promising discrimination power, comparable to that on the quantum simulator. This study demonstrates that quantum machine learning has the ability to differentiate between signal and background in realistic physics datasets.

Chan, Jay↗
DOE OSTI2022

Snowmass2021 Cosmic Frontier CF6 White Paper: Multi-Experiment Probes for Dark Energy - Transients

This invited Snowmass 2021 White Paper highlights the power of joint-analysis of astronomical transients in advancing HEP Science and presents research activities that can realize the opportunities that come with current and upcoming projects. Transients of interest include gravitational wave events, neutrino events, strongly-lensed quasars and supernovae, and Type~Ia supernovae specifically. These transients can serve as probes of cosmological distances in the Universe and as cosmic laboratories of extreme strong-gravity, high-energy physics. Joint analysis refers to work that requires significant coordination from multiple experiments or facilities so encompasses Multi-Messenger Astronomy and optical transient discovery and distributed follow-up programs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Genetic programming for understanding cognitive biases that generate polarization in social networks

Recent studies have applied agent-based models to infer human-interpretable explanations of individual-scale behaviors that generate macro-scale patterns in complex social systems. Genetic programming has proven to be an ideal explainable AI tool for this purpose, where primitives may be expressed in an interpretable fashion and assembled into agent rules. Evolutionary model discovery (EMD) is a tool that combines genetic programming and random forest feature importance analysis, to infer individual-scale, human-interpretable explanations from agent-based models. We deploy EMD to investigate the cognitive biases behind the emergence of ideological polarization within a population. An agent-based model is developed to simulate a social network, where agents are able to create or sever links with one another, and update an internal ideological stance based on their neighbors' stances. Agent rules govern these actions and constitute of cognitive biases. A set of 7 cognitive biases are included as genetic program primitives in the search for rules that generate hyper-polarization among the population of agents. We find that heterogeneity in cognitive biases is more likely to generate polarized social networks. Highly polarized social networks are likely to emerge when individuals with confirmation bias are exposed to those with either attentional bias, egocentric bias, or cognitive dissonance.

Gunaratne, Chathika↗
DOE OSTI2021

Overview of Nuclear Materials Discovery & Qualification initiative (NMDQi)

The Nuclear Materials Discovery and Qualification Initiative (NMDQi) is a program launched in 2020 to address the need for development of new nuclear materials on shorter timelines. The NMDQi has a goal to accelerate nuclear materials qualification to fulfill the promises of early and advanced reactor technologies as a safe, clean, and low-cost baseload energy. Materials development and qualification in the nuclear industry is by definition challenging due to stringent safety requirements, limited availability of specialized facilities for materials irradiation and testing, and the challenging high-temperature, high-radiation environment. Outside the nuclear industry, options exist for increasing the efficiency and rate of materials discovery for accelerated technology commercialization, which can also be applied for our use through the NMDQi. Focus will be on generating tools and capabilities that integrate experimental and computational techniques, allowing materials to be selected prior to fabrication, and providing crucial data to improve upon modeling.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Navigating Large Chemical Spaces Using Graph Theory and Integer Programming

Navigating and analyzing large chemical spaces are necessary to accelerate the design and discovery of new molecules and chemical processes. In this work, we introduce a computational framework that integrates graph theory and integer programming to enable the efficient navigation of large chemical spaces. Our framework represents the chemical space as a graph, wherein nodes represent molecules and edges represent the degree of similarity or connectivity based on domain-specific information. Using the graph representation, we identify representative molecules by computing the so-called minimum dominating set (MDS), which in our context is the minimum set of molecules that is connected to all other molecules. We present a suite of solution strategies for the MDS problem including heuristic and rigorous integer programming (IP) approaches. We show that these approaches allow us to capture physicochemical properties and domain-specific logic and constraints, facilitating the identification of molecules with the target properties. We demonstrate the effectiveness of the proposed approach by navigating the chemical space of per- and polyfluoroalkyl substances (PFAS); this comprises approximately 15,000 molecular structures. We compare our framework against traditional dimensionality reduction and clustering methods such as t-SNE and K-means clustering.

Chemical structure↗
DOE OSTI2021

High Energy Physics Network Requirements Review (Final Report, July-October 2020)

Throughout 2020, ESnet and the Office of High Energy Physics (HEP) of the DOE SC organized an ESnet requirements review of HEP-supported activities. Preparation for this event included identification of key stakeholders: program and facility management, research groups, technology providers, and a number of external observers. These individuals were asked to prepare formal case study documents about their relationship to the HEP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward. A series of pre-planning meetings better prepared case study authors for this task, along with guidance on how the review would proceed in a virtual fashion.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials (Final Report)

This research program established a transformative framework for the discovery and design of mechanical metamaterials, which are architected structures engineered to control physical phenomena like sound and vibration in ways natural materials cannot. To overcome the traditional reliance on trial-and-error, the project developed an interpretable Artificial Intelligence (AI) framework that moves beyond "black box" models to reveal the specific geometric patterns—such as "unit-cell templates"—that govern a material’s performance. A major breakthrough was the development of a hierarchical design method, which allows a single material to block vibrations across multiple frequency ranges simultaneously by layering patterns at different scales without them interfering with one another. This was further expanded to include irregular, graph-based designs that use spanning tree algorithms to ensure structural connectivity while allowing for customized, direction-dependent properties like stiffness and acoustic impedance. Beyond design, the project addressed the practicalities of real-world production by developing uncertainty quantification techniques that account for manufacturing defects and material variability, reducing the need for expensive physical testing by orders of magnitude. To speed up the discovery process, the team implemented Gaussian Process Regression and other surrogate models that provide accurate performance predictions at a fraction of the traditional computational cost. The AI-generated designs were successfully validated through fabrication of physical samples and wave propagation experiments, confirming their ability to accurately guide or reflect waves as predicted. By contributing these tools and high-quality FAIR benchmark datasets to the wider scientific community, this work provides a scalable foundation for advancing technologies in aerospace vibration control, medical imaging, and noise reduction.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Bridging Place-Based Astrobiology Education with Genomics, Including Descriptions of Three Novel Bacterial Species Isolated from Mars Analog Sites of Cultural Relevance

Democratizing genomic data science, including bioinformatics, can diversify the STEM workforce and may, in turn, bring new perspectives into the space sciences. In this respect, the development of education and research programs that bridge genome science with “place” and world-views specific to a given region are valuable for Indigenous students and educators. Through a multi-institutional collaboration, we developed an ongoing education program and model that includes Illumina and Oxford Nanopore sequencing, free bioinformatic platforms, and teacher training workshops to address our research and education goals through a place-based science education lens. High school students and researchers cultivated, sequenced, assembled, and annotated the genomes of 13 bacteria from Mars analog sites with cultural relevance, 10 of which were novel species. Students, teachers, and community members assisted with the discovery of new, potentially chemolithotrophic bacteria relevant to astrobiology. This joint education-research program also led to the discovery of species from Mars analog sites capable of producing N-acyl homoserine lactones, which are quorum-sensing molecules used in bacterial communication. Whole genome sequencing was completed in high school classrooms, and connected students to funded space research, increased research output, and provided culturally relevant, place-based science education, with participants naming three novel species described here. Students at St. Andrew's School (Honolulu, Hawai‘i) proposed the name Bradyrhizobium prioritasuperba for the type strain, BL16A T , of the new species (DSM 112479 T = NCTC 14602 T ). The nonprofit organization Kauluakalana proposed the name Brenneria ulupoensis for the type strain, K61 T , of the new species (DSM 116657 T = LMG = 33184 T ), and Hawai‘i Baptist Academy students proposed the name Paraflavitalea speifideiaquila for the type strain, BL16E T , of the new species (DSM 112478 T = NCTC 14603 T ).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Unraveling Emergent Quantum States in Magnetic Topological Insulators using High Pressure Neutron Scattering

This proposal aims to uncover novel quantum phenomena under high pressure and understand the pressure effects on the atomic electronic interactions in magnetic topological materials. This program supports a combined neutron scattering and materials discovery of novel magnetic materials that display the coexistence of magnetism plus other physical phenomena such as a non-trivial electronic band topology under high pressure. The goal of this project is to close a long-standing knowledge gap in quantum materials research by developing a high-pressure neutron scattering (HP-NS) toolbox to systematically determine the magnetic structures and electronic interactions present at high pressure.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗