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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Toward Quantum Confinement in Graphitic Carbon Nitride-Based Polymeric Monolayers

raphitic carbon nitride (g-C 3 N 4 ) has garnered much attention due to its potential as an efficient metal-free photocatalyst. This study examines the evolution of properties in zero-dimensional quantum dots up to sizable clusters that mimic extended g-C 3 N 4 monolayers. We employ density functional theory to investigate systematically the structural, electronic, and optical properties of the g-C 3 N 4 -based melamine and heptazine building blocks using a “bottom-up” construction of polymeric monolayers. The results from our computations indicate that the melamine- and heptazine-based polymeric g-C 3 N 4 systems must be reduced to at least 2.74 and 4.00 nm, respectively, to observe an increase of its optical gap with a size reduction. The present study also examines the nature of the electronic transitions exhibited by g-C 3 N 4 -based monolayers through full natural transition orbital and density of state analyses. Furthermore, the most promising sites for water splitting and subsequent chemical doping studies are identified, which generally correspond to the nitrogen and carbon atoms, respectively.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Hybrid quantum-classical algorithms for approximate graph coloring

We show how to apply the recursive quantum approximate optimization algorithm (RQAOA) to MAX- k -CUT, the problem of finding an approximate k -vertex coloring of a graph. We compare this proposal to the best known classical and hybrid classical-quantum algorithms. First, we show that the standard (non-recursive) QAOA fails to solve this optimization problem for most regular bipartite graphs at any constant level p : the approximation ratio achieved by QAOA is hardly better than assigning colors to vertices at random. Second, we construct an efficient classical simulation algorithm which simulates level- 1 QAOA and level- 1 RQAOA for arbitrary graphs. In particular, these hybrid algorithms give rise to efficient classical algorithms, and no benefit arising from the use of quantum mechanics is to be expected. Nevertheless, they provide a suitable testbed for assessing the potential benefit of hybrid algorithm: We use the simulation algorithm to perform large-scale simulation of level- 1 QAOA and RQAOA with up to 300 qutrits applied to ensembles of randomly generated 3 -colorable constant-degree graphs. We find that level- 1 RQAOA is surprisingly competitive: for the ensembles considered, its approximation ratios are often higher than those achieved by the best known generic classical algorithm based on rounding an SDP relaxation. This suggests the intriguing possibility that higher-level RQAOA may be a potentially useful algorithm for NISQ devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Investigating parameter trainability in the SNAP-displacement protocol of a qudit system

Here, in this study, we explore the universality of Selective Number-dependent Arbitrary Phase (SNAP) and Displacement gates for quantum control in qudit-based systems. However, optimizing the parameters of these gates poses a challenging task. Our main focus is to investigate the sensitivity of training any of the SNAP parameters in the SNAP-Displacement protocol. We analyze conditions that could potentially lead to the Barren Plateau problem in a qudit system and draw comparisons with multi-qubit systems. The parameterized ansatz we consider consists of blocks, where each block is composed of hardware operations, namely SNAP and Displacement gates [Fösel et al 2020 Efficient cavity control with snap gates arXiv:2004.14256]. Applying Variational Quantum algorithm (VQA) with observable and gate cost functions, we utilize techniques similar to those in [McClean et al 2018 Barren plateaus in quantum neural network training landscapes Nat. Commun.9 1–6] and [Cerezo et al 2021 Cost function dependent barren plateaus in shallow parametrized quantum circuits. Nat. Commun.12 1–12] along with the concept of t − design. Through this analysis, we make the following key observations: (a) The trainability of a SNAP-parameter does not exhibit a preference for any particular direction within our cost function landscape, (b) By leveraging the first and second moments properties of Haar measures, we establish new lemmas concerning the expectation of certain polynomial functions, and (c) utilizing these new lemmas, we identify a general condition that indicates an expected trainability advantage in a qudit system when compared to multi-qubit systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Controlling interactions between high-frequency phonons and single quantum systems using phononic crystals

The ability to control phonons in solids is key in many fields of quantum science, ranging from quantum information processing to sensing. Phonons often act as a source of noise and decoherence when solid-state quantum systems interact with the phonon bath of their host matrix. In this study, we demonstrate the ability to control the phononic local density of states of the host matrix using phononic crystals and measure its positive impact on single quantum systems. We design and fabricate diamond phononic crystals with features down to around 20 nm, resulting in a high-frequency complete phononic bandgap from 50 to 70 GHz. The engineered local density of states is probed using single silicon-vacancy colour centres embedded in the phononic crystals. We observe an 18-fold reduction in the phonon-induced orbital relaxation rate of the emitters compared to bulk, thereby demonstrating that the phononic crystal suppresses spontaneous single-phonon processes. Furthermore, we show that our approach can efficiently suppress single-phonon-emitter interactions up to 20 K, allowing the investigation of multi-phonon processes in the emitters. Our results represent an important step towards the realization of efficient phonon-emitter interfaces that can be used for quantum acoustodynamics and quantum phononic networks.

quantum information↗
DOE OSTI2020

Supported and coordinated single metal site electrocatalysts

The ever-increasing global environmental and energy crisis issues necessitates technological innovation, especially in the development of renewable energy-related devices, such as electrochemical energy conversion and storage technologies, including fuel cells, water electrolyzers, and CO 2 electrolyzers. Reliable and sustainable energy conversion devices are highly dependent on engineering of electrocatalysts. Furthermore, state-of-the-art electrocatalysts for these electrochemical conversion systems are usually platinum group metal (PGM)-based nanoparticles with high cost, which has sparked intensive research on atomically dispersed single metal site electrocatalysts for decreasing metal loadings and boosting catalytic efficiencies by taking advantage of their inherent electronic effects, quantum size effects, and metal-support interactions. In this review, we first introduced the intrinsic active site identification and stabilization, followed by addressing the mutual metal-support interactions, and most important, their correlations with catalytic properties. Next, the advances in synthetic strategies and characterization techniques for single metal site electrocatalysts are highlighted. Additionally, recent advances in single metal site electrocatalysts designs for applications in electrochemical conversion reactions are also presented. Finally, remaining challenges and a forward-looking perspective on this field of research are provided.

25 ENERGY STORAGE↗
DOE OSTI2023

Simulation studies related to the particle identification by the forward and backward RICH detectors at Electron Ion Collider

The Electron-Ion collider (EIC) will be the ultimate facility to study the dynamics played by colored quarks and gluons in the phenomenology of nucleons and nuclei, described by Quantum Chromodynamics. The physics programs will greatly rely on efficient particle identification (PID) in both the forward and the backward regions. The forward and the backward RICHes of the EIC have to be able to cover wide acceptance and momentum ranges; in the forward region a dual radiator RICH (dRICH) is foreseen and in the backward region a proximity-focusing RICH can be foreseen to be employed. The geometry and the performance studies of the dRICH have been performed as prescribed in the EIC Yellow Report using the ATHENA software framework. Furthermore, this part of our work reports the effort following the call for EIC detector proposal and the studies related to the forward and the backward RICH performance. In the forward region, the dRICH performance showed a pion- kaon separation from around 1 GeV/c to 50 GeV/c at a three sigma level; the proximity focusing RICH (pfRICH) foreseen for the backward region can reach three sigma separation up to 3 GeV/c for e/$π$ and up to 10 GeV/c for $π$/K mass hypotheses.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Unraveling Adsorbate-Induced Structural Evolution of Iron Carbide Nanoparticles

Iron carbide (Fe x C y ) nanoparticles (NPs) are promising candidates for replacing platinum group metals in industrial applications, such as high-temperature Fischer–Tropsch synthesis. However, due to their amorphous nature, characterization of the active sites has been challenging experimentally and computationally. Here, using a combined density functional theory (DFT), neural network interatomic potential-assisted global optimization, and ensemble learning study, we evaluate dynamic surface changes associated with syngas (H and CO) interactions. For this purpose, we have developed a general procedure that we use to model an experimentally relevant 270-atom Fe 182 C 88 NP using the neural network-assisted stochastic surface walk global optimization algorithm (SSW-NN). Once generated, the Fe 182 C 88 NP active sites and particle morphology are thoroughly characterized before the effects of syngas adsorbate interactions are explored by using DFT and molecular dynamics simulations. Lastly, we explore correlations between geometric and electronic features of the active sites and the adsorption of H (H ads ), using a regularized random forest machine learning algorithm. In doing so, we identified the Fe–C coordination number and p orbital occupancy as the most important descriptors affecting H ads . Furthermore, using a combined ML and quantum chemistry approach, our work demonstrates a general and efficient procedure for generating and probing complex surface phenomena on binary nanoparticles.

Adsorption↗
DOE OSTI2021

Direct evidence of microstructure dependence of magnetic flux trapping in niobium

Abstract Elemental type-II superconducting niobium is the material of choice for superconducting radiofrequency cavities used in modern particle accelerators, light sources, detectors, sensors, and quantum computing architecture. An essential challenge to increasing energy efficiency in rf applications is the power dissipation due to residual magnetic field that is trapped during the cool down process due to incomplete magnetic field expulsion. New SRF cavity processing recipes that use surface doping techniques have significantly increased their cryogenic efficiency. However, the performance of SRF Nb accelerators still shows vulnerability to a trapped magnetic field. In this manuscript, we report the observation of a direct link between flux trapping and incomplete flux expulsion with spatial variations in microstructure within the niobium. Fine-grain recrystallized microstructure with an average grain size of 10–50 µm leads to flux trapping even with a lack of dislocation structures in grain interiors. Larger grain sizes beyond 100–400 µm do not lead to preferential flux trapping, as observed directly by magneto-optical imaging. While local magnetic flux variations imaged by magneto-optics provide clarity on a microstructure level, bulk variations are also indicated by variations in pinning force curves with sequential heat treatment studies. The key results indicate that complete control of the niobium microstructure will help produce higher performance superconducting resonators with reduced rf losses 1 related to the magnetic flux trapping.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Full characterization of ultrathin 5-nm low-k dielectric bilayers: Influence of dopants and surfaces on the mechanical properties

Ultrathin films and multilayers, with controlled thickness down to single atomic layers, are critical for advanced technologies ranging from nanoelectronics to spintronics to quantum devices. However, for thicknesses less than 10 nm, surfaces and dopants contribute significantly to the film properties, which can differ dramatically from that of bulk materials. Therefore, for amorphous films being developed as low dielectric constant interfaces for nanoelectronics, the presence of surfaces or dopants can soften films and degrade their mechanical performance. Here we use coherent short-wavelength light to fully and nondestructively characterize the mechanical properties of individual films as thin as 5 nm within a bilayer. In general, we find that the mechanical properties depend both on the amount of doping and the presence of surfaces. In very thin (5-nm) silicon carbide bilayers with low hydrogen doping, surface effects induce a substantial softening - by almost an order of magnitude - compared with the same doping in thicker (46-nm) bilayers. These findings are important for informed design of ultrathin films for a host of nano- and quantum technologies, and for improving the switching speed and efficiency of next-generation electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Observation of thermally activated coherent magnon-magnon coupling in a magnonic hybrid system

In this work, we experimentally demonstrate coherent magnon-magnon coupling by thermal spin excitations in yttrium iron garnet/permalloy (YIG/Py) hybrid structures using microfocused Brillouin light scattering—an optical technique that enables the detection of zero-wave-vector and higher-order wave-vector spin waves in a broad frequency range. The thermally activated magnons in the bilayer lead to a hybrid excitation between magnon modes in the conductive Py layer with a wide wave-vector range and the first perpendicular standing magnon modes in the insulating YIG layer, facilitated by strong interfacial exchange coupling. To further investigate this coupling, we compare the thermal magnon spectra with the results obtained from electrical excitation and detection methods, which primarily detect the uniform Py mode. The realization of coherent coupling between incoherent (thermal) magnons is important for advancing energy-efficient magnonic devices, particularly in classical as well as quantum spin-wave computing technologies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Analytic continuation of noisy data using Adams Bashforth residual neural network

We propose a data-driven learning framework for the analytic continuation problem in numerical quantum many-body physics. Designing an accurate and efficient framework for the analytic continuation of imaginary time using computational data is a grand challenge that has hindered meaningful links with experimental data. The standard Maximum Entropy (MaxEnt)-based method is limited by the quality of the computational data and the availability of prior information. Also, the MaxEnt is not able to solve the inversion problem under high level of noise in the data. Here we introduce a novel learning model for the analytic continuation problem using a Adams-Bashforth residual neural network (AB-ResNet). Additionally, the advantage of this deep learning network is that it is model independent and, therefore, does not require prior information concerning the quantity of interest given by the spectral function. More importantly, the ResNet-based model achieves higher accuracy than MaxEnt for data with higher level of noise. Finally, numerical examples show that the developed AB-ResNet is able to recover the spectral function with accuracy comparable to MaxEnt where the noise level is relatively small.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Magnetic flux distribution, quasiparticle spectroscopy, and quality factors in Nb films for superconducting qubits

Niobium is a practical material platform for superconducting microwave circuits; however, device-level performance can vary significantly depending on film growth and processing conditions. We compare three epitaxial Nb films grown on $c-$plane sapphire substrates under nominally identical conditions, except for the deposition temperature. To correlate internal quality factors, $Q_{\mathrm {i}}$, with material properties, we combine magneto-optical imaging of magnetic flux distribution with quasiparticle spectroscopy via measurements of the London penetration depth, $λ(T)$. In the low-$Q_{\mathrm i}$ film, there is a lesser ability to screen the magnetic field and an irregular temperature variation of $λ(T)$, implying the existence of localized in-gap states. High $Q_{\mathrm i}$ films show the opposite trend. We conclude that our measurements provide an efficient method for characterizing and optimizing superconducting films for quantum informatics applications.

Datta, Amlan [Ames Lab; Iowa State U.]↗
DOE OSTI2025

A cross-platform execution engine for the quantum intermediate representation

Hybrid languages like the quantum intermediate representation (QIR) are essential for programming systems that mix quantum and conventional computing models, while execution of these programs is often deferred to a system-specific implementation. Here, we develop the QIR Execution Engine (QIR-EE) for parsing, interpreting, and executing QIR across multiple hardware platforms. QIR-EE uses LLVM to execute hybrid instructions specifying quantum programs and, by design, presents extension points that support customized runtime and hardware environments. We demonstrate an implementation that uses the XACC quantum hardware-accelerator library to dispatch prototypical quantum programs on different commercial quantum platforms and numerical simulators, and we validate execution of QIR-EE on IonQ, Quantinuum, and IBM hardware. Our results highlight the efficiency of hybrid executable architectures for handling mixed instructions, managing mixed data, and integrating with quantum computing frameworks to realize cross-platform execution.

LLVM↗
DOE OSTI2020

Mercury Chalcogenide Nanoplatelet–Quantum Dot Heterostructures as a New Class of Continuously Tunable Bright Shortwave Infrared Emitters

Despite broad applications in imaging, energy conversion, and telecommunications, few nanoscale moieties emit light efficiently in the shortwave infrared (SWIR, 1000–2000 nm or 1.24–0.62 eV). We report quantum-confined mercury chalcogenide (HgX, where X = Se or Te) nanoplatelets (NPLs) can be induced to emit bright (QY > 30%) and tunable (900–1500+ nm) infrared emission from attached quantum dot (QD) “defect” states. Here, we demonstrate near unity energy transfer from NPL to these QDs, which completely quench NPL emission and emit with a high QY through the SWIR. This QD defect emission is kinetically tunable, enabling controlled midgap emission from NPLs. Spectrally resolved photoluminescence demonstrates energy-dependent lifetimes, with radiative rates 10–20 times faster than those of their PbX analogues in the same spectral window. Coupled with their high quantum yield, midgap emission HgX dots on HgX NPLs provide a potential platform for novel optoelectronics in the SWIR.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation

Molecular conformer generation (MCG) is an important task in cheminformatics and drug discovery. The ability to efficiently generate low-energy 3D structures can avoid expensive quantum mechanical simulations, leading to accelerated virtual screenings and enhanced structural exploration. Several generative models have been developed for MCG, but many struggle to consistently produce high-quality conformers for meaningful downstream applications. To address these issues, we introduce CoarsenConf, which coarse-grains molecular graphs based on torsional angles and integrates them into an SE(3)-equivariant hierarchical variational autoencoder. Through equivariant coarse-graining, we aggregate the fine-grained atomic coordinates of subgraphs connected via rotatable bonds, creating a variable-length coarse-grained latent representation. Our model uses a novel aggregated attention mechanism to restore fine-grained coordinates from the coarse-grained latent representation, enabling efficient generation of accurate conformers. Furthermore, we evaluate the chemical and biochemical quality of our generated conformers on multiple downstream applications, including property prediction and large-scale oracle-based protein docking. Overall, CoarsenConf generates more accurate conformer ensembles compared to prior generative models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Intricate short-range order in GeSn alloys revealed by atomistic simulations with highly accurate and efficient machine-learning potentials

GeSn alloys hold promise for silicon-compatible integrated applications in electronics, photonics, and topological quantum devices. However, understanding their intricate structures using density functional theory (DFT) calculations is hindered by spatiotemporal constraints. To overcome this limitation, we develop highly accurate and efficient machine-learning interatomic potentials based on a neuroevolution potential approach with farthest point sampling on a comprehensive DFT data set. The application of the developed machine-learning potential in large-scale atomistic simulations bridges the spatiotemporal gap between modeling and advanced characterizations, and facilitates the discovery of structural intricacies in GeSn alloys. Through extensive statistical sampling, we identify a type of short-range order (SRO) that is distinguished by both its structural signature and electronic band gap from the SRO structure previously predicted. Modeling based on a large simulation cell reveals the coexistence of nano SRO domains with various degrees of ordering, demonstrating a complex spatial heterogeneity of SRO structure. Finally, our study not only reinforces the significance of fine-level structural information in alloys, but it also constitutes an effective framework for exploring SRO in a broad range of complex alloys based on highly accurate and effective machine-learning potentials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Scheme for stabilising the phase and arrival time of ultrashort laser pulses in a coherent beam combining fibre system

An algorithm has been demonstrated for simultaneously measuring the phase and arrival time of optical pulses relative to pulses in a reference channel using one detector array. Based on this algorithm, a simple optoelectronic pulse phase and arrival time stabilisation scheme has been designed for operation in a coherent beam combining fibre system for ultrashort pulses (including chirped ones). We have studied the performance of the stabilisation scheme. In particular, we have reached a phase measurement rate of 25 000 cps, demonstrated an ∼ 2 kHz limiting phase stabilisation frequency in a feedback loop, and obtained an arrival time measurement accuracy of ∼ 5 % of the transform-limited pulse duration at a measurement rate of 5 cps. In experiments concerned with coherent beam combining for two channels of a pulsed fibre system at a wavelength of 1030 nm, we have demonstrated phase stabilisation with an accuracy better than λ/100 and delay setting accuracy of 10 fs at a transform-limited pulse duration of ∼ 200 fs and obtained 94 % beam combining efficiency. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗