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arXiv 2023年

作者： Ma, Huanyu Wang, Xincheng Zhang, Linxuan Zou, Zhihan Yuan, Junyang Ma, Yixuan Lv, Rujin Shen, Zhenjie Yan, Tianmin Weidemüller, Matthias Ye, Difa Jiang, Yuhai Center for Transformative Science School of Physical Science and Technology ShanghaiTech University Shanghai201210 China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201210 China University of Chinese Academy of Sciences Beijing100049 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing100088 China Graduate School China Academy of Engineering Physics Beijing100193 China Physikalisches Institut Universität Heidelberg Im Neuenheimer Feld 226 Heidelberg69120 Germany

Photoionization of the rubidium (Rb) atoms cooled in a magneto-optical trap, characterized by the coexistence of the ground 5S1/2 and the excited 5P3/2 states, is investigated experimentally and theoretically with the 400 nm femtosecond laser pulses at intensities of I = 3×109 W/cm2 −4.5×1012 W/cm2. Recoil-ion momentum distribution (RIMD) of Rb+ exhibits rich ring-like structures and their energies correspond to one-photon ionization of the 5P3/2 state, two-photon and three-photon ionizations of the 5S1/2 state, respectively. With the increasing of I, we find that experimental signals near zero-momentum (NZM) in RIMDs resulted from the 5P3/2 state enhance dramatically and its peaked Rb+ momenta dwindle obviously while that from the 5S1/2 state is maintained. Meanwhile, the ion-yield ratio of the 5S1/2 over the 5P3/2 states varies from I to I1.5 as I increases. These features indicate a transition from perturbative ionization to strong-perturbative ionization for the 5P3/2 state. Numerical simulations by solving the time-dependent Schrödinger equation (TDSE) can qualitatively explain the measurements of RIMD, photoion angular distributions, as well as ion-yield ratio. However, some discrepancies still exist, especially for the NZM dip, which could stem from the electron-electron correlation that is neglected in the present TDSE simulations since we have adopted the single-active-electron approximation. Copyright © 2023, The Authors. All rights reserved.

关键词： Photons

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Efficient Power Transfer via Induced Virtual Critical Coupling in an Overcoupled Silicon Nitride Microresonator

Efficient Power Transfer via Induced Virtual Critical Coupli...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Jakob Hinney Seunghwi Kim Graydon J. K. Flatt Ipshita Datta Andrea Alù Michal Lipson Department of Electrical Engineering Columbia University New York NY USA Photonics Initiative Advanced Science Research Center City University of New York New York NY USA Department of Applied Physics and Applied Mathematics Columbia University New York NY USA Physics Program Graduate Center City University of New York New York NY USA

We induce virtual critical coupling in an overcoupled photonic microresonator using complex-frequency pulses. We observe efficient cavity storage as an incident pulse of time constant 138 ps suppresses the resonant tr... 详细信息

ISBN: (纸本)9781957171258

关键词： Continuous wave operation Coupled mode theory Electrooptical modulators Optical resonators Pulse shaping Silicon nitride

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Learning the solution operator of parametric partial differential equations with physics-informed deeponets

arXiv

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arXiv 2021年

作者： Wang, Sifan Wang, Hanwen Perdikaris, Paris Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania PhiladelphiaPA19104 United States Department of Mechanichal Engineering and Applied Mechanics University of Pennsylvania PhiladelphiaPA19104 United States

Deep operator networks (DeepONets) are receiving increased attention thanks to their demonstrated capability to approximate nonlinear operators between infinite-dimensional Banach spaces. However, despite their remarkable early promise, they typically require large training data-sets consisting of paired input-output observations which may be expensive to obtain, while their predictions may not be consistent with the underlying physical principles that generated the observed data. In this work, we propose a novel model class coined as physics-informed DeepONets, which introduces an effective regularization mechanism for biasing the outputs of DeepOnet models towards ensuring physical consistency. This is accomplished by leveraging automatic differentiation to impose the underlying physical laws via soft penalty constraints during model training. We demonstrate that this simple, yet remarkably effective extension can not only yield a significant improvement in the predictive accuracy of DeepOnets, but also greatly reduce the need for large training data-sets. To this end, a remarkable observation is that physics-informed DeepONets are capable of solving parametric partial differential equations (PDEs) without any paired input-output observations, except for a set of given initial or boundary conditions. We illustrate the effectiveness of the proposed framework through a series of comprehensive numerical studies across various types of PDEs. Strikingly, a trained physics informed DeepOnet model can predict the solution of O(103) time-dependent PDEs in a fraction of a second - up to three orders of magnitude faster compared a conventional PDE solver. The data and code accompanying this manuscript are publicly available at https://***/PredictiveIntelligenceLab/Physics-informed-DeepONets. Copyright © 2021, The Authors. All rights reserved.

关键词： Mathematical operators

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Long-time integration of parametric evolution equations with physics-informed deeponets

arXiv

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arXiv 2021年

作者： Wang, Sifan Perdikaris, Paris Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania PhiladelphiaPA19104 United States Department of Mechanichal Engineering and Applied Mechanics University of Pennsylvania PhiladelphiaPA19104 United States

Ordinary and partial differential equations (ODEs/PDEs) play a paramount role in analyzing and simulating complex dynamic processes across all corners of science and engineering. In recent years machine learning tools are aspiring to introduce new effective ways of simulating PDEs, however existing approaches are not able to reliably return stable and accurate predictions across long temporal horizons. We aim to address this challenge by introducing an effective framework for learning infinite-dimensional operators that map random initial conditions to associated PDE solutions within a short time interval. Such latent operators can be parametrized by deep neural networks that are trained in an entirely self-supervised manner without requiring any paired input-output observations. Global long-time predictions across a range of initial conditions can be then obtained by iteratively evaluating the trained model using each prediction as the initial condition for the next evaluation step. This introduces a new approach to temporal domain decomposition that is shown to be effective in performing accurate long-time simulations for a wide range of parametric ODE and PDE systems, from wave propagation, to reaction-diffusion dynamics and stiff chemical kinetics, all at a fraction of the computational cost needed by classical numerical solvers. © 2021, CC BY-NC-SA.

关键词： Domain decomposition methods

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Local order metrics for many-particle systems across length scales

arXiv

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arXiv 2024年

作者： Maher, Charles Emmett Torquato, Salvatore Department of Chemistry Princeton University New Jersey08544 United States Department of Chemistry Princeton University PrincetonNJ08544 United States Department of Physics Princeton University PrincetonNJ08544 United States Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Formulating order metrics that sensitively quantify the degree of order/disorder in many-particle systems in d-dimensional Euclidean space Rd across length scales is an outstanding challenge in physics, chemistry, and materials science. Since an infinite set of n-particle correlation functions is required to fully characterize a system, one must settle for a reduced set of structural information, in practice. We initiate a program to use the local number variance σN2 (R) associated with a spherical sampling window of radius R (which encodes pair correlations) and an integral measure derived from it ΣN(Ri, Rj) that depends on two specified radial distances Ri and Rj. Across the first three space dimensions (d = 1, 2, 3), we find these metrics can sensitively describe and categorize the degree of order/disorder of 41 different models of antihyperuniform, nonhyperuniform, disordered hyperuniform, and ordered hyperuniform many-particle systems at a specified length scale R. Using our local variance metrics, we demonstrate the importance of assessing order/disorder with respect to a specific value of R. These local order metrics could also aid in the inverse design of structures with prescribed length-scale-specific degrees of order/disorder that yield desired physical properties. In future work, it would be fruitful to explore the use of higher-order moments of the number of points within a spherical window of radius R [S. Torquato et al., Phys. Rev. X, 11, 021028 (2021)] to devise even more sensitive order metrics. © 2024, CC BY.

关键词： Inverse problems

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Ordered and disordered stealthy hyperuniform point patterns across spatial dimensions

arXiv

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arXiv 2024年

作者： Morse, Peter K. Steinhardt, Paul J. Torquato, Salvatore 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 Princeton Center for Theoretical Science Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

In previous work [Phys. Rev. X 5, 021020 (2015)], it was shown that stealthy hyperuniform systems can be regarded as hard spheres in Fourier-space in the sense that the the structure factor is exactly zero in a spherical region around the origin in analogy with the pair-correlation function of real-space hard spheres. In this work, we exploit this correspondence to confirm that the densest Fourier-space hard-sphere system is that of a Bravais lattice. This is in contrast to real-space hard-spheres, whose densest configuration is conjectured to be disordered. We also extend the virial series previously suggested for disordered stealthy hyperuniform systems to higher dimensions in order to predict spatial decorrelation as function of dimension. This prediction is then borne out by numerical simulations of disordered stealthy hyperuniform ground states in dimensions d = 2-8. © 2024, CC BY.

关键词： Ground state

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Design and Characterization of Type-Ii Superlattice-Based Inas/Alsb/Gasb Detector Structure

SSRN

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SSRN 2024年

作者： Kızılkaya, Kürşat Öztürk, Mustafa Kemal Hoştut, Mustafa Ergün, Yüksel Özçelik, Süleyman Turkish Aerospace Fethiye District Havacılık Avenue No:17 Ankara06980 Turkey Advanced Technologies Program Graduate School of Natural and Applied Sciences Gazi University Ankara06560 Turkey Photonics Application and Research Center Gazi University Ankara06560 Turkey Physics Department of Science Faculty Gazi University Ankara06560 Turkey Physics Department of Science Faculty Eskisehir Technical University Eskisehir26470 Turkey Photonics Department of Applied Science Faculty Gazi University Ankara06560 Turkey Department of Mathematics and Science Education Faculty of Education Akdeniz University Antalya07058 Turkey

In this study, N-Structured based InAs/AlSb/GaSb Type-II Superlattice pbin type detector structures are investigated. These systems make absorption in infrared region in electromagnetic spectrum as detectors. Structural characterization of these systems is aimed. n-type GaSb is used as the substrate. InAs/AlSb/GaSb-based repetitive SL layers are formed as n-layer, i-layer, p-layer and p-contact layer from the bottom InAsSb layer to the top GaSb cap layer for 120, 300, 15 and 80 periods, respectively. Structural characterization of all layers is made by using SIMS and XRD systems analyses. The structural depth profile of the SL layers has been presented comparatively through SIMS analysis. The interfacial transitions observed in the depth profile are consistent with the designed T2SL layers. By using XRD characterization, crystal quality parameters, lattice constants and periodicity and deviation of super lattice peak from substrate are determined. Such dislocation density and strain are measured AS 7.85x107cm-2 and 6.4x10-3 nm, respectively. SEM analysis is used to examine top layer morphology and cross-section of the structure. The cross-sectional measurements allowed the observation of interfacial transitions within the SL structure. In this study, super lattice with high crystal quality is achieved and results show that T2SL detector structures are succeeded. © 2024, The Authors. All rights reserved.

关键词： III-V semiconductors

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Hyperuniformity of maximally random jammed packings of hyperspheres across spatial dimensions

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Physical Review E 2023年第6期108卷 064602-064602页

作者： Charles Emmett Maher Yang Jiao Salvatore Torquato Department of Chemistry Princeton University Princeton New Jersey 08544 USA Materials Science and Engineering Arizona State University Tempe Arizona 85287 USA Department of Physics Arizona State University Tempe Arizona 85287 USA Department of Physics Princeton University Princeton New Jersey 08544 USA Princeton Materials Institute Princeton University Princeton New Jersey 08544 USA Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

The maximally random jammed (MRJ) state is the most random (i.e., disordered) configuration of strictly jammed (mechanically rigid) nonoverlapping objects. MRJ packings are hyperuniform, meaning their long-wavelength density fluctuations are anomalously suppressed compared to typical disordered systems, i.e., their structure factors S(k) tend to zero as the wave number |k| tends to zero. Here we show that generating high-quality strictly jammed states for Euclidean space dimensions d=3,4, and 5 is of paramount importance in ensuring hyperuniformity and extracting precise values of the hyperuniformity exponent α>0 for MRJ states, defined by the power-law behavior of S(k)∼|k|α in the limit |k|→0. Moreover, we show that for fixed d it is more difficult to ensure jamming as the particle number N increases, which results in packings that are nonhyperuniform. Free-volume theory arguments suggest that the ideal MRJ state does not contain rattlers, which act as defects in numerically generated packings. As d increases, we find that the fraction of rattlers decreases substantially. Our analysis of the largest truly jammed packings suggests that the ideal MRJ packings for all dimensions d≥3 are hyperuniform with α=d−2, implying the packings become more hyperuniform as d increases. The differences in α between MRJ packings and the recently proposed Manna-class random close packed (RCP) states, which were reported to have α=0.25 in d=3 and be nonhyperuniform (α=0) for d=4 and d=5, demonstrate the vivid distinctions between the large-scale structure of RCP and MRJ states in these dimensions. Our paper clarifies the importance of the link between true jamming and hyperuniformity and motivates the development of an algorithm to produce rattler-free three-dimensional MRJ packings.

关键词： Granular solids

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Fast PDE-constrained optimization via self-supervised operator learning: A preprint

arXiv

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arXiv 2021年

作者： Wang, Sifan Bhouri, Mohamed Aziz Perdikaris, Paris Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania PhiladelphiaPA19104 United States Department of Mechanichal Engineering and Applied Mechanics University of Pennsylvania PhiladelphiaPA19104 United States

Design and optimal control problems are among the fundamental, ubiquitous tasks we face in science and engineering. In both cases, we aim to represent and optimize an unknown (black-box) function that associates a performance/outcome to a set of controllable variables through an experiment. In cases where the experimental dynamics can be described by partial differential equations (PDEs), such problems can be mathematically translated into PDE-constrained optimization tasks, which quickly become intractable as the number of control variables and the cost of experiments increases. In this work we leverage physics-informed deep operator networks (DeepONets) – a self-supervised framework for learning the solution operator of parametric PDEs – to build fast and differentiable surrogates for rapidly solving PDE-constrained optimization problems, even in the absence of any paired input-output training data. The effectiveness of the proposed framework will be demonstrated across different applications involving continuous functions as control or design variables, including time-dependent optimal control of heat transfer, and drag minimization of obstacles in Stokes flow. In all cases, we observe that DeepONets can minimize high-dimensional cost functionals in a matter of seconds, yielding a significant speed up compared to traditional adjoint PDE solvers that are typically costly and limited to relatively low-dimensional control/design parametrizations. © 2021, CC BY-NC-SA.

关键词： Constrained optimization

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Communicationmachine Learning-Based QSAR and LB-PaCS-MD Guided Design of SARs-CoV-2 Main Protease Inhibitors

SSRN

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SSRN 2024年

作者： Toopradab, Borwornlak Xie, Wanting Duan, Lian Hengphasatporn, Kowit Harada, Ryuhei Sinsulpsiri, Silpsiri Shigeta, Yasuteru Shi, Liyi Maitarad, Phornphimon Rungrotmongkol, Thanyada Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok10330 Thailand Center of Excellence in Biocatalyst and Sustainable Biotechnology Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand Research Center of Nano Science and Technology Department of Chemistry College of Science Shanghai University Shanghai200444 China University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan Graduate School of Pure and Applied Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki305–8571 Japan Emerging Industries Institute Shanghai University Zhejiang Jiaxing314006 China

The global outbreak of the COVID-19 pandemic caused by the SARS-CoV-2 virus had led to profound respiratory health implications. This study focused on designing organoselenium-based inhibitors targeting the SARS-CoV-2 main protease (Mpro). The ligand-binding pathway sampling method based on parallel cascade selection molecular dynamics (LB-PaCS-MD) simulations was employed to elucidate plausible paths and conformations of ebselen, a synthetic organoselenium drug, within the Mpro catalytic site. Ebselen effectively engaged the active site, adopting proximity to H41 and interacting through the benzoisoselenazole ring in a π-π T-shaped arrangement, with an additional π-sulfur interaction with C145. In addition, the ligand-based drug design using the QSAR with GFA-MLR, RF, and ANN models were employed for biological activity prediction. The QSAR-ANN model showed robust statistical performance, with an R2training exceeding 0.98 and an RMSEtest of 0.21, indicating its suitability for predicting biological activities. Integration the ANN model with the LB-PaCS-MD insights enabled the rational design of novel compounds anchored in the ebselen core structure, identifying promising candidates with favorable predicted IC50 values. The designed compounds exhibited suitable drug-like characteristics and adopted an active conformation similar to ebselen, inhibiting Mpro function. These findings represent a synergistic approach merging ligand and structure-based drug design;with the potential to guide experimental synthesis and enzyme assay testing. © 2024, The Authors. All rights reserved.

关键词： Coronavirus

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