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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Light storage, retrieval, and controllable interference in an atomic tripod system

Highly efficient quantum memories are essential for advancing quantum information processing technologies, including scalable quantum computing and quantum networks. We experimentally demonstrate a light storage and retrieval protocol in a tripod system using an ensemble of laser-cooled 87 Rb atoms. The tripod system, which consists of three ground states and an excited state, offers rich dynamics: its use to coherently store and retrieve a weak probe pulse in the 87 Rb 𝐹 = 1 ground-state manifold leads to the interference of two spin-wave excitations during storage time that translate to an interference in the peak intensity of the retrieved probe pulse. Our work shows that these interferences, which manifest when varying the pulse sequence or energy level structure, can be controlled experimentally by varying the storage time, optical phase, and magnetic field strength. Theoretical simulations exhibit excellent agreement with the experimental results. In conclusion, this work demonstrates the rich dynamics and versatile capabilities of atomic tripod systems for light storage and retrieval, with key advantages over conventional Λ systems, highlighting the potential of atomic tripod systems for applications in quantum information processing, quantum synchronization, and atomic memory protocols.

Coherent control↗
DOE OSTI2024

Robust Measurements of n -Point Correlation Functions of Driven-Dissipative Quantum Systems on a Digital Quantum Computer

We propose and demonstrate a unified hierarchical method to measure n -point correlation functions that can be applied to driven, dissipative, or otherwise open or nonequilibrium quantum systems. In this method, the time evolution of the system is repeatedly interrupted by interacting an ancilla qubit with the system through a controlled operation, and measuring the ancilla immediately afterward. We discuss the robustness of this method as compared to other ancilla-based interferometric techniques (such as the Hadamard test), and highlight its advantages for near-term quantum simulations of open quantum systems. We implement the method on a quantum computer in order to measure single-particle Green’s functions of a driven-dissipative fermionic system. This Letter shows that dynamical correlation functions for driven-dissipative systems can be robustly measured with near-term quantum computers. Published by the American Physical Society 2024

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Learnability Transitions in Monitored Quantum Dynamics via Eavesdropper’s Classical Shadows

Monitored quantum dynamics—unitary evolution interspersed with measurements—has recently emerged as a rich domain for phase structure in quantum many-body systems away from equilibrium. Here we study monitored dynamics from the point of view of an eavesdropper who has access to the classical measurement outcomes, but not to the quantum many-body system. We show that a measure of information flow from the quantum system to the classical measurement record—the —undergoes a phase transition in correspondence with the measurement-induced phase transition (MIPT). This transition determines the eavesdropper’s (in)ability to learn properties of an unknown initial quantum state of the system, given a complete classical description of the monitored dynamics and arbitrary classical computational resources. We make this learnability transition concrete by defining classical shadow protocols that the eavesdropper may apply to this problem, and show that the MIPT manifests as a transition in the sample complexity of various shadow-estimation tasks, which become harder in the low-measurement phase. We focus on three applications of interest: Pauli expectation values (where we find the MIPT appears as a point of optimal learnability for typical Pauli operators), many-body fidelity, and global charge in U ( 1 ) -symmetric dynamics. Our work unifies different manifestations of the MIPT under the umbrella of and gives this notion a general operational meaning via classical shadows. Published by the American Physical Society 2024

Physics↗
DOE OSTI2020

A Benthic Habitat Monitoring Approach for Marine and Hydrokinetic Sites (Final Technical Report)

This final technical report summarizes the work completed as part of the Standardized and Cost-Effective Benthic Habitat Mapping and Monitoring Tools for MHK Environmental Assessments project funded by U.S. Department of Energy (DOE) under contract DE-EE007826 to Integral Consulting Inc. The overall goal of this project was to demonstrate a consistent, repeatable, and semi-automated seafloor and sediment mapping approach for rapidly characterizing benthic physical and biological/habitat conditions to support marine environmental assessments for marine and hydrokinetic energy sites. The approach evaluated combines sediment profile imaging and plan view (SPI/PV) technology with multibeam echosounder surveys as an effective and low-cost benthic habitat mapping protocol. A key innovation was the development of a semi-automated computer vision system that standardizes the extraction of data from the SPI/PV images. Other elements of the project were SPI camera hardware modifications, including the design and fabrication of a prototype “power” SPI camera to improve camera prism penetration in firm substrates, and outreach to agency regulators and other stakeholders on this habitat mapping approach. This technical report consists of five main subsections that summarize: 1) the benthic mapping approach; 2) benthic mapping results from the three areas’ surveys; 3) the development and performance of the image processing algorithms; 4) SPI camera hardware improvements and prototype testing; and 5) the regulatory outreach efforts.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Bridging Cloud and Edge Computing at NREL Using CONNECT: Cloud Optimized Networking for Next-Gen Edge Computing Technologies [Slides]

CONNECT is an innovative on-premise hardware and software solution that integrates edge and cloud computing infrastructure at NREL. Built on the AWS Greengrass middleware and leveraging the MQTT protocol, CONNECT enables real-time data streaming from IoT devices and gateways to both cloud and local services, empowering researchers to rapidly capture, analyze, and act upon edge-generated data while leveraging cloud capabilities. The platform addresses research infrastructure challenges by providing a pre-approved platform which is already configured with the correct networking and cybersecurity baselines thus eliminating procurement delays and enabling on-demand availability. CONNECT's hybrid architecture efficiently manages burstable workloads, allowing research teams to dynamically scale computational capacity, handle peak data loads, and reduce operational bottlenecks. Advanced capabilities include built-in GPU support for executing machine learning models which enables low-latency inference at the edge from models trained in the cloud. This architecture supports real-time analytics and filtering, providing a mechanism to allow only transmitting and processing high-value data. Cloud-based configuration management permits engineers to manage on-premise systems remotely, optimizing operational efficiency. By bridging edge and cloud computing, CONNECT provides NREL researchers with a flexible, scalable platform that accelerates scientific discovery while maintaining robust security and performance standards.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Zero and Finite Temperature Quantum Simulations Powered by Quantum Magic

We introduce a quantum information theory-inspired method to improve the characterization of many-body Hamiltonians on near-term quantum devices. We design a new class of similarity transformations that, when applied as a preprocessing step, can substantially simplify a Hamiltonian for subsequent analysis on quantum hardware. By design, these transformations can be identified and applied efficiently using purely classical resources. In practice, these transformations allow us to shorten requisite physical circuit-depths, overcoming constraints imposed by imperfect near-term hardware. Importantly, the quality of our transformations is t u n a b l e : we define a 'ladder' of transformations that yields increasingly simple Hamiltonians at the cost of more classical computation. Using quantum chemistry as a benchmark application, we demonstrate that our protocol leads to significant performance improvements for zero and finite temperature free energy calculations on both digital and analog quantum hardware. Specifically, our energy estimates not only outperform traditional Hartree-Fock solutions, but this performance gap also consistently widens as we tune up the quality of our transformations. In short, our quantum information-based approach opens promising new pathways to realizing useful and feasible quantum chemistry algorithms on near-term hardware.

Physics↗
DOE OSTI2023

HydroGEN and H2NEW Data Hub

HydroGEN is a multi-lab consortium focused on early-stage R&D in H2 production, supported by the U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy (EERE), Hydrogen and Fuel Cell Technologies Office (HFTO). The consortium advances research and development (R&D) of innovative materials for advanced water splitting (AWS) technologies to enable clean, sustainable and low-cost ($1/kg H2) hydrogen production and fosters cross-cutting innovation using theory-guided applied materials R&D to advance all emerging water-splitting pathways for hydrogen production. Hydrogen from Next-generation Electrolyzers of Water (H2NEW) is a consortium of nine U.S. Department of Energy (DOE) national laboratories focused on making large-scale electrolyzers, which produce hydrogen from electricity and water, more durable, efficient, and affordable. The presentation introduces the HydroGEN data hub to the H2NEW consortium and encourages H2NEW members to use it to store and share data with team members and eventually the public. The two consortia share this data hub.

advanced water splitting technologies↗
DOE OSTI2021

A Contrast Calibration Protocol for X-ray Speckle Visibility Spectroscopy

X-ray free electron lasers, with their ultrashort highly coherent pulses, opened up the opportunity of probing ultrafast nano- and atomic-scale dynamics in amorphous and disordered material systems via speckle visibility spectroscopy. However, the anticipated count rate in a typical experiment is usually low. Therefore, visibility needs to be extracted via photon statistics analysis, i.e., by estimating the probabilities of multiple photons per pixel events using pixelated detectors. Considering the realistic X-ray detector responses including charge cloud sharing between pixels, pixel readout noise, and gain non-uniformity, speckle visibility extraction relying on photon assignment algorithms are often computationally demanding and suffer from systematic errors. In this paper, we present a systematic study of the commonly-used algorithms by applying them to an experimental data set containing small-angle coherent scattering with visibility levels ranging from below 1% to ∼60%. We also propose a contrast calibration protocol and show that a computationally lightweight algorithm can be implemented for high-speed correlation evaluation.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

An Intelligent Distributed Ledger Construction Algorithm for IoT

Blockchain is the next generation of secure data management that creates near-immutable decentralized storage. Secure cryptography created a niche for blockchain to provide alternatives to well-known security compromises. However, design bottlenecks with traditional blockchain data structures scale poorly with increased network usage and are extremely computation-intensive. This made the technology difficult to combine with limited devices, like those in Internet of Things networks. In protocols like IOTA, replacement of blockchain's linked-list queue processing with a lightweight dynamic ledger showed remarkable throughput performance increase. However, current stochastic algorithms for ledger construction suffer distinct trade-offs between efficiency and security. This work proposed a machine-learning approach with a multi-arm bandit that resolved these issues and was designed for auditing on limited devices. This algorithm was tested in a reinforcement-learning environment simulating the IOTA ledger's construction with a decision tree. This study showed through regret analysis and experimentation that this approach was secure against impulse manipulation attacks while remaining energy-efficient. Although the IOTA protocol was a pioneer for lightweight distributed ledgers, it is expected that future blockchain protocols will adopt techniques similar to those presented in this work.

multi-arm bandit↗
DOE OSTI2023

Comparative Assessment of Pose Prediction Accuracy in RNA–Ligand Docking

Structure-based virtual high-throughput screening is used in early-stage drug discovery. Over the years, docking protocols and scoring functions for protein–ligand complexes have evolved to improve the accuracy in the computation of binding strengths and poses. In the past decade, RNA has also emerged as a target class for new small-molecule drugs. However, most ligand docking programs have been validated and tested for proteins and not RNA. Here, we test the docking power (pose prediction accuracy) of three state-of-the-art docking protocols on 173 RNA–small molecule crystal structures. The programs are AutoDock4 (AD4) and AutoDock Vina (Vina), which were designed for protein targets, and rDock, which was designed for both protein and nucleic acid targets. AD4 performed relatively poorly. For RNA targets for which a crystal structure of a bound ligand used to limit the docking search space is available and for which the goal is to identify new molecules for the same pocket, rDock performs slightly better than Vina, with success rates of 48% and 63%, respectively. However, in the more common type of early-stage drug discovery setting, in which no structure of a ligand–target complex is known and for which a larger search space is defined, rDock performed similarly to Vina, with a low success rate of ~27%. Further, Vina was found to have bias for ligands with certain physicochemical properties, whereas rDock performs similarly for all ligand properties. Thus, for projects where no ligand–protein structure already exists, Vina and rDock are both applicable. However, the relatively poor performance of all methods relative to protein–target docking illustrates a need for further methods refinement.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Assessment of Heavy-Duty Fueling Methods and Components

Chevron, NLR, ANL, and NextEnergy partnered in the development of a comprehensive assessment of heavy-duty (HD) fuel cell electric vehicle fueling protocols. The project leveraged and built upon existing international heavy-duty (HD) fueling protocols and fueling component development activities to deliver component performance assessments, modeling tools and methods evaluations, techno-economic assessments of industry-selected protocol structures and experimental validations of the strategies performed at NLR's HD hydrogen fueling station.

08 HYDROGEN↗
DOE OSTI2020

Digital-analog quantum computation

Digital quantum computing paradigm offers highly desirable features such as universality, scalability, and quantum error correction. However, physical resource requirements to implement useful error-corrected quantum algorithms are prohibitive in the current era of NISQ devices. As an alternative path to performing universal quantum computation, within the NISQ era limitations, we propose to merge digital single-qubit operations with analog multiqubit entangling blocks in an approach we call digital-analog quantum computing (DAQC). Along these lines, although the techniques may be extended to any resource, we propose to use unitaries generated by the ubiquitous Ising Hamiltonian for the analog entangling block and we prove its universal character. Here, we construct explicit DAQC protocols for efficient simulations of arbitrary inhomogeneous Ising, two-body, and M-body spin Hamiltonian dynamics by means of single-qubit gates and a fixed homogeneous Ising Hamiltonian. Additionally, we compare a sequential approach where the interactions are switched on and off (stepwise DAQC) with an always-on multiqubit interaction interspersed by fast single-qubit pulses (banged DAQC). Finally, we perform numerical tests comparing purely digital schemes with DAQC protocols, showing a remarkably better performance of the latter. The proposed DAQC approach combines the robustness of analog quantum computing with the flexibility of digital methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Toward a 2D Local Implementation of Quantum Low-Density Parity-Check Codes

Geometric locality is an important theoretical and practical factor for quantum low-density parity-check (qLDPC) codes that affects code performance and ease of physical realization. For device architectures restricted to two-dimensional (2D) local gates, naively implementing the high-rate codes suitable for low-overhead fault-tolerant quantum computing incurs prohibitive overhead. In this work, we present an error-correction protocol built on a bilayer architecture that aims to reduce operational overheads when restricted to 2D local gates by measuring some generators less frequently than others. We investigate the family of bivariate-bicycle qLDPC codes and show that they are well suited for a parallel syndrome-measurement scheme using fast routing with local operations and classical communication (LOCC). Through circuit-level simulations, we find that in some parameter regimes, bivariate-bicycle codes implemented with this protocol have logical error rates comparable to the surface code while using fewer physical qubits. Published by the American Physical Society 2025

Berthusen, Noah (ORCID:0000000275862786)↗
DOE OSTI2026

Transitions, Dynamics, and Driven States in Quantum Magnets (Final Report Revised)

Transitions into unusual electronic, optical, and magnetic states at the absolute zero of temperature display special properties that teach us about fundamental quantum mechanics and also underlie important technologies. These so-called quantum phase transitions intertwine the static and dynamical responses of the materials changing state. They are extremely sensitive to the effects of disorder and etch in high relief the role of quantum fluctuations. There are ample questions remaining about the character of such transitions and the nature of the competing quantum states. There are also opportunities to drive quantum materials out of equilibrium, with the possibility of new types of correlated and coherent order, and new ways to access the dynamical evolution of ground and excited states. We have investigated how the order develops in time and space, and the nature of the final configurations. We are able to compare and contrast classical and quantum excitations in our experimental system of choice, and thereby can trace the relative speed by which the system settles into its lowest-energy ground state. This comparison of quantum and classical annealing protocols lies at the heart of strategies for harnessing the power of quantum computers dedicated to optimization problems. A combination of magnetic susceptibility measurements at finite frequency where the magnetic spins are queried by an external ac field, dc magnetometry measurements that characterize the macroscopic strength of magnetic order and its resistance to change, noise measurements that reveal the microscopic fluctuations of the spins as they prepare to change state, and microwave spectroscopy to look at the response of both the electronic and nuclear degrees of freedom, have been performed as functions of temperature, magnetic field, frequency, excitation amplitude, magnetic spin concentration, and disorder. This combination of probes addresses issues of stability over time, overlap between spin states, the ability of the spins to respond independently or coherently, and the promise of control on the nanometer length scale. The research reported here provides insights into fundamental quantum physics. It also addresses the question of how best to use complex systems, such as magnetic solids, to stably process quantum information.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications

Molecular Dynamics (MD) simulations are used to understand the effects of corrosion on metallic materials in salt brine. Reactive force fields in classical MD enable accurate modeling of bond formation and breakage in the aqueous medium and at the metal-electrolyte interface, while also facilitating dynamic partial charge equilibration. However, MD simulations are computationally intensive and unsuitable for modeling the long time scales characteristic of corrosive phenomena. To address this, we develop reduced-order machine learning models that provide accurate and efficient predictions of charge density in corrosive environments. Specifically, we use Long Short-Term Memory (LSTM) networks to forecast charge density evolution based on atomic environments represented by Smooth Overlap of Atomic Positions (SOAP) descriptors. A physics-informed loss function enforces charge neutrality and electronegativity equivalence. The atomic charges predicted by the deep learning model trained on this work were obtained two orders of magnitude faster than those from molecular dynamics (MD) simulations, with an error of less than 3% compared to the MD-obtained charges, even in extrapolative scenarios, while adhering to physical constraints. This demonstrates the excellent accuracy, computational efficiency, and validity of the developed model. Lastly, even though developed for corrosion, these protocols are formulated in a phenomenon-agnostic manner, allowing application to various variable-charge interatomic potentials and related fields.

Atomistic models↗
DOE OSTI2020

Innovations in Sensors and Controls for Building Energy Management: Research and Development Opportunities Report for Emerging Technologies

Sensors, actuators, and controllers, which collectively serve as the backbone of cyberphysical systems for building energy management, are one of the core technical areas of investment for achieving the U.S. Department of Energy (DOE) Building Technologies Office's (BTO's) goals for energy affordability in the national building stock - both commercial and residential. In fact, an aggregated annual energy savings of 29% is estimated in the commercial sector alone through the implementation of efficiency measures using current state-of-the-art sensors and controls to retune buildings by optimizing programmable settings based on occupant schedules and comfort requirements, as well as detecting and diagnosing equipment operation and installation problems (Fernandez et al. 2017). Monitoring and control of building conditions and operations has advanced significantly, from the invention of the modern thermostat just before the start of the 20th century to the midcentury incorporation of direct digital control into devices, the introduction of open protocols and network communications at the end of the century, and finally the invention of cloud-based computing and additional advancements that have enabled remote operation and a proliferation of connected and intelligent devices in building automation. Despite this potential, however, two main challenges hinder widespread adoption of sensors and controls in building operations that can ensure savings for high-efficiency components and equipment (e.g., heat pumps, windows, and lighting devices), as well as additional savings from more sophisticated control architectures and algorithms. energy savings of 29% is estimated in the commercial sector alone through the implementation of efficiency measures using current state-of-the-art sensors and controls to retune buildings by optimizing programmable settings based on occupant schedules and comfort requirements, as well as detecting and diagnosing equipment operation and installation problems (Fernandez et al. 2017). Monitoring and control of building conditions and operations has advanced significantly, from the invention of the modern thermostat just before the start of the 20th century to the midcentury incorporation of direct digital control into devices, the introduction of open protocols and network communications at the end of the century, and finally the invention of cloud-based computing and additional advancements that have enabled remote operation and a proliferation of connected and intelligent devices in building automation. Despite this potential, however, two main challenges hinder widespread adoption of sensors and controls in building operations that can ensure savings for high-efficiency components and equipment (e.g., heat pumps, windows, and lighting devices), as well as additional savings from more sophisticated control architectures and algorithms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗