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arXiv 2023年

作者： Nawano, Aya Schroers, Jan Shattuck, Mark D. O’Hern, Corey S. Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department The City College of New York New YorkNY10031 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

Metallic glasses are frequently used as structural materials. Therefore, it is important to develop methods to predict their mechanical response as a function of the microstructure prior to loading. We develop a novel coarse-grained spring network model, which describes the mechanical response of metallic glasses using an equivalent series network of springs, which can break and re-form to mimic atomic rearrangements during deformation. To validate the spring network model, we perform numerical simulations of quasistatic, uniaxial tensile deformation of Lennard-Jones and embedded atom method (EAM) potentials for Cu50Zr50 metallic glasses in the absence of large-scale shear band formation. We consider samples prepared using a wide range of cooling rates and with different amounts of crystalline order. We show that both the Lennard-Jones and EAM models possess qualitatively similar stress σ versus strain γ curves. By specifying five parameters in the spring network model (ultimate strength, strain at ultimate strength, slopes of σ(γ) at γ = 0 and at large strain, and strain at fracture where σ = 0), we can accurately describe the form of the stress-strain curves during uniaxial tension for the computational studies of Cu50Zr50, as well as recent experimental studies of several Zr-based metallic glasses. For the computational studies of Cu50Zr50, we find that the yield strain distribution is shifted to larger strains for slowly cooled glasses compared to rapidly cooled glasses. In addition, the average number of new springs and their rate of formation decreases with decreasing cooling rate. These effects offset each other at large strains, causing the stress-strain curve to become independent of the sample preparation protocol in this regime. In future studies, we will extract the parameters that define the spring network model directly from atomic rearrangements that occur during uniaxial deformation. © 2023, CC BY-NC-ND.

关键词： Metallic glass

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Anomalous suppression of large-scale density fluctuations in classical and quantum spin liquids

arXiv

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arXiv 2025年

作者： Chen, Duyu Samajdar, Rhine Jiao, Yang Torquato, Salvatore Materials Research Laboratory University of California Santa BarbaraCA93106 United States Department of Physics Princeton University PrincetonNJ08544 United States Princeton Center for Theoretical Science Princeton University PrincetonNJ08544 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 Princeton Materials Institute Princeton University PrincetonNJ08540 United States Program in Applied and Computational Mathematics PrincetonNJ08544 United States

Classical spin liquids (CSLs) are intriguing states of matter that do not exhibit long-range magnetic order and are characterized by an extensive ground-state degeneracy. Adding quantum fluctuations, which induce dynamics between these different classical ground states, can give rise to quantum spin liquids (QSLs). QSLs are highly entangled quantum phases of matter characterized by fascinating emergent properties, such as fractionalized excitations and topological order. One such exotic quantum liquid is the Z2 QSL, which can be regarded as a resonating valence bond (RVB) state formed from superpositions of dimer coverings of an underlying lattice. In this work, we unveil a hidden large-scale structural property of archetypal CSLs and QSLs known as hyperuniformity, i.e., normalized infinite-wavelength density fluctuations are completely suppressed in these systems. In particular, we first demonstrate that classical ensembles of close-packed dimers and their corresponding quantum RVB states are perfectly hyperuniform in general. Subsequently, we focus on a ruby-lattice spin liquid that was recently realized in a Rydberg-atom quantum simulator, and show that the QSL remains effectively hyperuniform even in the presence of a finite density of spinon and vison excitations, as long as the dimer constraint is still largely preserved. Moreover, we demonstrate that metrics based on the framework of hyperuniformity can be used to distinguish the QSL from other proximate quantum phases. These metrics can help identify potential QSL candidates, which can then be further analyzed using more advanced, computationally-intensive quantum numerics to confirm their status as true QSLs. © 2025, CC BY-NC-ND.

关键词： Quantum entanglement

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Buildup time of Autler-Townes splitting in attosecond transient absorption spectroscopy

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Physical Review A 2021年第6期103卷 L061102-L061102页

作者： Xiaoxia Wu Zengqiang Yang Shaofeng Zhang Xinwen Ma Jie Liu Difa Ye Institute of Modern Physics Chinese Academy of Sciences Lanzhou 730000 China University of Chinese Academy of Sciences Beijing 100049 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing 100088 China Department of Physics School of Arts and Sciences Shaanxi University of Science and Technology Xi'an 710021 China Graduate School China Academy of Engineering Physics Beijing 100193 China

Although the time-dependent buildup processes of Autler-Townes splitting in attosecond transient absorption spectra have been observed in plenty of experiments, there still exists an open question of when the splitting is maximized, which tells us how fast the system responds to the pump field and how well the adiabatic following is in a slowly varying few-cycle laser pulse. In this Letter we work out a compact formula to quantify the buildup time of the Autler-Townes splitting, which is related to multiple timescales of the laser-atom system, e.g., the Rabi period, the laser cycle, and the pulse duration, according to a universal power law. This scaling law applies not only to singly excited states, but also to doubly excited states in which complex electron-electron interaction gets involved. Our findings have potential applications in calibrating the zero delay in a transient absorption spectroscopy.

关键词： Nonlinear optics Optical transient phenomena Stark effect Ultrafast optics Ultrafast phenomena Adiabatic approximation Rotating wave approximation Schroedinger equation Two-level models

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Understanding degeneracy of two-point correlation functions via debye random media

arXiv

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arXiv 2021年

作者： Skolnick, Murray Torquato, Salvatore Department of Chemistry Princeton University PrincetonNJ08544 United States Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

It is well-known that the degeneracy of two-phase microstructures with the same volume fraction and two-point correlation function S2(r) is generally infinite. To elucidate the degeneracy problem explicitly, we examine Debye random media, which are entirely defined by a purely exponentially decaying two-point correlation function S2(r). In this work, we consider three different classes of Debye random media. First, we generate the "most probable" class using the Yeong-Torquato construction algorithm [Yeong and Torquato, Phys. Rev. E, 57, 495 (1998)]. A second class of Debye random media is obtained by demonstrating that the corresponding two-point correlation functions are effectively realized in the first three space dimensions by certain models of overlapping, polydisperse spheres. A third class is obtained by using the Yeong-Torquato algorithm to construct Debye random media that are constrained to have an unusual prescribed pore-size probability density function. We structurally discriminate these three classes of Debye random media from one another by ascertaining their other statistical descriptors, including the pore-size, surface correlation, chordlength probability density, and lineal-path functions. We also compare and contrast the percolation thresholds as well as the diffusion and fluid transport properties of these degenerate Debye random media. We find that these three classes of Debye random media are generally distinguished by the aforementioned descriptors and their microstructures are also visually distinct from one another. Our work further confirms the well-known fact that scattering information is insufficient to determine the effective physical properties of two-phase media. Additionally, our findings demonstrate the importance of the other two-point descriptors considered here in the design of materials with a spectrum of physical properties. © 2021, CC BY-SA.

关键词： Random processes

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Heat transport in liquid water from first-principles and deep-neural-network simulations

arXiv

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arXiv 2021年

作者： Tisi, Davide Zhang, Linfeng Bertossa, Riccardo Wang, Han Car, Roberto Baroni, Stefano SISSA - Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States CNR Istituto Officina Dei Materiali SISSA Unit Trieste34136 Italy

We compute the thermal conductivity of water within linear response theory from equilibrium molecular dynamics simulations, by adopting two different approaches. In one, the potential energy surface (PES) is derived on the fly from the electronic ground state of density functional theory (DFT) and the corresponding analytical expression is used for the energy flux. In the other, the PES is represented by a deep neural network (DNN) trained on DFT data, whereby the PES has an explicit local decomposition and the energy flux takes a particularly simple expression. By virtue of a gauge invariance principle, established by Marcolongo, Umari, and Baroni, the two approaches should be equivalent if the PES were reproduced accurately by the DNN model. We test this hypothesis by calculating the thermal conductivity, at the GGA (PBE) level of theory, using the direct formulation and its DNN proxy, finding that both approaches yield the same conductivity, in excess of the experimental value by approximately 60%. Besides being numerically much more efficient than its direct DFT counterpart, the DNN scheme has the advantage of being easily applicable to more sophisticated DFT approximations, such as meta-GGA and hybrid functionals, for which it would be hard to derive analytically the expression of the energy flux. We find in this way, that a DNN model, trained on meta-GGA (SCAN) data, reduce the deviation from experiment of the predicted thermal conductivity by about 50%, leaving the question open as to whether the residual error is due to deficiencies of the functional, to a neglect of nuclear quantum effects in the atomic dynamics, or, likely, to a combination of the two. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods

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Current Opinion in Structural Biology 2023年 78卷 102517页

作者： Banerjee, Anupam Saha, Satyaki Tvedt, Nathan C. Yang, Lee-Wei Bahar, Ivet Computational and Systems Biology University of Pittsburgh School of Medicine Pittsburgh 15261 PA United States Computational and Applied Mathematics and Statistics The College of William and Mary Williamsburg 23185 VA United States Institute of Bioinformatics and Structural Biology and PhD Program in Biomedical Artificial Intelligence National Tsing Hua University Hsinchu 300044 Taiwan Physics Division National Center for Theoretical Sciences Taipei 106319 Taiwan

Proteins sample an ensemble of conformers under physiological conditions, having access to a spectrum of modes of motions, also called intrinsic dynamics. These motions ensure the adaptation to various interactions in the cell, and largely assist in, if not determine, viable mechanisms of biological function. In recent years, machine learning frameworks have proven uniquely useful in structural biology, and recent studies further provide evidence to the utility and/or necessity of considering intrinsic dynamics for increasing their predictive ability. Efficient quantification of dynamics-based attributes by recently developed physics-based theories and models such as elastic network models provides a unique opportunity to generate data on dynamics for training ML models towards inferring mechanisms of protein function, assessing pathogenicity, or estimating binding affinities. © 2022 The Authors

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Collective Neutrino Oscillations in Three Flavors on Qubit and Qutrit Processors

arXiv

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arXiv 2025年

作者： Spagnoli, Luca Goss, Noah Roggero, Alessandro Rrapaj, Ermal Cervia, Michael J. Patwardhan, Amol V. Naik, Ravi K. Balantekin, A. Baha Younis, Ed Santiago, David I. Siddiqi, Irfan Aldaihan, Sheakha Physics Department University of Trento Via Sommarive 14 TrentoI-38123 Italy INFN-TIFPA Trento Institute of Fundamental Physics and Applications Trento Italy Quantum Nanoelectronics Laboratory Department of Physics University of California at Berkeley BerkeleyCA94720 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Physics University of California BerkeleyCA94720 United States RIKEN iTHEMS Saitama Wako351-0198 Japan Department of Physics University of Washington SeattleWA98195 United States School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Department of Physics New York Institute of Technology New YorkNY10023 United States Department of Physics University of Wisconsin MadisonWI53706 United States Department of Physics Gettysburg College GettysburgPA17325 United States

Collective neutrino flavor oscillations are of primary importance in understanding the dynamic evolution of core-collapse supernovae and subsequent terrestrial detection, but also among the most challenging aspects of numerical simulations. This situation is complicated by the quantum many-body nature of the problem due to neutrino-neutrino interactions which demands a quantum treatment. An additional complication is the presence of three flavors, which often is approximated by the electron flavor and a heavy lepton flavor. In this work, we provide both qubit and qutrit encodings for all three flavors, and develop optimized quantum circuits for the time evolution and analyze the Trotter error. We conclude our study with a hardware experiment of a system of two neutrinos with superconducting hardware: the IBM Torino device for qubits and AQT device for qutrits. We find that error mitigation greatly helps in obtaining a signal consistent with simulations. While hardware results are comparable at this stage, we expect the qutrit setup to be more convenient for large-scale simulations since it does not suffer from probability leakage into nonphsycial qubit space, unlike the qubit setup. Copyright © 2025, The Authors. All rights reserved.

关键词： Neutrons

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Deep Learning-Based Automated Post-Operative Gross Tumor Volume Segmentation in Glioblastoma Patients

arXiv

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arXiv 2024年

作者： Muthusivarajan, Rajarajeswari Celaya, Adrian Farhat, Maguy Talpur, Wasif Langshaw, Holly White, Victoria Elliott, Andrew Thrower, Sara Schellingerhout, Dawid Fuentes, David Chung, Caroline Department of Imaging Physics University of Texas MD Anderson Cancer Center HoustonTX77030 United States Department of Radiation Oncology University of Texas MD Anderson Cancer Center HoustonTX77030 United States Department of Neuroradiology Division of Diagnostic Imaging University of Texas MD Anderson Cancer Center HoustonTX77030 United States Department of Computational Applied Mathematics and Operations Research Rice University HoustonTX77005 United States

Precise automated delineation of post-operative gross tumor volume in glioblastoma cases is challenging and time-consuming owing to the presence of edema and the deformed brain tissue resulting from the surgical tumor resection. The pre-operative glioma segmentation models that have been developed to date struggle to define the post-operative cavity and any residual tumor;however, the postoperative tumor and cavity are what are tracked to assess treatment response. To develop a model for automated delineation of post-operative gross tumor volumes in glioblastoma, we proposed a novel 3D double pocket U-Net architecture that has two parallel pocket U-Nets. Both U-Nets were trained simultaneously with two different subsets of MRI sequences and the output from the models was combined to do the final prediction. We strategically combined the MRI input sequences (T1, T2, T1C, FL) for model training to achieve improved segmentation accuracy. The dataset comprised 82 postoperative studies collected from 23 glioblastoma patients who underwent maximal safe tumor resection. All had gross tumor volume (GTV) segmentations performed by human experts, and these were used as a reference standard. The results of 3D double pocket U-Net were compared with baseline 3D pocket U-Net models and the ensemble of 3D pocket U-Net models. All the models were evaluated with fivefold cross-validation in terms of the Dice similarity coefficient and Hausdorff distance. Our proposed double U-Net model trained with input sequences [T1, T1C, FL + T2, T1C] achieved a better mean Dice score of 0.8585 and Hausdorff distance of 4.1942 compared to all the baseline models and ensemble models trained. The presence of infiltrating tumors and vasogenic edema in the post-operative MRI scans tends to reduce segmentation accuracy when considering the MRI sequences T1, T2, T1C, and FL together for model training. The double U-Net approach of combining subsets of the MRI sequences as distinct inputs for model trai

关键词： Convolutional neural networks

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Efficient First-Principles Framework for Overdamped Phonon Dynamics and Anharmonic Electron-Phonon Coupling in Superionic Materials

arXiv

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arXiv 2025年

作者： Wang, Yuxuan Zacharias, Marios Zhang, Xiao Pant, Nick Even, Jacky Poudeu, Pierre F.P. Kioupakis, Emmanouil Department of Materials Science and Engineering University of Michigan Ann ArborMI48109 United States Univ Rennes INSA Rennes CNRS Institut FOTON - UMR 6082 RennesF-35000 France Applied Physics Program University of Michigan Ann ArborMI48109 United States Oden Institute for Computational Engineering and Sciences The University of Texas at Austin AustinTX78712 United States

Relying on the anharmonic special displacement method, we introduce an ab initio quasistatic polymorphous framework to describe local disorder, anharmonicity, and electron-phonon coupling in superionic conductors. Using the example of cubic Cu2Se, we show that positional polymorphism yields extremely overdamped anharmonic vibrations while preserving transverse acoustic phonons, consistent with experiments. We also demonstrate well-defined electronic band structures with large band gap openings due to polymorphism of 1.0 eV and calculate anharmonic electron-phonon renormalization, yielding band gap narrowing with increasing temperature in agreement with previous measurements. Our approach opens the way for efficient ab initio electronic structure calculations in superionic crystals to elucidate their compelling high figure-of-merit. © 2025, CC BY-NC-ND.

关键词： Energy gap

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Cross sections for the electron-impact excitations Ã1B1 and B̃1A1 of H2O determined by high-energy electron scattering

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Physical Review A 2021年第3期103卷 032808-032808页

作者： Wei-Qing Xu Zi-Ru Ma Yi-Geng Peng Xiao-Jiao Du Yuan-Chen Xu Li-Han Wang Bo Li Hao-Ran Zhang Bei-Yuan Zhang Jian-Hui Zhu Shu-Xing Wang Yong Wu Jian-Guo Wang Lin-Fan Zhu School of Physical Science and Technology ShanghaiTech University Shanghai 201210 China Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of China Hefei Anhui 230026 China Department of Applied Physics Nanjing University of Science and Technology Nanjing 210094 China Institute of Applied Physics and Computational Mathematics Beijing 100088 China

Understanding the role of inelastic electron scattering in water is of fundamental importance in various fields ranging from atmospheric chemistry to radiation biology. The lack of accurate excitation cross sections for water results in a large uncertainty, for example, in the track structure simulation for modeling radiation damage to DNA. The large differences of the integral cross sections (ICSs) for the optical-allowed excitations Ã1B1 and B̃1A1 of H2O among experiments and theories have been maintaining for decades. To resolve this issue, by combining the ab initio calculation with the electron correlation being taken into account accurately, we compare the ICSs determined by high-energy electron scattering with the existing experiments at low and intermediate energies and theories, where the present experiment eliminated the errors from the spectral deconvolution in low-energy measurements. Our work provides a recommendation for the ICSs of the two excitations from thresholds up to several keV, and suggests the existing selected ICSs for H2O be revised for accurate modeling radiation effects in biological matter and describing the transport properties of electrons in aqueous systems.

关键词： Atomic & molecular collisions Electron & positron scattering Electronic excitation & ionization Electronic structure of atoms & molecules Electronic transitions Molecular spectra Molecules Configuration interaction High-resolution electron energy loss spectroscopy

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