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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A Scalable Reduced‐Order Model for the Steady Navier–Stokes Equations

Scaling up new scientific technologies from laboratory to industry often involves demonstrating performance on a larger scale. Computer simulations can accelerate design and predictions in the deployment process, though traditional numerical methods are computationally intractable even for intermediate pilot plant scales. Recently, the component reduced order modeling method has been developed to tackle this challenge by combining projection reduced order modeling and discontinuous Galerkin domain decomposition. However, while many scientific or engineering applications involve nonlinear physics, this method has only been demonstrated for various linear systems. In this work, the component reduced order modeling method is extended to steady Navier–Stokes flow, with application to general nonlinear physics in view. The large‐scale, global domain is decomposed into a combination of small‐scale unit component. Linear subspaces for flow velocity and pressure are identified via proper orthogonal decomposition over sample snapshots collected from each small‐scale unit component. Velocity bases are augmented with a pressure supremizer to satisfy the inf–sup condition for stable pressure prediction. Two different nonlinear reduced order modeling methods are employed and compared for efficient evaluation of nonlinear advection: A third‐order tensor projection operator and the empirical quadrature procedure. The proposed method is demonstrated on the flow over arrays of five different unit objects, achieving a 23‐fold speedup with less than 4% relative error in domains up to 256 times larger than the unit components. Furthermore, a numerical experiment with the pressure supremizer strongly indicates the need for a supremizer for stable pressure prediction. A comparison between the tensorial approach and the empirical quadrature procedure revealed a slight advantage of the empirical quadrature procedure. The framework is compared with an alternating Schwarz‐based reduced‐order approach, demonstrating improved efficiency and robustness for the DG‐based global solver while retaining flexibility for sub‐scale iterative solvers. The method is further extended to a coupled advection–diffusion and Navier–Stokes system, illustrating its applicability to multi‐physics problems and its potential for more general, inter‐coupled nonlinear systems.

42 ENGINEERING↗
DOE OSTI2021

Co-design Center for Exascale Machine Learning Technologies (ExaLearn)

We report rapid growth in data, computational methods, and computing power is driving a remarkable revolution in what variously is termed machine learning (ML), statistical learning, computational learning, and artificial intelligence. In addition to highly visible successes in machine-based natural language translation, playing the game Go, and self-driving cars, these new technologies also have profound implications for computational and experimental science and engineering, as well as for the exascale computing systems that the Department of Energy (DOE) is developing to support those disciplines. Not only do these learning technologies open up exciting opportunities for scientific discovery on exascale systems, they also appear poised to have important implications for the design and use of exascale computers themselves, including high-performance computing (HPC) for ML and ML for HPC. The overarching goal of the ExaLearn co-design project is to provide exascale ML software for use by Exascale Computing Project (ECP) applications, other ECP co-design centers, and DOE experimental facilities and leadership class computing facilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A multimodal large language model for materials science

Understanding and predicting the properties of inorganic materials is crucial for accelerating advancements in materials science and driving applications in energy, electronics and beyond. Integrating material structure data with language-based information through multimodal large language models (LLMs) offers great potential to support these efforts by enhancing human–artificial intelligence interaction. However, a key challenge lies in integrating atomic structures at full resolution into LLMs. In this work, we introduce MatterChat, a versatile structure-aware multimodal LLM that unifies material structural data and textual inputs into a single cohesive model. MatterChat uses a bridging module to effectively align a pretrained universal machine learning interatomic potential with a pretrained LLM, reducing training costs and enhancing flexibility. Our results demonstrate that MatterChat greatly improves performance in material property prediction and human–artificial intelligence interaction, surpassing general-purpose LLMs such as GPT-4. We also demonstrate its usefulness in applications such as more advanced scientific reasoning and step-by-step material synthesis.

Tang, Yingheng [Lawrence Berkeley National Laborat↗
DOE OSTI2026

Data Center Market Report

The data center market is poised to explode in the coming decade due to undeniable drivers such as continued adoption of generative AI, increased data storage needs, and enterprise integration of AI in numerous industries [1] [2] [3]. Scalable power and increased computational capacity are at the forefront of considerations for hyperscalers, the major cloud service providers in this space. Lawrence Livermore National Laboratory is uniquely poised to help with informed decision making for data center market leaders during this phase of explosive expansion. National grid modeling expertise and cutting edge innovations in computer cooling systems place LLNL in an enviable position for creating economic impact in the data center industry by leveraging its expertise in these areas which can help the data center market keep up with growing demand.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Molecules Functionalized with Cycling Centers for Quantum Information Science

The ability for a single molecule to produce enough laser-induced fluorescence to control and detect its quantum state is known as optical cycling. This property has been used to great effect in atoms, where the relatively small number of states promotes their ability to repeatedly fluoresce, for applications such as laser cooling, precision measurement, atomic clocks, and quantum information processing. The extension of this capability to molecular species, however, has been difficult, primarily due to their propensity to become vibrationally excited during illumination. This project brought together experts from atomic physics, physical chemistry, and computational quantum chemistry to develop means to create molecules capable of optical cycling. In particular, this program sought to produce an optical cycling center (OCC), a quantum functional group capable of chemical attachment to a wide variety of molecular hosts while retaining (and thereby furnishing to its host) the ability to optically cycle. Following our team’s theoretical identification of promising species, including alkaline-earth phenoxides, we then created these species and in many cases confirmed their optical cycle closure. We now not only know of the existence of a handful of large, molecular species that can be deployed for applications in quantum technologies and measurement, but have improved our knowledge of the molecular mechanisms leading to this discovery, which will enable generalization in the future. The extension of these ideas for informed chemical design of cycling molecules with bespoke properties is now being pursued by multiple groups.

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

Brochure on the 2024 ASCR Workshop on Energy-Efficient Computing for Science

Large-scale computing has enabled numerous scientific discoveries, including ground-breaking achievements facilitated by the US Department of Energy (DOE) supercomputers and advances in applied mathematics and computer science. While important advances were made in energy efficiency to enable exascale computing, continued efforts are needed to dramatically improve the energy efficiency of the next generation of high-performance computing (HPC) systems and, more broadly, AI data centers. Without substantial improvements in energy efficiency, the energy consumption associated with computing could become a limiting factor for future scientific discovery, national security, and technological advancement.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Radiation-Induced Noise Resilience of Neuromorphic Architectures

Neuromorphic event-based networks use asynchronous time-dependent information to extract features from input data that can allow for edge-based distributed applications such as object recognition. The noise resilience properties of such networks, especially in the context of space applications, are yet to be explored. In this paper, we use the hierarchy of time surfaces (HOTS) algorithm, which is one of the neuromorphic algorithms, to understand the least and most resilient modules in a neuromorphic network. The HOTS algorithm relies on the computing of time surfaces that maps the temporal delays between neighboring pixels into normalized features that involve many computations that are also found in other neuromorphic networks such as exponential decays, distance computations, etcetera. We implemented HOTS on a Digilent PYNQ board with a Xilinx Zynq 7020 system on a chip, and we subjected the boards running the HOTS network inference to neutron radiation at the Los Alamos Neutron Science Center. Furthermore, we used simulation models from our previous similar experiments on the event-based sensor to create a neutron induced noise model to quantify the effect of this noise on the overall performance of the network. This experiment provides the preliminary measurements of the reliability of the HOTS algorithm and proposes methods to create a more reliable HOTS architecture in future spacecraft missions.

Engineering↗
DOE OSTI2025

Large‐Scale 2D Perovskite Nanocrystals Photodetector Array via Ultrasonic Spray Synthesis

Abstract 2D perovskite (PVSK) single crystals have received significant attention due to their unique optical and optoelectronic properties. However, current synthesis methods face limitations, particularly in large‐area fabrication, which remain critical barriers to practical applications. In this study, the synthesis of red/green/purple‐blue‐colored 2D PVSK nanocrystals over a large area (4‐inch wafer) and the fabrication of high‐performance photodetector arrays are presented via a facile yet efficient spray‐coating approach with a liquid‐bridge transport effect. The photodetector array achieves 100% working yield, high photo‐responsivity (1.5 × 10 6 A W −1 ) and specific‐detectivity (1.1 × 10 16 Jones) with competitive photomapping characteristics. An intelligent vision system for automatic shape recognition is further demonstrated with a recognition rate exceeding 90%. This study provides significant advances in the scalable synthesis of nanoscale 2D PVSK crystals, their integration into large‐area optoelectronic devices, and their potential use in artificial‐intelligence systems.

Lee, Yoon Ho [Davidson School of Chemical Engineer↗
DOE OSTI2025

Classical Benchmarks for Variational Quantum Eigensolver Simulations of the Hubbard Model

Simulating the Hubbard model is of great interest to a wide range of applications within condensed matter physics, however its solution on classical computers remains challenging in dimensions larger than one. The relative simplicity of this model, embodied by the sparseness of the Hamiltonian matrix, allows for its efficient implementation on quantum computers, and for its approximate solution using variational algorithms such as the variational quantum eigensolver. While these algorithms have been shown to reproduce the qualitative features of the Hubbard model, their quantitative accuracy in terms of producing true ground state energies and other properties, and the dependence of this accuracy on the system size and interaction strength, the choice of variational ansatz, and the degree of spatial inhomogeneity in the model, remains unknown. Here we present a rigorous classical benchmarking study, demonstrating the potential impact of these factors on the accuracy of the variational solution of the Hubbard model on quantum hardware, for systems with up to 32 qubits. We find that even when using the most accurate wavefunction ansätze for the Hubbard model, the error in its ground state energy and wavefunction plateaus for larger lattices, while stronger electronic correlations magnify this issue. Concurrently, spatially inhomogeneous parameters and the presence of off-site Coulomb interactions only have a small effect on the accuracy of the computed ground state energies. Our study highlights the capabilities and limitations of current approaches for solving the Hubbard model on quantum hardware, and we discuss potential future avenues of research.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Li 3.6 In 7 S 11.8 Cl: an air- and moisture-stable superionic conductor

Li 3.6 In 7 S 11.8 Cl has a face-centered cubic arrangement of S 2− /Cl − stabilized by Li + /In 3+ that form 3D ion conduction paths. The moisture stability and fast ion conduction make Li 3.6 In 7 S 11.8 Cl a promising electrolyte for solid-state batteries.

Oyekunle, Ifeoluwa P. [Department of Chemistry and↗
DOE OSTI2025

Mondo: integrating disease terminology across communities

Precision medicine aims to enhance diagnosis, treatment, and prognosis by integrating multimodal data at the point of care. However, challenges arise due to the vast number of diseases, differing methods of classification, and conflicting terminological coding systems and practices used to represent molecular definitions of disease. This lack of interoperability artificially constrains the potential for diagnosis, clinical decision support, care outcome analysis, as well as data linkage across research domains to support the development or repurposing of therapeutics. There is a clear and pressing need for a unified system for managing disease entities⁠—including identifiers, synonyms, and definitions. To address these issues, we created the Mondo disease ontology—a community-driven, open-source, unified disease classification system that harmonizes diverse terminologies into a consistent, computable framework. Mondo integrates key medical and biomedical terminologies, including Online Mendelian Inheritance in Man (OMIM), Orphanet, Medical Subject Headings (MeSH), National Cancer Institute Thesaurus (NCIt), and more, to provide a comprehensive and accurate representation of disease concepts with fully provenanced and attributed links back to the sources. Mondo can be used as the handle for curation of gene–disease associations utilized in diagnostic applications, research applications such as computational phenotyping, and in clinical coding systems in clinical decision support by pointing the clinician to the numerous knowledge resources linked to the Mondo identifier. Mondo's community-centric approach, stewarded by the Monarch Initiative's expertise in ontologies, ensures that the ontology remains adaptable to the evolving needs of biomedical research and clinical communities, as well as the knowledge providers.

biomedical informatics↗
DOE OSTI2025

Warming Permafrost Model Intercomparision Project (WrPMIP): Pan-Arctic Perturbation Warming Simulations

Title: Warming Permafrost Model Intercomparision Project (WrPMIP): Pan-Arctic Perturbation Warming Simulations Description: WrPMIP Pan-Arctic simulations including historial baseline and two warming simulations. Models were perturbed similarly to known experimental warming trials that have been implemented across the Pan-Arctic over the last several decades. More information about the dataset can be found at the following links: https://warmingpermafrost.nau.edu/ The dataset is available at: https://esgf-node.ornl.gov/search/project=WrPMIP&mip_era=CMIP6&activity_id=WrPMIP

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

An electronic microemulsion phase emerging from a quantum crystal-to-liquid transition

Strongly interacting electronic systems often exhibit a complicated phase diagram that results from the competition between different quantum ground states. One feature of these phase diagrams is the emergence of microemulsion phases, where regions of different phases self-organize across multiple length scales. The experimental characterization of these microemulsions can pose considerable challenges, as the long-range Coulomb interaction microscopically mingles with the competing states. Here we observe the signatures of the microemulsion between an electronic Wigner crystal and an electron liquid in a MoSe 2 monolayer using cryogenic reflectance and magneto-optical spectroscopy. We find that the transition into this microemulsion state is marked by anomalies in exciton reflectance, spin susceptibility and umklapp scattering, establishing it as a distinct phase of electronic matter.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Carbon-nitrogen complex as an alternative to the 𝑇 center in Si

A first-principles study of a carbon-nitrogen (CN) impurity complex in silicon is presented as an isoelectronic alternative to the T center [(CCH) Si ]. The latter has been pursued for applications in quantum information science, yet its sensitivity to the presence of hydrogen is still problematic. Our proposed complex has no hydrogen, thereby eliminating this issue. First, we show that the CN complex is stable against decomposition into substitutional and interstitial defects. Next, we show that due to being isoelectronic to the T center, the CN complex has a similar electronic structure, and therefore could be used in similar applications. We assess several low-energy configurations of the CN complex, finding (CN) Si to be stable and have the largest Debye-Waller factor. We predict a zero-phonon line (ZPL) of 828 meV (in the telecom S-band) and a radiative lifetime of 4.2 μs, comparable to the T center. Due to the presence of a bound exciton, choice of the exchange-correlation functional and also supercell-size scaling of the ZPL and transition dipole moment require special scrutiny; we rigorously justify our extrapolation schemes that allow computing values in the dilute limit.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The Oak Ridge National Laboratory Glenn T. Seaborg Institute: 2025 highlights

The Glenn T. Seaborg Institute (GTSI) at Oak Ridge National Laboratory (ORNL) serves as a dedicated hub for advancing actinide R&D, aimed at strengthening the United States’ capabilities in actinide science. With a focus on medical, industrial, and national security applications, the ORNL GTSI seeks to foster the next generation of scientists and engineers. Collaborating closely with corresponding Seaborg Institutes at Lawrence Livermore National Laboratory (LLNL), Lawrence Berkeley National Laboratory (LBNL), Los Alamos National Laboratory (LANL), and Idaho National Laboratory, the ORNL GTSI leverages its unique strengths. These include unparalleled expertise in isotope production, handling of rare isotopes, and leadership in neutron and computational sciences. Notably, the ORNL GTSI is positioned as the nation’s cornerstone for actinide R&D, capitalizing on the exceptional capabilities of the High Flux Isotope Reactor—the premier reactor for isotope production—and the Hazard Category 2 Radiochemical Engineering Development Center, which supports advanced production, processing, and applications research. Furthermore, the GTSI is committed to serving as a national center of excellence for training across all levels of actinide science education. This report provides an overview of the activities, milestones, and accomplishments of the ORNL GTSI over the past year.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Mission Operations Center for Lawrence Livermore National Laboratory Space Science & Security Program

Space mission operations has grown in line with the development of new space and ground technology. Originally, the philosophy for a space mission was to keep the spacecraft as simple as possible and have as much of the mission complexity on the ground where engineers can fix, control and access things. This was mostly due to immature space technologies and associated risk, as well as cost to develop complex and capable ground stations was relatively cheaper than complex space missions. This led to large room withs multiple computers and an army of engineers and operators sitting at computers and monitoring specific spacecraft information during operations. With development of new space technologies, a lot of functions that were kept and constantly monitored on the ground have been automated and completely integrated into the spacecraft, reducing the number of operators and engineers needed to operate a mission. With the paradigm shift in increasing spacecraft technologies, improved computer technologies, and scaling down space mission size; the mission operations centers have also adapted to changes. More organizations are becoming space-faring institutions and have developed mission operations centers for their unique missions.

42 ENGINEERING↗