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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 OSTI2024

Hong-Ou-Mandel interference of single-photon-level pulses stored in independent room-temperature quantum memories

Abstract Quantum repeater networks require independent absorptive quantum memories capable of storing and retrieving indistinguishable photons to perform high-repetition entanglement swapping operations. The ability to perform these coherent operations at room temperature is of prime importance for the realization of scalable quantum networks. We perform Hong-Ou-Mandel (HOM) interference between photonic polarization states and single-photon-level pulses stored and retrieved from two sets of independent room-temperature quantum memories. We show that the storage and retrieval of polarization states from quantum memories does not degrade the HOM visibility for few-photon-level polarization states in a dual-rail configuration. For single-photon-level pulses, we measure the HOM visibility with various levels of background in a single polarization, single-rail QM, and investigate its dependence on the signal-to-background ratio. We obtain an HOM visibility of 43%, compared to the 48% no-memory limit of our set-up. These results allow us to estimate a 33% visibility for polarization qubits under the same conditions. These demonstrations lay the groundwork for future applications using large-scale memory-assisted quantum networks.

Physics↗
DOE OSTI2023

Signal Processing Based Method for Real-Time Anomaly Detection in High-Performance Computing

Performance anomalies can manifest as irregular execution times or abnormal execution events for many reasons, including network congestion and resource contention. Detecting such anomalies in real-time by analyzing the details of performance traces at scale is impractical due to the sheer volume of data High-Performance Computing (HPC) applications produce. In this paper, we propose formulating HPC performance anomaly detection as a signal-processing problem where anomalies can be treated as noise. We evaluate our proposed method in comparison with two other commonly used anomaly detection techniques of varying complexity based on their detection accuracy and scalability. Since real-time in-situ anomaly detection at a large scale requires lightweight methods that can handle a large volume of streaming data, we find that our proposed method provides the best trade-off. We then implement the proposed method in Chimbuko, the first online, distributed, and scalable workflow-level performance trace analysis framework. We compare our proposed signal-based anomaly detection algorithm with two other methods using a function of their accuracy, F1 score, and detection overhead. Our experiments demonstrate that our proposed approach achieves a 99% improvement for the benchmark datasets and a 93% improvement with Chimbuko traces.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

TAMM: Tensor algebra for many-body methods

Tensor algebra operations such as contractions in computational chemistry consume a significant fraction of the computing time on large-scale computing platforms. The widespread use of tensor contractions between large multi-dimensional tensors in describing electronic structure theory has motivated the development of multiple tensor algebra frameworks targeting heterogeneous computing platforms. In this paper, we present Tensor Algebra for Many-body Methods (TAMM), a framework for productive and performance-portable development of scalable computational chemistry methods. TAMM decouples the specification of the computation from the execution of these operations on available high-performance computing systems. With this design choice, the scientific application developers (domain scientists) can focus on the algorithmic requirements using the tensor algebra interface provided by TAMM, whereas high-performance computing developers can direct their attention to various optimizations on the underlying constructs, such as efficient data distribution, optimized scheduling algorithms, and efficient use of intra-node resources (e.g., graphics processing units). The modular structure of TAMM allows it to support different hardware architectures and incorporate new algorithmic advances. We describe the TAMM framework and our approach to the sustainable development of scalable ground- and excited-state electronic structure methods. We present case studies highlighting the ease of use, including the performance and productivity gains compared to other frameworks.

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

Quantum Electrodynamics Coupled-Cluster at Scale: High-Performance Implementation for Complex Systems

Coupled-cluster theory (CC) is a highly accurate and versatile method for simulating complex interactions within quantum systems. The extension of CC theory to model mixed electron-photon processes with quantum electrodynamics (QED) has improved our capability to predict cavity-modified chemistry, a field where photons are used as cost-effective and eco-friendly alternatives to catalyze/inhibit chemical reactions. However, calculations with CC methods, even without incorporating QED effects, are often prohibitively expensive. Simulations of larger systems require scalable infrastructures that exist for traditional CC methods but not for QED-CC methods. As such, we present a GPU-enabled, high-performance, open-source implementation of the quantum electrodynamics coupled-cluster method with single and double excitations (QED-CCSD) within the ExaChem quantum chemistry software package. ExaChem relies on the Tensor Algebra for Many-body Methods (TAMM) infrastructure: a parallel heterogeneous tensor library designed to achieve scalable performance on modern heterogeneous supercomputing platforms. Furthermore, we discuss theoretical foundations, algorithmic details, and numerical benchmarks to showcase the larger systems that ExaChem can simulate and how the integration of photonic degrees-of-freedom alters their ground-state properties.

Basis sets↗
DOE OSTI2022

Hydrothermal synthesis of Co-free NMA cathodes for high performance Li-ion batteries

Scalable and sustainable production of high voltage cathodes is required to meet the increasing demands for Li-ion batteries. Additionally, the anticipated scarcity of critical materials like cobalt necessitates demonstration of Co-free alternatives that can match the performance metrics of conventional cathodes. Herein, a hydrothermal synthesis route for production of a new class of high-capacity, cobalt-free cathode material, LiNi 0.9 Mn 0.05 Al 0.05 O 2 (NMA9055) for next-generation Li-ion batteries is reported. In this work, the synthesized cathode material shows high crystallinity and purity with monodispersed spherical morphology. Extensive electrochemical, structural, and post-mortem characterization of this novel NMA material is carried out. NMA-Li half cells show an initial discharge capacity of 200 mAh/g with a 96% capacity retention over 100 cycles when cycled between 3.0 and 4.4 V. On the other hand, NMA full cells with Li 4 Ti 5 O 12 (LTO) electrodes as the anode, show an initial discharge capacity of 186 mAh/g with 81% capacity retention over 200 cycles. Post-mortem structural and morphological characterization show that the NMA morphology and crystal structure do not degrade significantly over 200 charge/discharge cycles. This new class of cobalt free cathode material containing nickel, manganese and aluminum synthesized by an ammonia-free synthesis route is expected to provide a facile solution towards sustainable cathode production.

25 ENERGY STORAGE↗
DOE OSTI2025

Preventing native oxide formation in niobium thin films through platinum encapsulation

Superconducting transmon qubits are a key technology in quantum computing, with niobium (Nb) widely used due to its favorable superconducting properties and compatibility with scalable fabrication. However, the performance of Nb-based qubits is hindered by two-level systems (TLS), which arise predominantly from native oxide layers at surfaces and interfaces. These oxides and suboxides form rapidly in air and contribute significantly to decoherence by introducing dielectric losses. While various mitigation strategies, including thermal-annealing, chemical-etching, and nitrogen plasma treatment, have been explored, many are challenging to integrate into large-scale manufacturing due to complexity and instability in ambient conditions. In this study, we investigate the structural, chemical, and superconducting properties of room-temperature sputtered Nb thin films grown on sapphire substrates. We focus on a surface protection strategy using noble metal encapsulation with platinum (Pt) to suppress oxides and reduce TLS-related losses. Structural and chemical characterization were performed using X-ray diffraction, X-ray photoelectron spectroscopy, and transmission electron microscopy. Superconducting properties were measured using a physical property measurement system. Results demonstrate that Pt encapsulation effectively suppresses surface oxidation and mitigates its detrimental impact on superconducting performance. In conclusion, this approach offers a promising path toward enhancing the coherence of Nb-based qubits while remaining compatible with scalable-fabrication processes.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Tailoring Crystallization Dynamics of CsPbI 3 for Scalable Production of Efficient Inorganic Perovskite Solar Cells

All-inorganic perovskite cesium lead triiodide (CsPbI 3 ) with inorganic nature, low-temperature synthesis, and a suitable bandgap is desirable for high-performance photovoltaics. However, the scalable production of CsPbI 3 photovoltaics is still challenging due to a large nucleation energy barrier and slow phase transition during unassisted natural crystallization. Here, the crystallization dynamics of CsPbI 3 thin films is tailored via lead acetate (PbAc 2 ) substitution in the perovskite precursor ink, allowing the scalable fabrication of efficient all-inorganic perovskite solar cells and minimodules. Introducing PbAc 2 enlarges CsPbI 3 colloid size in the precursor and reduces the nucleation energy barrier. Additionally, reactions between acetate and dimethylammonium in the wet film accelerate the removal of dimethylammonium additives and generate solvent vapors for self-regulate internal solvent annealing, resulting in densely packed, uniform, and pinhole-free CsPbI 3 perovskite films over large areas. This strategy demonstrates inverted CsPbI 3 solar cells with 20.17% efficiency and good operational stability (retaining 95.5% of initial efficiency after continuous operation for 1800 h) and 15.1%-efficient CsPbI 3 minimodules with an active area of 26.8 cm 2 .

36 MATERIALS SCIENCE↗
DOE OSTI2022

Zeolitic Imidazolate Framework Membranes: Novel Synthesis Methods and Progress Toward Industrial Use

In the last decade, zeolitic imidazolate frameworks (ZIFs) have been studied extensively for their potential as selective separation membranes. In this review, we highlight unique structural properties of ZIFs that allow them to achieve certain important separations, like that of propylene from propane, and summarize the state of the art in ZIF thin-film deposition on porous substrates and their modification by postsynthesis treatments. We also review the reported membrane performance for representative membrane synthesis approaches and attempt to rank the synthesis methods with respect to potential for scalability. To compare the dependence of membrane performance on membrane synthesis methods and operating conditions, we map out fluxes and separation factors of selected ZIF-8 membranes for propylene/propane separation. Finally, we provide future directions considering the importance of further improvements in scalability, cost effectiveness, and stable performance under industrially relevant conditions.

Chemistry↗
DOE OSTI2025

Evaluating Function-as-a-Service (FaaS) frameworks for the Accelerator Control System

As particle accelerator control systems evolve in complexity and scale, the need for responsive, scalable, and cost-effective computational infrastructure becomes increasingly critical. Function-as-a-Service (FaaS) offers an alternative to traditional monolithic architecture by enabling event-driven execution, automatic scaling, and fine-grained resource utilization. This paper explores the applicability and performance of FaaS frameworks in the context of a modern particle accelerator control system, with the objective of evaluating their suitability for short lived and triggered workloads. In this paper, we evaluate prominent open-source FaaS platforms in executing functional logic, triggers, and diagnostics routines. Evaluation metrics consist of cold-start latency, scalability, performance, integration with other open-source tools like Kafka. Experimental workloads were designed to simulate real-world control tasks when implemented as stateless FaaS functions. These workloads were benchmarked under various invocation loads and network conditions. Self-hosted FaaS platforms, when deployed within accelerator networks, offer greater control over execution environment, better integration with legacy systems, and support for real-time guarantees when paired with message queues. Based on lessons learned and evaluation metrics, this paper describes reliability of the FaaS framework for the Accelerator Control Systems (ACS).

Jaikar, A. [Fermilab] (ORCID:0000000332046217)↗
DOE OSTI2023

Report for the ASCR Workshop on Basic Research Needs in Quantum Computing and Networking - 2023

Employing quantum mechanical resources in computing and networking opens the door to new computation and communication models and potential disruptive advantages over classical counterparts. However, quantifying and realizing such advantages face extensive scientific and engineering challenges. Investments by the Department of Energy (DOE) have driven progress toward addressing such challenges. Quantum algorithms have been recently developed, in some cases offering asymptotic exponential advantages in speed or accuracy, for fundamental scientific problems such as simulating physical systems, solving systems of linear equations, or solving differential equations. Empirical demonstrations on nascent quantum hardware suggest better performance than classical analogs on specialized computational tasks favorable to the quantum computing systems. However, demonstration of an end-to-end, substantial and rigorously quantifiable quantum performance advantage over classical analogs remains a grand challenge, especially for problems of practical value. The definition of requirements for quantum technologies to exhibit scalable, rigorous, and transformative performance advantages for practical applications also remains an outstanding open question, namely, what will be required to ultimately demonstrate practical quantum advantage?

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Report for the ASCR Workshop on Basic Research Needs in Quantum Computing and Networking - 2023

Employing quantum mechanical resources in computing and networking opens the door to new computation and communication models and potential disruptive advantages over classical counterparts. However, quantifying and realizing such advantages face extensive scientific and engineering challenges. Investments by the Department of Energy (DOE) have driven progress toward addressing such challenges. Quantum algorithms have been recently developed, in some cases offering asymptotic exponential advantages in speed or accuracy, for fundamental scientific problems such as simulating physical systems, solving systems of linear equations, or solving differential equations. Empirical demonstrations on nascent quantum hardware suggest better performance than classical analogs on specialized computational tasks favorable to the quantum computing systems. However, demonstration of an end-to-end, substantial and rigorously quantifiable quantum performance advantage over classical analogs remains a grand challenge, especially for problems of practical value. The definition of requirements for quantum technologies to exhibit scalable, rigorous, and transformative performance advantages for practical applications also remains an outstanding open question, namely, what will be required to ultimately demonstrate practical quantum advantage?

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

SV-Sim: Scalable PGAS-based State Vector Simulation of Quantum Circuits

High-performance quantum circuit simulation in a classic HPC is still imperative in the NISQ era. Observing that the major obstacle of scalable state-vector quantum simulation arises from the massively fine-grained irregular data-exchange with remote nodes, in this paper we present SV-Sim to apply the emerging PGAS-based communication models (i.e., direct peer access for intra-node CPUs/GPUs and SHMEM for inter-node CPU/GPU clusters) for efficient scalable quantum circuit simulation. Through an orchestrated device functional pointer design, SV-Sim is able to abstract the quantum gate sets across various heterogeneous backends, including IBM/Intel/AMD CPUs, NVIDIA /AMD GPUs, and Intel MIC, in a unified framework, but still asserting outstanding performance and tractable interface to higher-level quantum programming environments, such as IBM Qiskit, Microsoft Q\# and Google Cirq. Circumventing the disability of polymorphism in GPUs and leveraging the device-initiated one-sided communication, SV-Sim can process dynamically synthesized quantum circuit in a single GPU/CPU kernel without the need of expensive JIT or runtime branching, significantly improving the performance and simplifying the programming complexity for the emerging variational quantum algorithms. Evaluations on NVIDIA A100-DGX-1, V100-DGX-2, AMD MI100, ALCF Theta, and OLCF Summit HPCs show that SV-Sim can delivery scalable performance on various state-of-the-art HPC platforms, offering a useful tool for quantum algorithm validation and verification.

Li, Ang↗
DOE OSTI2023

Basic Research Needs in Quantum Computing and Networking

Employing quantum mechanical resources in computing, information processing, and networking opens the door to potential exponential advantages over classical counterparts. However, quantifying and realizing such advantages poses extensive scientific and engineering challenges. Department of Energy (DOE) investments have driven steady progress in addressing such challenges. Recently developed quantum algorithms offer asymptotic exponential advantages in speed or accuracy for fundamental scientific problems. These problems include simulating physical systems, solving systems of linear equations, differential equations, and optimization problems. Empirical demonstrations on nascent quantum hardware suggest better performance on contrived computational tasks than classical analogs. However, the requirements for a quantum computer or network to demonstrate an end-to-end rigorously quantifiable performance improvement over classical analogs remains a grand challenge, especially for problems of practical value. In particular, what will be required for quantum technology to ultimately exhibit scalable, rigorous, and transformative performance advantages for practical applications? In July 2023, DOE’s Advanced Scientific Computing Research program in the Office of Science convened the Workshop on Basic Research Needs in Quantum Computing and Networking, where major opportunities and grand challenges were identified. The following five priority research directions (PRDs) were identified as a result of the workshop.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Enhanced thermoelectric performance of PbSe-graphene nanocomposite manufactured with acoustic cavitation induced defects

An order of magnitude rise in the thermoelectric (TE) performance of the PbSe, a scalable and easy-to-manufacture TE material, has been achieved by incorporating reduced graphene oxide (Gr) nanoplatelets in a PbSe/PbSeO 3 heterostructure formed by acoustic cavitation-assisted oxidation. The fabricated Gr/PbSe/PbSeO 3 nanocomposites exhibit high TE performance with an exceptionally high Seebeck coefficient coupled with low thermal conductivity. The variation in the Seebeck coefficient has been attributed to a reduction in charge carrier mobility due to the ferroelectric polarization effect. Furthermore, the increase in electrical resistivity is minimized by adding graphene. At an optimal weight fraction (0.2 wt%), graphene nano-inclusions lead to superior Seebeck coefficient values as high as ~2000 μV/K at ~500 K, providing high overall TE performance. This study shows substantial changes in the TE properties of PbSe through the incorporation of graphene and PbSeO 3 . The understanding and methodology developed in this study can be exploited for the scalable manufacturing of high-performance TE materials.

Structure-property correlations↗
DOE OSTI2023

Aligned Conjugated Polymer Nanofiber Networks in an Elastomer Matrix for High-Performance Printed Stretchable Electronics

Conjugated polymer films are promising in wearable X-ray detection. However, achieving optimal film microstructure possessing good electrical and detection performance under large deformation via scalable printing remains challenging. Herein, we report bar-coated high-performance stretchable films based on a conjugated polymer P(TDPP-Se) and elastomer SEBS blend by optimizing the solution-processing conditions. The moderate preaggregation in solution and prolonged growth dynamics from a solvent mixture with limited dissolving capacity is critical to forming aligned P(TDPP-Se) chains/crystalline nanofibers in the SEBS phase with enhanced π–π stacking for charge transport and stress dissipation. The film shows a large elongation at break of >400% and high mobilities of 5.29 cm 2 V –1 s –1 at 0% strain and 1.66 cm 2 V –1 s –1 over 500 stretch–release cycles at 50% strain, enabling good X-ray imaging with a high sensitivity of 1501.52 μC Gy air –1 cm –2 . Finally, our work provides a morphology control strategy toward high-performance conjugated polymer film-based stretchable electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Early Performance Results on 4th Gen Intel(R) Xeon (R) Scalable Processors with DDR and Intel(R) Xeon(R) processors, codenamed Sapphire Rapids with HBM

The Crossroads supercomputer was designed to simulate some of the most complex physical devices in the world. These simulations routinely require 1/2 petabyte or more of system memory running on thousands of compute nodes for months at a time on the most powerful supercomputers. Improvements in time to solutions for these workloads can have major impact on our mission capabilities. In this paper we present early results of representative application workloads on 4th Gen Intel Xeon and Intel Xeon Processors codenamed Sapphire Rapids with HBM. These results demonstrate an extremely promising 8.57x improvement (node to node) over our prior generation Intel Broadwell (BDW) based HPC systems. No code modifications were required to achieve this speedup, providing a compelling path forward toward major reductions in time to solution and the complexity of physical systems that can be simulated in the future.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Scalable deep learning for watershed model calibration

Watershed models such as the Soil and Water Assessment Tool (SWAT) consist of high-dimensional physical and empirical parameters. These parameters often need to be estimated/calibrated through inverse modeling to produce reliable predictions on hydrological fluxes and states. Existing parameter estimation methods can be time consuming, inefficient, and computationally expensive for high-dimensional problems. In this paper, we present an accurate and robust method to calibrate the SWAT model (i.e., 20 parameters) using scalable deep learning (DL). We developed inverse models based on convolutional neural networks (CNN) to assimilate observed streamflow data and estimate the SWAT model parameters. Scalable hyperparameter tuning is performed using high-performance computing resources to identify the top 50 optimal neural network architectures. We used ensemble SWAT simulations to train, validate, and test the CNN models. We estimated the parameters of the SWAT model using observed streamflow data and assessed the impact of measurement errors on SWAT model calibration. We tested and validated the proposed scalable DL methodology on the American River Watershed, located in the Pacific Northwest-based Yakima River basin. Our results show that the CNN-based calibration is better than two popular parameter estimation methods (i.e., the generalized likelihood uncertainty estimation [GLUE] and the dynamically dimensioned search [DDS], which is a global optimization algorithm). For the set of parameters that are sensitive to the observations, our proposed method yields narrower ranges than the GLUE method but broader ranges than values produced using the DDS method within the sampling range even under high relative observational errors. The SWAT model calibration performance using the CNNs, GLUE, and DDS methods are compared using R 2 and a set of efficiency metrics, including Nash-Sutcliffe, logarithmic Nash-Sutcliffe, Kling-Gupta, modified Kling-Gupta, and non-parametric Kling-Gupta scores, computed on the observed and simulated watershed responses. The best CNN-based calibrated set has scores of 0.71, 0.75, 0.85, 0.85, 0.86, and 0.91. The best DDS-based calibrated set has scores of 0.62, 0.69, 0.8, 0.77, 0.79, and 0.82. The best GLUE-based calibrated set has scores of 0.56, 0.58, 0.71, 0.7, 0.71, and 0.8. The scores above show that the CNN-based calibration leads to more accurate low and high streamflow predictions than the GLUE and DDS sets. Our research demonstrates that the proposed method has high potential to improve our current practice in calibrating large-scale integrated hydrologic models.

54 ENVIRONMENTAL SCIENCES↗