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Structural Properties of Hyperuniform Networks

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arXiv 2024年

作者： Newby, Eli Shi, Wenlong Jiao, Yang Albert, Reka Torquato, Salvatore Department of Physics Pennsylvania State University University ParkPA16802 United States Materials Science and Engineering Arizona State University TempeAZ85287 United States Department of Physics Arizona State University TempeAZ85287 United States Department of Chemistry Princeton University PrincetonNJ08544 United States Department of Physics Princeton University PrincetonNJ08544 United States Princeton Institute of Materials Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Disordered hyperuniform many-particle systems are recently discovered exotic states of matter, characterized by a complete suppression of normalized infinite-wavelength density fluctuations, as in perfect crystals, and lack of conventional long-range order, as in liquids and glasses. In this work, we begin a program to quantify the structural properties of nonhyperuniform and hyperuniform networks. In particular, large two-dimensional (2D) Voronoi networks (graphs) containing approximately 10,000 nodes are created from a variety of different point configurations, including the antihyperuniform hyperplane intersection process (HIP), nonhyperuniform Poisson process, nonhyperuniform random sequential addition (RSA) saturated packing, and both non-stealthy and stealthy hyperuniform point processes. We carry out an extensive study of the Voronoi-cell area distribution of each of the networks through determining multiple metrics that characterize the distribution, including their higher-cumulants (i.e., skewness 1 and excess kurtosis 2). We show that the HIP distribution is far from Gaussian, as evidenced by a high skewness (γ1 = 3.16) and large positive excess kurtosis (γ2 = 16.2). The Poisson (with γ1 = 1.07 and γ2 = 1.79) and non-stealthy hyperuniform (with γ1 = 0.257 and γ2 = 0.0217) distributions are Gaussian-like distributions, since they exhibit a small but positive skewness and excess kurtosis. The RSA (with γ1 = 0.450 and γ2 = -0.0384) and the highest stealthy hyperuniform distributions (with γ1 = 0.0272 and γ2 = -0.0626) are also non-Gaussian because of their low skewness and negative excess kurtosis, which is diametrically opposite non-Gaussian behavior of the HIP. The fact that the cell-area distributions of large, finite-sized RSA and stealthy hyperuniform networks (e.g., with N ≈ 10, 000 nodes) are narrower, have larger peaks, and smaller tails than a Gaussian distribution implies that in the thermodynamic limit the distributions should exhibit compact suppo

关键词： Gaussian distribution

Reduced modeling of porous media convection in a minimal flow unit at large Rayleigh number

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arXiv 2018年

作者： Wen, Baole Chini, Gregory P. Department of Geological Sciences Jackson School of Geosciences University of Texas at Austin AustinTX78712 United States Institute of Computational Engineering and Sciences University of Texas at Austin AustinTX78712 United States Program in Integrated Applied Mathematics University of New Hampshire W113 Kingsbury Hall 33 Academic Way DurhamNH03824 United States Center for Fluid Physics University of New Hampshire DurhamNH03824 United States Department of Mechanical Engineering University of New Hampshire DurhamNH03824 United States

Direct numerical simulations (DNS) indicate that at large values of the Rayleigh number (Ra) convection in porous media self-organizes into narrowly-spaced columnar flows, with more complex spatiotemporal features being confined to boundary layers near the top and bottom walls. In this investigation of high-Ra porous media convection in a minimal flow unit, two reduced modeling strategies are proposed that exploit these specific flow characteristics. Both approaches utilize the idea of decomposition since the flow exhibits different dynamics in different regions of the domain: small-scale cellular motions generally are localized within the thermal and vorticity boundary layers near the upper and lower walls, while in the interior, the flow exhibits persistent large-scale structures and only a few low (horizontal) wavenumber Fourier modes are active. Accordingly, in the first strategy, the domain is decomposed into two near-wall regions and one interior region. Our results confirm that suppressing the interior high-wavenumber modes has negligible impact on the essential structural features and transport properties of the flow. In the second strategy, a hybrid reduced model is constructed by using Galerkin projection onto a fully a priori eigenbasis drawn from energy stability and upper bound theory, thereby extending the model reduction strategy developed by Chini et al. (Physica D, vol. 240, 2011, pp. 241-248) to large Ra. The results indicate that the near-wall upper-bound eigenmodes can economically represent the small-scale rolls within the exquisitely-thin thermal boundary layers. Relative to DNS, the hybrid algorithm enables over an order-of-magnitude increase in computational efficiency with only a modest loss of accuracy. Copyright © 2018, The Authors. All rights reserved.

关键词： Porous materials

Bayesian quantum phase estimation with fixed photon states

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arXiv 2023年

作者： Zhou, Boyu Guha, Saikat Gagatsos, Christos N. Department of Physics The University of Arizona 1118 E 4th St TucsonAZ85721 United States Department of Electrical and Computer Engineering University of Maryland 2410 A.V. Williams Building College ParkMD20742 United States Wyant College of Optical Sciences The University of Arizona 1630 E University Blvd TucsonAZ85721 United States Department of Electrical and Computer Engineering The University of Arizona 1230 E Speedway Blvd TucsonAZ85721 United States Program in Applied Mathematics The University of Arizona 617 N. Santa Rita TucsonAZ85721 United States

We consider a two-mode bosonic state with fixed photon number n ∈ N, whose upper and lower modes pick up a phase and − respectively. We compute the optimal Fock coefficients of the input state, such that the mean square error (MSE) for estimating is minimized while the minimum MSE is always attainable by a measurement. Our setting is Bayesian, i.e., we consider to be a random variable that follows a prior probability distribution function (PDF). Initially, we consider the flat prior PDF and we discuss the well-known fact that the MSE is not an informative tool for estimating a phase when the variance of the prior PDF is large. Therefore, we move on to study truncated versions of the flat prior in both single-shot and adaptive approaches. For our adaptive technique we consider n = 1 and truncated prior PDFs. Each subsequent step utilizes as prior PDF the posterior probability of the previous step and at the same time we update the optimal state and optimal measurement. Copyright © 2023, The Authors. All rights reserved.

关键词： Distribution functions

Hopping-Transport Mechanism for Reconfigurable Logic in Disordered Dopant Networks

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Physical Review applied 2022年第6期17卷 064025-064025页

作者： Henri Tertilt Jesse Bakker Marlon Becker Bram de Wilde Indrek Klanberg Bernard J. Geurts Wilfred G. van der Wiel Andreas Heuer Peter A. Bobbert Institut für physikalische Chemie Westfälische Wilhelms-Universität Münster Münster Germany NanoElectronics Group University of Twente Enschede Netherlands Multiscale Modeling and Simulation Group Faculty of Electrical Engineering Mathematics and Computer Science MESA+ Institute for Nanotechnology and Center for Brain-Inspired Nano Systems (BRAINS) University of Twente Enschede Netherlands Center for Computational Energy Research (CCER) Eindhoven University of Technology Netherlands Fachbereich Physik Westfälische Wilhelms-Universität Münster Münster Germany Department of Applied Physics Eindhoven University of Technology Netherlands

We present an atomic-scale mechanism based on variable-range hopping of interacting charges enabling reconfigurable logic in dopant network processing units. Kinetic Monte Carlo simulations of the hopping process show temperature-dependent current-voltage characteristics and artificially evolved basic Boolean logic gates in very good agreement with experiment. The simulations provide unique insights into the local electrostatic potential and current flow in the dopant network, showing subtle changes induced by control voltages that set the conditions for the logic operation. These insights will be crucial in the systematic further development of this burgeoning technology for unconventional computing. The establishment of the principles underlying the logic functionality of these devices encourages the exploration and utilization of the same principles in other materials and device geometries.

关键词： Doping effects Hopping transport Neuromorphic computing Devices for digital logic, storage & processing Disordered systems Doped semiconductors Genetic algorithm Monte Carlo methods

Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography

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arXiv 2023年

作者： Lozenski, Luke Wang, Hanchen Li, Fu Anastasio, Mark Wohlberg, Brendt Lin, Youzuo Villa, Umberto the Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO63130 United States the Energy and Natural Resources Security Group Los Alamos National Laboratory Los AlamosNM87545 United States the Department of Bioengineering University of Illinois Urbana-Champaign UrbanaIL61801 United States the Applied Mathematics and Plasma Physics Group Los Alamos National Laboratory Los AlamosNM87545 United States the Oden Institute for Computational Engineering and Sciences University of Texas at Austin AustinTX78712 United States

—Ultrasound computed tomography (USCT) is an emerging imaging modality that holds great promise for breast imaging. Full-waveform inversion (FWI)-based image reconstruction methods incorporate accurate wave physics to produce high spatial resolution quantitative images of speed of sound or other acoustic properties of the breast tissues from USCT measurement data. However, the high computational cost of FWI reconstruction represents a significant burden for its widespread application in a clinical setting. The research reported here investigates the use of a convolutional neural network (CNN) to learn a mapping from USCT waveform data to speed of sound estimates. The CNN was trained using a supervised approach with a task-informed loss function aiming at preserving features of the image that are relevant to the detection of lesions. A large set of anatomically and physiologically realistic numerical breast phantoms (NBPs) and corresponding simulated USCT measurements was employed during training. Once trained, the CNN can perform real-time FWI image reconstruction from USCT waveform data. The performance of the proposed method was assessed and compared against FWI using a hold-out sample of 41 NBPs and corresponding USCT data. Accuracy was measured using relative mean square error (RMSE), structural self-similarity index measure (SSIM), and lesion detection performance (DICE score). This numerical experiment demonstrates that a supervised learning model can achieve accuracy comparable to FWI in terms of RMSE and SSIM, and better performance in terms of task performance, while significantly reducing computational time. © 2023, CC BY-NC-ND.

关键词： Image reconstruction

Layer-dependent optically induced spin polarization in InSe

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Physical Review B 2023年第11期107卷 115304-115304页

作者： Jovan Nelson Teodor K. Stanev Dmitry Lebedev Trevor LaMountain J. Tyler Gish Hongfei Zeng Hyeondeok Shin Olle Heinonen Kenji Watanabe Takashi Taniguchi Mark C. Hersam Nathaniel P. Stern Applied Physics Program Northwestern University Evanston Illinois 60208 USA Department of Physics and Astronomy Northwestern University Evanston Illinois 60208 USA Department of Material Science and Engineering Northwestern University Evanston Illinois 60208 USA Computational Science Division Argonne National Laboratory Lemont Illinois 60439 USA Material Science Division Argonne National Laboratory Lemont Illinois 60439 USA Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan Department of Chemistry Northwestern University Evanston Illinois 60208 USA Department of Electrical Engineering and Computer Science Northwestern University Evanston Illinois 60208 USA

Optical control of spin in semiconductors has been pioneered using nanostructures of III-V and II-VI semiconductors, but the emergence of two-dimensional van der Waals materials offers an alternative low-dimensional platform for spintronic phenomena. Indium selenide (InSe), a group-III monochalcogenide van der Waals material, has shown promise for optoelectronics due to its high electron mobility, tunable direct band gap, and quantum transport. In addition to these confirmed properties, there are predictions of spin-dependent optical selection rules, suggesting the potential for all-optical excitation and control of spin in a two-dimensional layered material. Despite these predictions, layer-dependent optical spin phenomena in InSe have yet to be explored. Here, we present measurements of layer-dependent optical spin dynamics in few-layer and bulk InSe. Polarized photoluminescence reveals layer-dependent optical orientation of spin, thereby demonstrating the optical selection rules in few-layer InSe. Spin dynamics are also studied in many-layer InSe using time-resolved Kerr rotation spectroscopy. By applying out-of-plane and in-plane static magnetic fields for polarized emission measurements and Kerr measurements, respectively, the g factor for InSe was extracted. Further investigations are done by calculating precession values using a k·p model, which is supported by ab initio density functional theory. Comparison of predicted precession rates with experimental measurements highlights the importance of excitonic effects in InSe for understanding spin dynamics. Optical orientation of spin is an important prerequisite for optospintronic phenomena and devices, and these first demonstrations of layer-dependent optical excitation of spins in InSe lay the foundation for combining layer-dependent spin properties with advantageous electronic properties found in this material.

关键词： Magneto-optics Spin dynamics Chalcogenides Layered semiconductors Van der Waals systems Density functional theory Kerr effect Photoluminescence k dot p method

BEAST DB: Grand-Canonical Database of Electrocatalyst Properties

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arXiv 2024年

作者： Tezak, Cooper Clary, Jacob Gerits, Sophie Quinton, Joshua Rich, Benjamin Singstock, Nicholas Alherz, Abdulaziz Aubry, Taylor Clark, Struan Hurst, Rachel Ben, Mauro Del Sutton, Christopher Sundararaman, Ravishankar Musgrave, Charles Vigil-Fowler, Derek Department of Chemical and Biological Engineering University of Colorado Boulder BoulderCO80309 United States Materials Chemical and Computational Science Directorate National Renewable Energy Laboratory GoldenCO80401 United States Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute TroyNY12180 United States Department of Chemistry University of Colorado Boulder BoulderCO80309 United States Department of Mechanical Engineering University of Colorado Boulder BoulderCO80309 United States Department of Chemical Engineering College of Engineering and Petroleum Kuwait University Safat13060 Kuwait Applied Mathematics & Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Chemistry and Biochemistry University of South Carolina ColumbiaSC29208 United States Department of Materials Science and Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Materials Science and Engineering Program University of Colorado Boulder BoulderCO80309 United States Renewable and Sustainable Energy Institute University of Colorado Boulder BoulderCO80309 United States Department of Chemical Engineering University of Utah Salt Lake CityUT84112 United States

We present BEAST DB, an open-source database comprised of ab initio electrochemical data computed using grand-canonical density functional theory in implicit solvent at consistent calculation parameters. The database contains over 20,000 surface calculations and covers a broad set of heterogenous catalyst materials and electrochemical reactions. Calculations were performed at self-consistent fixed potential as well as constant charge to facilitate comparisons to the computational hydrogen electrode. This article presents common use cases of the database to rationalize trends in catalyst activity, screen catalyst material spaces, understand elementary mechanistic steps, analyze electronic structure, and train machine learning models to predict higher fidelity properties. Users can interact graphically with the database by querying for individual calculations to gain granular understanding of reaction steps or by querying for an entire reaction pathway on a given material using an interactive reaction pathway tool. BEAST DB will be periodically updated, with planned future updates to include advanced electronic structure data, surface speciation studies, and greater reaction coverage. Copyright © 2024, The Authors. All rights reserved.

关键词： Database systems

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

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arXiv 2021年

作者： Batzner, Simon Musaelian, Albert Sun, Lixin Geiger, Mario Mailoa, Jonathan P. Kornbluth, Mordechai Molinari, Nicola Smidt, Tess E. Kozinsky, Boris John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Robert Bosch Research and Technology Center CambridgeMA02139 United States Computational Research Division Center for Advanced Mathematics for Energy Research Applications Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02142 United States

This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

Data-driven brain network models predict individual variability in behavior

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arXiv 2018年

作者： Bansal, Kanika Medaglia, John D. Bassett, Danielle S. Vettel, Jean M. Muldoon, Sarah F. Department of Mathematics University at Buffalo BuffaloNY United States U.S. Army Research Laboratory Aberdeen Proving GroundMD United States Department of Biomedical Engineering Columbia University New YorkNY United States Department of Psychology Drexel University PhiladelphiaPA United States Department of Neurology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biomedical Engineering University of Pennsylvania PhiladelphiaPA United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA United States Department of Psychological and Brain Sciences University of California Santa BarbaraCA United States Computational and Data-Enabled Science and Engineering Program University at Buffalo-SUNY BuffaloNY United States

The relationship between brain structure and function has been probed using a variety of approaches, but how the underlying structural connectivity of the human brain drives behavior is far from understood. To investigate the effect of anatomical brain organization on human task performance, we use a data-driven computational modeling approach and explore the functional effects of naturally occurring structural differences in brain networks. We construct personalized brain network models by combining anatomical connectivity estimated from diffusion spectrum imaging of individual subjects with a nonlinear model of brain dynamics. By performing computational experiments in which we measure the excitability of the global brain network and spread of synchronization following a targeted computational stimulation, we quantify how individual variation in the underlying connectivity impacts both local and global brain dynamics. We further relate the computational results to individual variability in the subjects' performance of three language-demanding tasks both before and after transcranial magnetic stimulation to the left-inferior frontal gyrus. Our results show that task performance correlates with either local or global measures of functional activity, depending on the complexity of the task. By emphasizing differences in the underlying structural connectivity, our model serves as a powerful tool to predict individual differences in task performances, to dissociate the effect of targeted stimulation in tasks that differ in cognitive complexity, and to pave the way for the development of personalized therapeutics. Copyright © 2018, The Authors. All rights reserved.

关键词： Digital storage