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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Correlated States of 2D Electrons near the Landau Level Filling ν = 1 / 7

The ground state of two-dimensional electron systems (2DESs) at low Landau level filling factors ( ν ≲ 1 / 6 ) has long been a topic of interest and controversy in condensed matter. Following the recent breakthrough in the quality of ultrahigh-mobility GaAs 2DESs, we revisit this problem experimentally and investigate the impact of reduced disorder. In a GaAs 2DES sample with density n = 6.1 × 10 10 / cm 2 and mobility μ = 25 × 10 6 cm 2 / V s , we find a deep minimum in the longitudinal magnetoresistance ( R x x ) at ν = 1 / 7 when T ≃ 104 mK . There is also a clear sign of a developing minimum in R x x at ν = 2 / 13 . While insulating phases are still predominant when ν ≲ 1 / 6 , these minima strongly suggest the existence of fractional quantum Hall states at filling factors that comply with the Jain sequence ν = p / ( 2 m p ± 1 ) even in the very low Landau level filling limit. The magnetic-field-dependent activation energies deduced from the relation R x x ∝ e E A / 2 k T corroborate this view and imply the presence of pinned Wigner solid states when ν ≠ p / ( 2 m p ± 1 ) . Similar results are seen in another sample with a lower density, further generalizing our observations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

First Measurement of Drift-Alfvén Wave Polarization in Magnetically Confined Fusion Plasmas

Polarization of drift-Alfvén waves, defined as the ratio of electrostatic to electromagnetic fluctuations, has remained unmeasurable in fusion plasmas for decades, despite its pivotal role in understanding wave dynamics and their impact on plasmas. We report the first measurements of drift-Alfvén wave polarization in a hot, magnetically-confined plasma. Here, the breakthrough is enabled by a novel methodology developed from gyrokinetic theory, utilizing fluctuations of electron temperature and density. Analysis of data from the DIII-D tokamak reveals that the waves above the geodesic acoustic mode frequency exhibit dominant electromagnetic polarization, whereas lower frequency waves show a mix of electromagnetic and electrostatic polarization, indicating a strong coupling between shear Alfvén waves and drift-acoustic waves.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Scattering mechanisms in state-of-the-art GaAs/AlGaAs quantum wells

Motivated by recent breakthroughs in molecular beam epitaxy of GaAs/AlGaAs quantum wells, we examine contributions to mobility and quantum mobility from various scattering mechanisms and their dependencies on the electron density. Here we find that at lower electron densities, n e ≲ 1 x 10 11 cm -2 , both transport and quantum mobility are limited by unintentional background impurities and follow a power-law dependence, ∝ n$^{α}_{e}$, with α ≈ 0.85. Our predictions for quantum mobility are in reasonable agreement with an estimate obtained from the resistivity at filling factor ν = 1/2 in a sample of Y. J. Chung et al. with n e = 1 x 10 11 cm -2 . Consideration of other scattering mechanisms indicates that interface roughness (remote donors) is likely a limiting factor of transport (quantum) mobility at higher electron densities. Future measurements of quantum mobility should yield information on the distribution of background impurities in GaAs and AlGaAs.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Quantum Nature of Dielectric Laser Accelerators

Dielectric laser accelerators (DLAs) hold great promise for producing economic and compact on-chip radiation sources. On-chip DLAs benefit from fabrication capabilities of the silicon industry and from breakthroughs in silicon-photonic nanostructures to enhance the interaction between particles and laser fields. Seemingly unrelated recent advances in the quantum interactions of electrons and light have raised interest in the underlying classical-quantum correspondence principle at the foundations of electron acceleration. Here, we present the observation of the underlying quantum nature of DLAs: observing quantized peaks in the electron-energy spectra. Our findings demonstrate quasi-phase-matching between an electron wave function and a light wave, which also demonstrates the role of the quantum wave function in the inverse Smith-Purcell effect. We harness the capabilities of an ultrafast transmission electron microscope (UTEM) to maintain a long electron-light interaction length extending over hundreds of periods of the laser pulse, mediated by a silicon-photonic nanograting DLA. The UTEM is shown as a new platform for characterization of future DLA concepts. The results raise fundamental questions regarding the role of quantum mechanics in DLA design, and more generally about the prospects of manipulating particles’ quantum wave functions in accelerator physics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Theory for Equivariant Quantum Neural Networks

Quantum neural network architectures that have little to no inductive biases are known to face trainability and generalization issues. Inspired by a similar problem, recent breakthroughs in machine learning address this challenge by creating models encoding the symmetries of the learning task. This is materialized through the usage of equivariant neural networks the action of which commutes with that of the symmetry. In this work, we import these ideas to the quantum realm by presenting a comprehensive theoretical framework to design equivariant quantum neural networks (EQNNs) for essentially any relevant symmetry group. We develop multiple methods to construct equivariant layers for EQNNs and analyze their advantages and drawbacks. Our methods can find unitary or general equivariant quantum channels efficiently even when the symmetry group is exponentially large or continuous. As a special implementation, we show how standard quantum convolutional neural networks (QCNNs) can be generalized to group-equivariant QCNNs where both the convolution and pooling layers are equivariant to the symmetry group. We then numerically demonstrate the effectiveness of a S U ( 2 ) -equivariant QCNN over symmetry-agnostic QCNN on a classification task of phases of matter in the bond-alternating Heisenberg model. Our framework can be readily applied to virtually all areas of quantum machine learning. Lastly, we discuss about how symmetry-informed models such as EQNNs provide hopes to alleviate central challenges such as barren plateaus, poor local minima, and sample complexity. Published by the American Physical Society 2024

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Practical Introduction to Benchmarking and Characterization of Quantum Computers

Rapid progress in quantum technology has transformed quantum computing and quantum information science from theoretical possibilities into tangible engineering challenges. Breakthroughs in quantum algorithms, quantum simulations, and quantum error correction are bringing useful quantum computation closer to fruition. These remarkable achievements have been facilitated by advances in quantum characterization, verification, and validation (QCVV). QCVV methods and protocols enable scientists and engineers to scrutinize, understand, and enhance the performance of quantum information-processing devices. In this tutorial, we review the fundamental principles underpinning QCVV, and introduce a diverse array of QCVV tools used by quantum researchers. We define and explain QCVV’s core models and concepts—quantum states, measurements, and processes—and illustrate how these building blocks are leveraged to examine a target system or operation. We survey and introduce protocols ranging from simple qubit characterization to advanced benchmarking methods. Along the way, we provide illustrated examples and detailed descriptions of the protocols, highlight the advantages and disadvantages of each, and discuss their potential scalability to future large-scale quantum computers. This tutorial serves as a guidebook for researchers unfamiliar with the benchmarking and characterization of quantum computers, and also as a detailed reference for experienced practitioners.

open quantum systems & decoherence↗
DOE OSTI2021

Modern and Future Colliders

Since the initial development of charged particle colliders in the middle of the 20th century, these advanced scientific instruments have been at the forefront of scientific discoveries in high energy physics over the past 60 years. Collider accelerator technology and beam physics have progressed immensely and modern facilities now operate at energies and luminosities many orders of magnitude greater than the pioneering colliders of the early 1960s. In addition, the field of colliders remains extremely dynamic and continues to develop many innovative approaches. Indeed, several novel concepts are currently being considered for designing and constructing even more powerful future colliders. In this paper, we first review the colliding beam method and the history of colliders, and then present, in detail, the major achievements of operational machines and the key features of near-term collider projects that are currently under development. We conclude with an analysis of numerous proposals and studies for far-future colliders. The evaluation of their respective potentials reveals tantalizing prospects for further significant breakthroughs in the collider field.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Electrical control of magnetism by electric field and current-induced torques

The remanent magnetization of ferromagnets has long been studied and used to store binary information. While early magnetic memory designs relied on magnetization switching by locally generated magnetic fields, key insights in condensed matter physics later suggested the possibility of doing it by electrical means instead. In the 1990s, Slonczewski and Berger formulated the concept of current-induced spin torques in magnetic multilayers through which a spin-polarized current generated by a first ferromagnet may be used to switch the magnetization of a second one. This discovery drove the development of spin-transfer-torque magnetic random-access memories (MRAMs). More recent fundamental research revealed other types of current-induced torques named spin-orbit torques (SOTs) and will lead to a new generation of devices including SOT MRAMs and skyrmion-based devices. Parallel to these advances, multiferroics and their magnetoelectric coupling, first investigated experimentally in the 1960s, experienced a renaissance. Dozens of multiferroic compounds with new magnetoelectric coupling mechanisms were discovered and high-quality multiferroic films were synthesized (notably of BiFeO 3 ), also leading to novel device concepts for information and communication technology such as the magnetoelectric spin-orbit (MESO) transistor. The story of the electrical switching of magnetization, which is discussed in this review, is that of a dance between fundamental research (in spintronics, condensed matter physics, and materials science) and technology (MRAMs, MESO transistors, microwave emitters, spin diodes, skyrmion-based devices, components for neuromorphics, etc.). This pas de deux has led to major scientific and technological breakthroughs in recent decades (such as the conceptualization of pure spin currents, the observation of magnetic skyrmions, and the discovery of spin-charge interconversion effects). As a result, this field has not only propelled MRAMs into consumer electronics products but also fueled discoveries in adjacent research areas such as ferroelectrics or magnonics. Here, in this review, recent advances in the control of magnetism by electric fields and by current-induced torques are covered. Fundamental concepts in these two directions are reviewed first, their combination is then discussed, and finally current various families of devices harnessing the electrical control of magnetic properties for various application fields are addressed. The review concludes by giving perspectives in terms of both emerging fundamental physics concepts and new directions in materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Over 8 hours of continuous operation of a free-electron laser driven by a laser-plasma accelerator

Since the emergence of laser-plasma accelerators (LPAs), substantial work has been dedicated to using LPAs to drive free-electron lasers (FELs) for a broad range of applications. Despite recent breakthroughs, which have proven the fundamental feasibility of operating FELs with an LPA source, stable FEL operation over multiple hours without operator input had yet to be achieved. In this work, we report significant improvements to the stability of a hundred terawatt laser system, resulting in successful demonstration of reliable, long-term operation of an LPA-driven FEL in the self-amplified spontaneous emission (SASE) regime at 420 nm. The LPA source delivered 100 MeV electron beams at 1 Hz with high stability over more than 10 h, enabling over 8 h of continuous FEL operation without operator input. The acquired data were subsequently used to investigate correlations between the measured undulator radiation and parameters of the drive laser, plasma source, and electron beam. The revealed connections between LPA and FEL performance gave important additional insights into ways to further improve and stabilize the system, thus demonstrating the capability of our setup to serve as a powerful platform for future studies of LPA-driven FEL operation. The one-of-a-kind integration of multiple stabilization concepts onto the LPA facility discussed in this manuscript yielded significant accelerator and light source improvements (with residual correlations suggesting even more is possible), which will positively impact LPA applications at large.

Kohrell, Finn [Lawrence Berkeley National Laborato↗
DOE OSTI2022

VWC-BERT: Scaling Vulnerability–Weakness–Exploit Mapping on Modern AI Accelerators

Defending cybersystems needs accurate mapping of software and hardware vulnerabilities to generalized descriptions of weaknesses, and weaknesses to exploits. These mappings enable cyber defenders to build plans for effective defense and assessment of potential risks to a cybersystem. With close to 170k vulnerabilities, manual mapping is not a feasible option. However, automated mapping is challenging due to limited training data, computational intractability, and limitations in computational natural language processing. Tools based on breakthroughs in Transformer-based language models have been demonstrated to classify vulnerabilities with high accuracy. We make three key contributions in this paper: (1) We present a new framework, \VWCBERT, that augments the Transformer-based hierarchical multi-class classification framework of Das et al. (\textsc{V2W-BERT}) with the ability to map weaknesses to exploits. (2) We implement \VWCBERT~ on modern AI accelerator platforms using two data parallel techniques for the pre-training phase and demonstrate nearly linear speedups across NVIDIA and Graphcore accelerator platforms. We observe nearly linear speedups for up to 16 V100 and 8 A100 GPUs, and about 3.4$\times$ speedup for A100 relative to V100 GPUs. We also observe excellent speedups on Graphcore, with $5.7\times$ speedup on 128 IPUs relative to 16 IPUs. Enabled by scaling, we also demonstrate higher accuracy using a larger language model, RoBERTa-Large. We show up to 87\% accuracy for strict and up to 98\% accuracy for relaxed classification. (3) We develop a novel parallel link manager for the link prediction phase and demonstrate up to 21$\times$ speedup with 16 V100 GPUs relative to one V100 GPU, and thus reducing the runtime from 2.5 hours to 10 minutes. We believe that generalizability and scalability of \VWCBERT~ will benefit both the theoretical development and practical deployment of novel cyberdefense solutions and vulnerability classification.

Das, Siddhartha Shankar↗
DOE OSTI2022

Accelerating Scientific Workflows on HPC Platforms with In Situ Processing

Scientific workflows drive most modern large-scale science breakthroughs by allowing scientists to define their computations as a set of jobs executed in a given order based on their data dependencies. Workflow management systems (WMSs) have become key to automating scientific workflows-executing computational jobs and orchestrating data transfers between those jobs running on complex high-performance computing (HPC) platforms. Traditionally, WMSs use files to communicate between jobs: a job writes out files that are read by other jobs. However, HPC machines face a growing gap between their storage and compute capabilities. To address that concern, the scientific community has adopted a new approach called in situ, which bypasses costly parallel filesystem I/O operations with faster in-memory or in-network communications. When using in situ approaches, communication and computations can be interleaved. In this work, we leverage the Decaf in situ dataflow framework to accelerate task-based scientific workflows managed by the Pegasus WMS, by replacing file communications with faster MPI messaging. We propose a new execution engine that uses Decaf to manage communications within a sub-workflow (i.e., set of jobs) to optimize inter-job communications. We consider two workflows in this study: (i) a synthetic workflow that benchmarks and compares file- and MPI-based communication; and (ii) a realistic bioinformatics workflow that computes mu-tational overlaps in the human genome. Experiments show that in situ communication can improve the bioinformatics workflow execution time by 22% to 30% compared with file communication. Our results motivate further opportunities and challenges for bridging traditional WMSs with in situ frameworks.

Decaf↗
DOE OSTI2025

Scaling Ultrahigh-Resolution E3SM Land Model for Leadership-Class Supercomputers

This paper presents advancements in scaling the ultrahigh-resolution E3SM Land Model (uELM) for deployment on leadership-class supercomputers, addressing the increased demand for km-scale Earth system modeling. By focusing on km-scale ELM simulations, we enhance predictive capabilities for climate interactions, facilitating improved responses to climate change impacts on energy systems, agriculture, and water resources. Our approach leverages innovative software architecture optimizations, sophisticated data handling techniques, and advanced parallel processing, achieving strong scalability on two leadership supercomputers (2400 nodes (105,600 cores) on Summit, and 1200 nodes (76,800 cores) on Frontier). Results from extensive scalability assessments on the Summit and Frontier also demonstrate outstanding I/O performance (close to 400 GB/s write throughput) and the model's ability to efficiently handle increasing computational demands. This study not only establishes uELM's capability for high-resolution simulations over vast geographical domains, but also sets a foundation for future Earth system modeling breakthroughs.

Wang, Dali [ORNL] (ORCID:0000000168065108)↗
DOE OSTI2021

Blockchain-Based Man-in-the-Middle (MITM) Attack Detection for Photovoltaic Systems

Cybersecurity of photovoltaic (PV) systems entails a much larger scope than just encryption and firewall of communications. For instance, integrity of data in transit between inverters and a cloud server can be compromised by authorized third-party, devices, and internal network within security perimeter (i.e., man-in-the-middle (MITM) attack). To address this challenge, this paper proposes a blockchain-based MITM attack detection method for a PV system. A breakthrough method includes screening network data, network intrusion detection, and hash comparison of in-transit data using distributed ledgers. Furthermore, the proposed method is implemented in Internet-of-Thing (IoT) security modules as clients of a blockchain network and validated by experiments.

blockchain↗
DOE OSTI2025

Reinforcement Learning-Based Oscillation Dampening: Scaling Up Single-Agent Reinforcement Learning Algorithms to a 100-Autonomous-Vehicle Highway Field Operational Test

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. Here, we explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

The Scientific Impact of the Exascale Computing Project

The recent arrival of the Frontier Supercomputer at Oak Ridge National Laboratory officially marked the dawn of the exascale computing era. Its successful deployment coincided with the culmination of the U.S. Department of Energy Exascale Computing Project (ECP), an ambitious, complex, and risky research and development effort that integrated contributions from a broad and diverse subset of the high-performance computing community. The success of ECP will ultimately be judged by the scientific and engineering advances that it enabled. In conclusion, this Special Issue is focused on showcasing early successes in the use of exascale resources to enable breakthroughs in key areas of science in engineering.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Unifying Framework to Enable Artificial Intelligence in High-Performance Computing Workflows

Current trends point to a future where large-scale scientific applications are tightly coupled high-performance computing/artificial intelligence (HPC/AI) hybrids. Hence, we urgently need to invest in creating a seamless, scalable framework where HPC and AI/machine learning can efficiently work together and adapt to novel hardware and vendor libraries without starting from scratch every few years. Finally, the current ecosystem and sparsely connected community are not sufficient to tackle these challenges, and we require a breakthrough catalyst for science similar to what PyTorch enabled for AI.

high-performance computing↗
DOE OSTI2024

ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability

Earth system predictability is challenged by the complexity of environmental dynamics and the multitude of variables involved. Current AI foundation models, although advanced by leveraging large and heterogeneous data, are often constrained by their size and data integration, limiting their effectiveness in addressing the full range of Earth system prediction challenges. To overcome these limitations, we introduce the Oak Ridge Base Foundation Model for Earth System Predictability (ORBIT), an advanced vision transformer model that scales up to 113 billion parameters using a novel hybrid tensor-data orthogonal parallelism technique. As the largest model of its kind, ORBIT surpasses the current climate AI foundation model size by a thousandfold. Performance scaling tests conducted on the Frontier supercomputer have demonstrated that ORBIT achieves 684 petaFLOPS to 1.6 exaFLOPS sustained throughput, with scaling efficiency maintained at 41% to 85% across 49,152 AMD GPUs. These breakthroughs establish new advances in AI-driven climate modeling and demonstrate promise to significantly improve the Earth system predictability.

Wang, Xiao↗
DOE OSTI2026

The High Rigidity Spectrometer at the FRIB: Magnet Development Status

This paper presents the 2025 progress of the High Rigidity Spectrometer (HRS) project at the Facility for Rare Isotope Beams (FRIB). We report on the construction and testing of the first-article HRS-High Transmission BeamLine (HTBL) magnets. An HTBL dipole magnet has successfully completed cold testing, achieving full magnetic performance without quenching, and is now ready for field mapping at FRIBs offline test bench. HTBL quadrupole triplet coils have been fabricated and tested, with all coils reaching their design currents, some following initial training quenches. Furthermore, the preliminary design of the large-scale, large-aperture SPectrometer Section (SPS) magnets has progressed to determine the baseline of the downstream HRS-SPS subproject in 2025. This includes the introduction of novel magnet designs, such as the sweeper dipole magnet, the sector dipole magnet, and iron-free coil-dominated quadrupole magnets, representing a significant scale breakthrough and a first for FRIB.

Choi, Yoonhyuck↗