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We present a new hybrid quantum algorithm to estimate molecular excited and charged states on near-term quantum computers following any VQE-based ground state estimation.
New advances in fabrication of low-density high-Z-doped foams have opened new windows on understanding how materials that are not in local thermodynamic equilibrium (LTE) are heated and radiate. Simulations are discussed in this paper of the x-ray spectral emissions from laser-irradiated very low-density Ge-doped silica aerogel targets using a two-dimensional radiation-hydrodynamics code incorporating a modern non-LTE superconfiguration atomic model. Details of the computational model are presented, and it is shown that, for the long-scale-length, subcritical-density, approx2-3 keV electron temperature plasmas created in experiments at the Omega laser facility [T. R. Boehly et al., Opt. Commun. 133, 495 (1997)], the simulations provide a close match to both the measured Ge L-shell emission (approx1-1.5 keV) and the measured Ge K-shell emission (approx10-11 keV), but only by accounting properly for nonlocal thermal conduction. The older average-atom atomic model is shown to be inadequate for these non-LTE plasmas.
Motivated by the significant biological functions of polysulfides in life processes and important applications of sodium polysulfides in battery sciences, herein we report a joint experimental and computational study on the electronic and geometric structures of a series of NaSn- (n = 5-9) clusters. The cryogenic, size-selective, negative ion photoelectron spectroscopy (NIPES) is employed to obtain their photoelectron spectra, in which distinctive spectral features with electron binding energy (EBE) up to 6.4 eV are unraveled. The EBE of the first peak in each spectrum for NaSn- (n =5-9), assigned to the transition from the ground state of anion to the ground state of each neutral radical, is observed to increase with cluster size. The vertical detachment energies (VDEs), determined from the first peak maximum, are 3.43 ± 0.02, 3.57 ± 0.02, 3.82 ± 0.03, 3.86 ± 0.02, and 4.00 ± 0.02 eV and the adiabatic detachment energies (ADEs), determined by the onset of the first peak, are 3.27 ± 0.05, 3.44 ± 0.05, 3.65 ± 0.05, 3.75 ± 0.05, and 3.93 ± 0.05 eV, for n = 5-9, respectively. A plural of low-lying isomers of anions are screened and identified with density functional theory calculations, showing that all featuring preference for a chain-like polysulfide moiety electrostatically interacted with a sodium cation. The calculated VDEs and ADEs using the high-level CCSD(T)/aug-cc-pVTZ method are in excellent agreement with experimental results, further confirming the identified isomers as most stable ones. Further analyses based on the excited-state transition, molecular orbitals and natural population charge are performed to assign all observed spectral bands along the binding energy axis, suggesting that the electron detachment process and observed excitations mainly derived from the polysulfide chain of NaSn- clusters. This work provides a fundamental understanding of the intrinsic molecular properties of sodium polysulfide systems, which widely exist in life science and sodium-sulfur cells.
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A high-fidelity, low-Mach computational fluid dynamics simulation tool that includes evaporating droplets and variable-density turbulent flow coupling is well-suited to ascertain transmission probability and supports risk mitigation methods development for airborne infectious diseases such as COVID-19. A multi-physics large-eddy simulation-based paradigm is used to explore droplet and aerosol pathogen transport from a synthetic cough emanating from a kneeling humanoid. For an outdoor configuration that mimics the recent open-space social distance strategy of San Francisco, maximum primary droplet deposition distances are shown to approach 8.1 m in a moderate wind configuration with the aerosol plume transported in excess of 15 m. In quiescent conditions, the aerosol plume extends to approximately 4 m before the emanating pulsed jet becomes neutrally buoyant. A dose–response model, which is based on previous SARS coronavirus (SARS-CoV) data, is exercised on the high-fidelity aerosol transport database to establish relative risk at eighteen virtual receptor probe locations.
Cosmic Reionization On Computers is a long-term program of numerical simulations of cosmic reionization. Its goal is to model fully self-consistently (albeit not necessarily from the first principles) all relevant physics, from radiative transfer to gas dynamics and star formation, in simulation volumes of up to 100 comoving Mpc, and with spatial resolution approaching 100 pc in physical units. In this method paper, we describe our numerical method, the design of simulations, and the calibration of numerical parameters. Using several sets (ensembles) of simulations in 20 h {sup –1} Mpc and 40 h {sup –1} Mpc boxes with spatial resolution reaching 125 pc at z = 6, we are able to match the observed galaxy UV luminosity functions at all redshifts between 6 and 10, as well as obtain reasonable agreement with the observational measurements of the Gunn-Peterson optical depth at z < 6.
We compare the results from several sets of cosmological simulations of cosmic reionization, produced under the Cosmic Reionization On Computers project, with existing observational data on the high-redshift Lyα forest and the abundance of Lyα emitters. We find good consistency with the observational measurements and previous simulation work. By virtue of having several independent realizations for each set of numerical parameters, we are able to explore the effect of cosmic variance on observable quantities. One unexpected conclusion we are forced into is that cosmic variance is unusually large at z > 6, with both our simulations and, most likely, observational measurements still not fully converged for even such basic quantities as the average Gunn-Peterson optical depth or the volume-weighted neutral fraction. We also find that reionization has little effect on the early galaxies or on global cosmic star formation history, because galaxies whose gas content is affected by photoionization contain no molecular (i.e., star-forming) gas in the first place. In particular, measurements of the faint end of the galaxy luminosity function by the James Webb Space Telescope are unlikely to provide a useful constraint on reionization.
Atmospheric pressure plasmas (APPs) are at the core of diverse technological applications in materials processing, chemical synthesis, resource recovery, water treatment, and medicine, among others. APPs span a wide range of power density, from low-power non-thermal to high-power thermal discharges, and typically involve interactions with a stream of working gas, processing material, or gas environment. The article provides an overview of Computational Fluid Dynamics (CFD) approaches, from mathematical models to software strategies, for the analysis of APP flows. Its focus is flows with large variations in ionization degree and significant fluid dynamic-thermal-electromagnetic coupling, as particularly found in mid- to high-power discharges. The advances achieved and challenges faced by CFD of APP flows have been driven by established and emerging applications, and can be broadly characterized in terms of model fidelity and numerical accuracy. Fidelity refers to the degree of underlying phenomena captured by the model, whereas accuracy to the precision of the numerical solution of the model. Two distinct numerical accuracy challenges are addressed: the capture of instability and pattern formation phenomena, and of plasma-gas interaction and turbulence; as well as two representative fidelity challenges: radiative transport under nonequilibrium conditions, and nonequilibrium electron and particle kinetics. The purpose of this article is to offer guidance to researchers, from modelers and code developers to open-source and commercial software users, working on CFD analyses of APP flows within technological contexts.
Abstract Synchrotron x-ray imaging techniques, like x-ray computed tomography (CT) and radiography have proven instrumental in expanding the communities knowledge of complex transport and reaction kinetics in electrochemical devices such as fuel cells and electrolyzers. This work presents the development of novel x-ray CT imaging techniques for operando visualization of water within low temperature fuel cells at spatial resolutions spanning the micro and nano scales. The design of operando sample holders, for both micro x-ray CT and nano CT experiments is described in depth, and prototypes of these sample holders were evaluated across a set of requirements, the most important of which are x-ray transmissibility, electrical conductivity and mechanical stability. Water segmentation from micro x-ray CT data was enabled by an image subtraction method, where the image without water is subtracted from the one with water. Through iterative experimentations, the operando nano CT cell was developed to optimize mechanical compression, electric conductivity and gas flow. While three-dimensional fuel cell reconstructions were shown possible, there remain challenges to overcome at typical lower energies (8 keV) due to beam damage, whereas it is not as significant for higher energies (>17.5 keV).
The conformal compensator formalism is a convenient and versatile representation of supergravity (SUGRA) obtained by gauge-fixing conformal SUGRA. Unfortunately, practical calculations often require cumbersome manipulations of component field terms involving the full gravity multiplet. In this paper, we derive an alternative gauge fixing for conformal SUGRA which decouples these gravity complications from SUGRA computations. This yields a simplified tree-level action for the matter fields in SUGRA which can be expressed compactly in terms of superfields and a modified conformal compensator. Phenomenologically relevant quantities such as the scalar potential and fermion mass matrix are then straightforwardly obtained by expanding the action in superspace.
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Purpose: This study demonstrates a methodology for tracking changes in metastatic bone disease using trajectories in material basis space in serial dual energy computed tomography (DECT) studies. Methods: This study includes patients with bone metastases from breast cancer that had clinical surveillance CT scans using a General Electric CT750HD in dual energy mode. A radiologist defined regions-of-interested (ROI) for bone metastasis, normal bone, and marrow across the serial DECT scans. Our approach employs a Radon transform to forward-projection the basis images, namely, water and iodine, into sinogram space. This data is then repartitioned into fat/bone and effective density/Z image pairs using assumed energy spectrums for the x-ray energies. This approach both helps remove negative material densities and avoids adding spectrum-hardening artifacts. These new basis data sets were then reconstructed via filtered back-projection to create new material basis pair images. The trajectories of these pairs were then plotted in the new basis space providing a means to both visualize and quantitatively measure changes in the material properties of the tumors. Results: ROI containing radiologist defined metastatic bone disease showed well-defined trajectories in both fat/bone and effective density/Z space. ROI that contained radiologist defined normal bone and marrow did not exhibit any discernible trajectories and were stable from scan to scan. Conclusions: The preliminary results show that changes in material composition and effective density/Z image pairs were seen primarily in metastasis and not in normal tissue. This study indicates that by using routine clinical DECT it may be possible to monitor therapy response of bone metastases because healing or worsening bone metastases change material composition of bone. Additional studies are needed to further validate these results and to test for their correlation with outcome.
Classifying memory access patterns is paramount to the selection of the right set of optimizations and determination of the parallelization strategy. Static analyses suffer from ambiguities present in source code, which modern compilation techniques, such as profile-guided optimization, alleviate by observing runtime behavior and feeding back into the compilation flow. This paper discusses a dynamic analysis technique for recognizing memory access patterns, with application to the stencils domain, and presents our design and C++ implementation using the memory-tracing tool Gleipnir. Finally, we evaluate and discuss the performance and matching capability of our classifiers in the context of the Polybench scientific benchmark suite, which includes both stencil and matrix computations.
Here, dielectric microstructures have generated much interest in recent years as a means of accelerating charged particles when powered by solid state lasers. The acceleration gradient (or particle energy gain per unit length) is an important figure of merit. To design structures with high acceleration gradients, we explore the adjoint variable method, a highly efficient technique used to compute the sensitivity of an objective with respect to a large number of parameters. With this formalism, the sensitivity of the acceleration gradient of a dielectric structure with respect to its entire spatial permittivity distribution is calculated by the use of only two full-field electromagnetic simulations, the original and ‘adjoint’. The adjoint simulation corresponds physically to the reciprocal situation of a point charge moving through the accelerator gap and radiating. Using this formalism, we perform numerical optimizations aimed at maximizing acceleration gradients, which generate fabricable structures of greatly improved performance in comparison to previously examined geometries.
Future energy production and storage in the chemical and refinery industries, stationary power generation, and transportation sectors will employ a diverse suite of technologies, including renewables, such as biomass, untapped energy resources, and processes with improved energy efficiency. Heterogeneous nanocatalysts will play an ever-increasing role in these technologies. Increased precision in molecular architecture over multiple length scales and/or tailored multi-functionality will often be needed in these materials. Advances in computational-based discovery of such nanomaterials are described through examples that predict the molecular architecture of emergent catalytic materials and reveal mechanisms of colloidal metal nanoparticle growth.