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At least 289 records · Page 16

Windowed multipole representation of R -matrix cross sections

Nuclear cross sections are basic inputs to any nuclear computation. Campaigns of experiments are fitted with the parametric R-matrix model of quantum nuclear interactions, and the resulting cross sections are documented—both pointwise and as resonance parameters (with uncertainties)—in standard evaluated nuclear data libraries (ENDF, JEFF, BROND, JENDL, CENDL, TENDL): these constitute our common knowledge of fundamental low-energy nuclear cross sections. In the past decade, a collaborative effort has been deployed to establish a new nuclear cross-section library format—the Windowed Multipole Library—with the goal of considerably reducing the computational cost of cross-section calculations in nuclear transport simulations. This work lays the theoretical foundations underpinning these efforts. From general R-matrix scattering theory, we derive the windowed multipole representation of nuclear cross sections. Though physically and mathematically equivalent to R-matrix cross sections, the windowed multipole representation is particularly well suited for subsequent temperature treatment of angle-integrated cross sections, in particular Doppler broadening, which is the averaging of cross sections over the thermal motion of the target atoms. Doppler broadening is of critical importance in neutron transport applications, as it ensures the stability of many nuclear reactors (negative thermal reactivity). Yet, Doppler broadening of nuclear cross sections has been a considerable bottleneck for nuclear transport computations, often requiring memory-costly pretabulations. We show that the windowed multipole representation can perform accurate Doppler broadening analytically (up to the first reaction threshold), from which we derive cross-section temperature derivatives to any order—all computable on the fly (without precalculations stored in memory). Furthermore, we here establish a way of converting the R-matrix resonance parameters uncertainty (covariance matrices) into windowed multipole parameters uncertainty. We show that generating stochastic nuclear cross sections by sampling from the resulting windowed multipole covariance matrix can reproduce the cross-section uncertainty in the original nuclear data file. The windowed multipole representation is therefore a novel nuclear physics formalism able to generate Doppler broadened stochastic nuclear cross sections on the fly, unlocking breakthrough computational gains for nuclear computations. Through this foundational paper, we hope to make the windowed multipole representation accessible, reproducible, and usable for the nuclear physics community, as well as provide the theoretical basis for future research on expanding its capabilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

2019 Lighting R&D Opportunities

BTO’s Lighting R&D Opportunities is updated annually and covers foundational R&D needs for lighting technology and lighting application understanding that will result in maximum energy savings while also optimizing for building occupant well-being and productivity.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Foundations of machine learning for low-temperature plasmas: methods and case studies

Abstract Machine learning (ML) and artificial intelligence have proven to be an invaluable tool in tackling a vast array of scientific, engineering, and societal problems. The main drivers behind the recent proliferation of ML in practically all aspects of science and technology can be attributed to: (a) improved data acquisition and inexpensive data storage; (b) exponential growth in computing power; and (c) availability of open-source software and resources that have made the use of state-of-the-art ML algorithms widely accessible. The impact of ML on the field of low-temperature plasmas (LTPs) could be particularly significant in the emerging applications that involve plasma treatment of complex interfaces in areas ranging from the manufacture of microelectronics and processing of quantum materials, to the LTP-driven electrification of the chemical industry, and to medicine and biotechnology. This is primarily due to the complex and poorly-understood nature of the plasma-surface interactions in these applications that pose unique challenges to the modeling, diagnostics, and predictive control of LTPs. As the use of ML is becoming more prevalent, it is increasingly paramount for the LTP community to be able to critically analyze and assess the concepts and techniques behind data-driven approaches. To this end, the goal of this paper is to provide a tutorial overview of some of the widely-used ML methods that can be useful, amongst others, for discovering and correlating patterns in the data that may be otherwise impractical to decipher by human intuition alone, for learning multivariable nonlinear data-driven prediction models that are capable of describing the complex behavior of plasma interacting with interfaces, and for guiding the design of experiments to explore the parameter space of plasma-assisted processes in a systematic and resource-efficient manner. We illustrate the utility of various supervised, unsupervised and active learning methods using LTP datasets consisting of commonly-available, information-rich measurements (e.g. optical emission spectra, current–voltage characteristics, scanning electron microscope images, infrared surface temperature measurements, Fourier transform infrared spectra). All the ML demonstrations presented in this paper are carried out using open-source software; the datasets and codes are made publicly available. The FAIR guiding principles for scientific data management and stewardship can accelerate the adoption and development of ML in the LTP community.

Physics↗

Robust d-Wave Superconductivity in the Square-Lattice t–J Model

Unravelling competing orders emergent in doped Mott insulators and their interplay with unconventional superconductivity is one of the major challenges in condensed matter physics. Here, to explore the possible superconducting state in a doped Mott insulator, we study the square-lattice t-J model with both the nearest-neighbor and next-nearest-neighbor electron hoppings and spin interactions. By using the state-of-the-art density matrix renormalization group calculation with imposing charge U(1) and spin SU(2) symmetries on the six-leg cylinders, we establish a quantum phase diagram including three phases: a stripe charge density wave phase, a superconducting phase without static charge order, and a superconducting phase coexistent with a weak charge stripe order. Crucially, we demonstrate that the superconducting phase has a power-law pairing correlation that decays much slower than the charge density and spin correlations, which is a quasi-1D descendant of the uniform d-wave superconductor in two dimensions. These findings reveal that enhanced charge and spin fluctuations with optimal doping is able to produce robust d-wave superconductivity in doped Mott insulators, providing a foundation for connecting theories of superconductivity to models of strongly correlated systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Gaunt and Breit two-electron contributions to mean-field transformations and fine structure splitting

Materials utilized by novel energy systems are often studied using weakly correlated mean-field theories. However, if these systems incorporate heavy elements, relativistic effects must be included. Therefore, a Kramers unrestricted coupled cluster with singles and doubles excitation formalism within a molecular mean-field exact two-component framework (X2C mmf ) using a four-component Dirac–Hartree–Fock (DHF) reference state is presented. The exact X2C mmf transformed normal-order Hamiltonian incorporates all one-electron and two-electron (2e) contributions from the Coulomb, Gaunt, and Breit operators and is used with the equation of motion method to calculate the excitation energies of the alkali group of elements. Using this framework, the effects of 2e Gaunt and Breit integrals are studied. Results demonstrate growing contributions from these integrals to the generated X2C mmf mean-fields and electronic fine structure calculations with increasing atomic number. Overall, this paper outlines the method, its effect within the X2C mmf approach, and lays the foundation for future theoretical development of relativistic calculations within this framework.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Probing Cellular Activity Via Charge‐Sensitive Quantum Nanoprobes

Nitrogen‐vacancy (NV) based quantum sensors hold great potential for real‐time single‐cell sensing with far‐reaching applications in fundamental biology and medical diagnostics. Although highly sensitive, the mapping of quantum measurements onto cellular physiological states has remained an exceptional challenge. Here, we introduce a novel quantum sensing modality capable of detecting changes in cellular activity. Our approach is based on the detection of environment‐induced charge depletion within an individual particle that, owing to a previously unaccounted transverse dipole term, induces systematic shifts in the zero‐field splitting (ZFS). Importantly, these charge‐induced shifts serve as a reliable indicator for lipopolysaccharide (LPS)‐mediated inflammatory response in macrophages. Furthermore, we demonstrate that surface modification of our diamond nanoprobes effectively suppresses these environment‐induced ZFS shifts, providing an important tool for differentiating electrostatic shifts caused by the environment from other unrelated effects, such as temperature variations. Notably, this surface modification also leads to significant reductions in particle‐induced toxicity and inflammation. Our findings shed light on systematic drifts and sensitivity limits of NV spectroscopy in a biological environment with ramifications for the critical discussion surrounding single‐cell thermogenesis. Notably, this work establishes the foundation for a novel sensing modality capable of probing complex cellular processes through straightforward physical measurements.

band bending↗

The Scientific Case for Concurrent Neutron and X-ray Scattering and Spectroscopy

The interrogation of materials with X-rays or neutrons to determine the structure, energetics, and dynamics of materials is fundamental to advancing materials' physical and chemical science and developing innovative material technologies. A transcending challenge in developing novel materials is that progress hinges on understanding the structure and dynamics across multiple time and length scales in complex materials that feature multiple components, interfaces, and compositions. Despite the ever-growing demands on materials’ characterization, existing approaches are almost exclusively based on isolated X-ray or neutron scattering, i.e., an approach commensurate with the more narrowly defined needs of fifty years ago. A three-day workshop sponsored by the U.S. National Science Foundation (NSF) analyzed the demand for concurrent neutron and X-ray (NeX) experiments. It was held at the Spring Hill Suites, San Jose, California, from June 2 to 4, 2022. In this workshop, 70 national and international experts ascertained the crucial need to establish NeX capabilities to advance the science of complex materials and systems in the US. Here, we illustrate the need for NeX scattering and spectroscopy experiments by showcasing examples that span areas as diverse as biomaterials, energy science, soft matter, and nanomaterials. To provide NeX capability will require new instrumentation that enables concurrent experiments. Affected areas include chemistry, soft matter, quantum materials, pure and applied chemistry, bioscience, geoscience, and applied materials. NeX benefits research outcomes due to the complementarity of the two techniques, which is essential for better model refinement. While joint refinement of data from separate neutron and X-ray experiments is critical to avoid ambiguities, especially in multiphase-multicomponent materials, concurrent experiments overcome scientific and technical barriers associated with single measurements, separated by location and, thus, time. Among all the examples, these factors introduce uncertainties in the results that complicate data analysis. [1,2] [3] While models are strongly sample-dependent, the principles of joint refinement are generally applicable to these disciplines, including the development of advanced parameterization, modeling, and analysis techniques that also consider the temporal and spatial resolutions of the two methods, leading to unambiguous data interpretation. Solutions for technical barriers must be found to realize NeX experiments, including developing robust sample environments that meet the optical requirements of neutrons and X-rays.

36 MATERIALS SCIENCE↗

Imaging two-dimensional generalized Wigner crystals

The Wigner crystal has fascinated condensed matter physicists for nearly 90 years. Signatures of two-dimensional (2D) Wigner crystals were first observed in 2D electron gases under high magnetic field, and recently reported in transition metal dichalcogenide moiré superlattices. Direct observation of the 2D Wigner crystal lattice in real space, however, has remained an outstanding challenge. Conventional scanning tunnelling microscopy (STM) has sufficient spatial resolution but induces perturbations that can potentially alter this fragile state. Here we demonstrate real-space imaging of 2D Wigner crystals in WSe 2 /WS 2 moiré heterostructures using a specially designed non-invasive STM spectroscopy technique. This employs a graphene sensing layer held close to the WSe 2 /WS 2 moiré superlattice. Local STM tunnel current into the graphene layer is modulated by the underlying Wigner crystal electron lattice in the WSe 2 /WS 2 heterostructure. Different Wigner crystal lattice configurations at fractional electron fillings of n = 1/3, 1/2 and 2/3, where n is the electron number per site, are directly visualized. The n = 1/3 and n = 2/3 Wigner crystals exhibit triangular and honeycomb lattices, respectively, to minimize nearest-neighbour occupations. The n = 1/2 state spontaneously breaks the original C3 symmetry and forms a stripe phase. Here our study lays a solid foundation for understanding Wigner crystal states in WSe 2 /WS 2 moiré heterostructures and provides an approach that is generally applicable for imaging novel correlated electron lattices in other systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Vortex dynamics induced by scanning SQUID susceptometry

In this work, we measured the local magnetic response of a niobium thin film by applying a millitesla-scale AC magnetic field using a micron-scale field coil and detecting the response with a micron-scale pickup loop in a scanning superconducting quantum interference device (SQUID) susceptometry measurement. Near the film's critical temperature, we observed a steplike nonlinear and dissipative magnetic response due to the dynamics of a small number of vortex-antivortex pairs induced in the film by the local applied AC field. We modeled the dynamics of the measurement using a combined two-dimensional London-Maxwell and time-dependent Ginzburg-Landau approach, allowing us to construct a detailed real-space picture of the vortex motion causing the observed dissipative response. This work pushes scanning SQUID susceptometry of two-dimensional superconductors beyond the regime of linear response and lays the foundation for microscopic studies of vortex dynamics and pinning in superconducting devices and more exotic materials systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties

Machine-readable chemical structure representations are foundational in all attempts to harness machine learning for the prediction of reactivities, selectivities, and chemical properties directly from molecular structure. The featurization of discrete chemical structures into a continuous vector space is a critical phase undertaken before model selection, and the development of new ways to quantitatively encode molecules is an active area of research. Here, we highlight the application and suitability of different representations, from expert-guided “engineered” descriptors to automatically “learned” features, in different prediction tasks relevant to organic and organometallic chemistry, where differing amounts of training data are available. These tasks include statistical models of stereo- and enantioselectivity, thermochemistry, and kinetics developed using experimental and quantum chemical data. The use of expert-guided molecular descriptors provides an opportunity to incorporate chemical knowledge, domain expertise, and physical constraints into statistical modeling. In applications to stereoselective organic and organometallic catalysis, where data sets may be relatively small and 3D-geometries and conformations play an important role, mechanistically informed features can be used successfully to obtain predictive statistical models that are also chemically interpretable. We provide an overview of several recent applications of this approach to obtain quantitative models for reactivity and selectivity, where topological descriptors, quantum mechanical calculations of electronic and steric properties, along with conformational ensembles, all feature as essential ingredients of the molecular representations used. Alternatively, more flexible, general-purpose molecular representations such as attributed molecular graphs can be used with machine learning approaches to learn the complex relationship between a structure and prediction target. This approach has the potential to out-perform more traditional representation methods such as “hand-crafted” molecular descriptors, particularly as data set sizes grow. One area where this is particularly relevant is in the use of large sets of quantum mechanical data to train quantitative structure–property relationships. A general approach toward curating useful data sets and training highly accurate graph neural network models is discussed in the context of organic bond dissociation enthalpies, where this strategy outperforms regression using precomputed descriptors. Finally, we describe how graph neural network predictions can be incorporated into mechanistically informed statistical models of chemical reactivity and selectivity. Once trained, this approach avoids the expensive computational overhead associated with quantum mechanical calculations, while maintaining chemical interpretability. We illustrate examples for which fast predictions of bond dissociation enthalpy and of the identities of radicals formed through cleavage of a molecule’s weakest bond are used in simple physical models of site-selectivity and reactivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Revealing multiple classes of stable quantum emitters in hexagonal boron nitride with correlated optical and electron microscopy

Defects in hexagonal boron nitride (hBN) exhibit high-brightness, room-temperature quantum emission, but their large spectral variability and unknown local structure challenge their technological utility. Here, we directly correlate hBN quantum emission with local strain using a combination of photoluminescence (PL), cathodoluminescence (CL) and nanobeam electron diffraction. Across 40 emitters, we observe zero phonon lines (ZPLs) in PL and CL ranging from 540 to 720 nm. CL mapping reveals that multiple defects and distinct defect species located within an optically diffraction-limited region can each contribute to the observed PL spectra. Local strain maps indicate that strain is not required to activate the emitters and is not solely responsible for the observed ZPL spectral range. Instead, at least four distinct defect classes are responsible for the observed emission range, and all four classes are stable upon both optical and electron illumination. Our findings offer a foundation for future atomic-scale optical characterization of colour centres.

36 MATERIALS SCIENCE↗

PHOENIX Electrostatic Design

PHOENIX (Portable, High-efficiency, Optimal ENergy Imaging X-rays) is a quasi-DC, electrostatic, vacuum-diode designed as a portable x-ray source with national defense and commercial applications. The patent-pending PHOENIX concept combines a megavoltage, Cockroft-Walton voltage multiplier with a Van de-Graaff electrostatic charge-storage dome to create a vacuum-diode suitable for x-ray production. Naturally this structure must minimize internal electric fields to reduce electrical breakdown while simultaneously reducing size and weight to enhance portability. In this paper we describe the optimization process and model results obtained using the COMSOL multi-physics code. We describe three models: a prototype model built to demonstrate the PHOENIX concept as part of Laboratory Directed Research and Development (LDRD) Mission Foundation Research (MFR) Phase-I , a “back-of-the-envelope” design used as a starting point for further COMSOL optimization, and finally, the optimized geometry implemented in the MFR Phase-II. In all cases compromises resulting from cost, schedule, and manufacturing constraints were taken into account as the design progressed.

42 ENGINEERING↗

Exploring Divisibility and Summability of 'Photon' Wave Packets in Nonlinear Optical Phenomena

Formulations for second and higher harmonic frequency up and down conversions, as well as multi photon processes directly assume summability and divisibility of photons. Quantum mechanical (QM) interpretations are completely congruent with these assumptions. However, for linear optical phenomena (interference, diffraction, refraction, material dispersion, spectral dispersion, etc.), we have a profound dichotomy. Most optical engineers innovate and analyze all optical instruments by propagating pure classical electromagnetic (EM) fields using Maxwell s equations and gives only lip-service to the concept "indivisible light quanta". Further, irrespective of linearity or nonlinearity of the phenomena, the final results are always registered through some photo-electric or photo-chemical effects. This is mathematically well modeled by a quadratic action (energy absorption) relation. Since QM does not preclude divisibility or summability of photons in nonlinear & multi-photon effects, it cannot have any foundational reason against these same possibilities in linear optical phenomena. It implies that we must carefully revisit the fundamental roots behind all light-matter interaction processes and understand the common origin of "graininess" and "discreteness" of light energy.

Prasad, Narasimha↗

Chemical Control of Symmetry and Bandgap in Tungsten Oxyhalide van der Waals Semiconductors

Tunability in solid-state materials is essential for testing theory, discovering quantum phases, and enabling functionality. Layered van der Waals (vdW) semiconductors offer a unique platform, providing new degrees of freedom at the two-dimensional (2D) limit through exfoliation and external controls. Here, in this study, we demonstrate tunability of symmetry and electronic structure via halogen substitution in a family of layered vdW tungsten oxyhalides. Substituting the halogens in WO 2 X 2 (X = I, Br, Cl) tunes the bandgap across a broad energy range and modifies the structural symmetry from centrosymmetric to noncentrosymmetric. By alloying WO 2 I 2– y Br y , we continuously tune the polar distortion and optical gap across the visible range. These insights into halogen substitution effects on symmetry and electronic structure lay the foundation for new tunable vdW semiconductors for optoelectronics and nonlinear optics.

36 MATERIALS SCIENCE↗

Untangling charge-order dependent bulk states from surface effects in a topological kagome metal ScV 6 Sn 6

Kagome metals with charge density wave (CDW) order exhibit a broad spectrum of intriguing quantum phenomena. The recent discovery of the novel kagome CDW compound ScV 6 Sn 6 has spurred significant interest. However, understanding the interplay between CDW and the bulk electronic structure has been obscured by a profusion of surface states and terminations in this quantum material. Here, in this study, we employ photoemission spectroscopy and potassium dosing to elucidate the complete bulk band structure of ScV 6 Sn 6 , revealing multiple van Hove singularities near the Fermi level. We surprisingly discover a robust spin-polarized topological Dirac surface resonance state at the M point within the twofold van Hove singularities. Assisted by first-principles calculations, the temperature dependence of the k z -resolved angle-resolved photoemission spectroscopy spectrum provides unequivocal evidence for the proposed $\sqrt{3}$×$\sqrt{3}$×3 charge order over other candidates. Our work not only enhances the understanding of the CDW-dependent bulk and surface states in ScV 6 Sn 6 , but also establishes an essential foundation for potential manipulation of the CDW order in kagome materials.

36 MATERIALS SCIENCE↗

Quantum Dots on Silicon-on-Insulator (QD/SOI): Nanoscale Strain and Band Structure Engineering (Final Report)

As the project titles indicate, the work focused on Group IV nanomembranes (NMs), thin functional layers, and interfaces, all with one or more dimensions at the nanoscale. The primary focus was discovery driven fundamental science. This approach led to surprising new, unexpected results that ended up being patentable and creating commercial value, but that were also high-impact science. Our effort divided into several overlapping thrusts: 1) strain engineering of NMs, via both lattice strain and externally applied strain, 2) interfaces between crystalline semiconductor NMs and other 2D sheets that are stacked or grown on each other, and 3) charge transport (electronics and optoelectronics) in thin layers, sheets, and surfaces. We accomplish these goals via 1) fabrication of new or higher-quality materials using NM approaches, 2) growth of new combinations, or 3) transfer and stacking to create new composites. The range of materials included combinations of Si, Ge, graphene, and several III-V compounds. The work has foundations in both nano- and mesoscale science.

36 MATERIALS SCIENCE↗

Perspectives on van der Waals Density Functionals: The Case of TiS 2

The van der Waals interaction is of foundational importance for a wide variety of physical systems. In particular, van der Waals forces lie at the heart of potential device technologies that may be realized from the functional organization of layered two-dimensional (2D) nanomaterials. Furthermore, for intermediate to large-scale applications modeling, van der Waals density functionals have become the de facto choice for first-principles calculations. In particular, the vdW-DF family of functionals have provided a systematic approach to this theoretically challenging problem. While much progress has been made, there remains room for improvement in the microscopic description of vdW forces from these density functionals. In this work, we compute benchmark results for the binding energy and the electronic density response to binding in TiS2 via accurate diffusion quantum Monte Carlo calculations. We compare these benchmark data to results obtained from local, semilocal, and van der Waals functionals. In particular, we gauge the quality of the original vdW-DF/vdW-DF2 functionals, as well as updated variants such as vdW-DF-C09, vdW-DF-optB88, vdW-DF-optB86b, and vdW-DF2-B86R. We find a close relationship between the accuracy of predicted interlayer separation distances and binding energies for TiS 2 , with the vdW-DF-optB88 functional performing very well in terms of both quantities. In general, the more recently developed functionals are systematic improvements over older ones. However, when considering the response of the electron density to binding, we find that local-density approximation (LDA) and PBEsol generally outperform the vdW-DF functionals in describing the interlayer charge accumulation with vdW-DF-C09 variants performing the best overall.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Towards dislocation-driven quantum interconnects

A central problem in the deployment of quantum technologies is the realization of robust architectures for quantum interconnects. We propose to engineer interconnects in semiconductors and insulators by patterning spin qubits at dislocations, thus forming quasi one-dimensional lines of entangled point defects. To gain insight into the feasibility and control of dislocation-driven interconnects, we investigate the optical cycle and coherence properties of nitrogen-vacancy (NV) centers in diamond, in proximity of dislocations, using a combination of advanced first-principles calculations. We show that one can engineer spin defects with properties similar to those of their bulk counterparts, including charge stability and a favorable optical cycle, and that NV centers close to dislocations have much improved coherence properties. Finally, we predict optically detected magnetic resonance spectra that may facilitate the experimental identification of specific defect configurations. Our results provide a theoretical foundation for the engineering of one-dimensional arrays of spin defects in the solid state.

Materials science↗