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Experimental Evidence of Kinetic Effects in Indirect-Drive Inertial Confinement Fusion Hohlraums

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Physical Review Letters 2018年第19期120卷 195001-195001页

作者： L. Q. Shan H. B. Cai W. S. Zhang Q. Tang F. Zhang Z. F. Song B. Bi F. J. Ge J. B. Chen D. X. Liu W. W. Wang Z. H. Yang W. Qi C. Tian Z. Q. Yuan B. Zhang L. Yang J. L. Jiao B. Cui W. M. Zhou L. F. Cao C. T. Zhou Y. Q. Gu B. H. Zhang S. P. Zhu X. T. He Science and Technology on Plasma Physics Laboratory Research Center of Laser Fusion CAEP Mianyang 621900 China Institute of Applied Physics and Computational Mathematics Beijing 100094 China HEDPS Center for Applied Physics and Technology Peking University Beijing 100871 China IFSA Collaborative Innovation Center Shanghai Jiao Tong University Shanghai 200240 China Graduate School China Academy of Engineering Physics P.O. Box 2101 Beijing 100088 China

We present the first experimental evidence supported by simulations of kinetic effects launched in the interpenetration layer between the laser-driven hohlraum plasma bubbles and the corona plasma of the compressed pellet at the Shenguang-III prototype laser facility. Solid plastic capsules were coated with carbon-deuterium layers; as the implosion neutron yield is quenched, DD fusion yield from the corona plasma provides a direct measure of the kinetic effects inside the hohlraum. An anomalous large energy spread of the DD neutron signal (∼282 keV) and anomalous scaling of the neutron yield with the thickness of the carbon-deuterium layers cannot be explained by the hydrodynamic mechanisms. Instead, these results can be attributed to kinetic shocks that arise in the hohlraum-wall–ablator interpenetration region, which result in efficient acceleration of the deuterons (∼28.8 J, 0.45% of the total input laser energy). These studies provide novel insight into the interactions and dynamics of a vacuum hohlraum and near-vacuum hohlraum.

关键词： Indirect drive Inertial confinement fusion

Isotope effects in liquid water via deep potential molecular dynamics

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arXiv 2019年

作者： Ko, Hsin-Yu Zhang, Linfeng Santra, Biswajit Wang, Han Weinan, E. DiStasio, Robert A. Cara, Roberto Department of Chemistry Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Physics Temple University PhiladelphiaPA19122 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Department of Mathematics Princeton University PrincetonNJ08544 United States Department of Chemistry and Chemical Biology Cornell University IthacaNY14853 United States Department of Physics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States

A comprehensive microscopic understanding of ambient liquid water is a major challenge for ab initio simulations as it simultaneously requires an accurate quantum mechanical description of the underlying potential energy surface (PES) as well as extensive sampling of configuration space. Due to the presence of light atoms (e.g., H or D), nuclear quantum fluctuations lead to observable changes in the structural properties of liquid water (e.g., isotope effects), and therefore provide yet another challenge for ab initio approaches. In this work, we demonstrate that the combination of dispersion-inclusive hybrid density functional theory (DFT), the Feynman discretized path-integral (PI) approach, and machine learning (ML) constitutes a versatile ab initio based framework that enables extensive sampling of both thermal and nuclear quantum fluctuations on a quite accurate underlying PES. In particular, we employ the recently developed deep potential molecular dynamics (DPMD) model - a neural-network representation of the ab initio PES - in conjunction with a PI approach based on the generalized Langevin equation (PIGLET) to investigate how isotope effects influence the structural properties of ambient liquid H2O and D2O. Through a detailed analysis of the interference differential cross sections as well as several radial and angular distribution functions, we demonstrate that this approach can furnish a semi-quantitative prediction of these subtle isotope effects. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

Tuning cooperative behavior in games with nonlinear opinion dynamics

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arXiv 2021年

作者： Park, Shinkyu Bizyaeva, Anastasia Kawakatsu, Mari Franci, Alessio Leonard, Naomi Ehrich Computer Electrical and Mathematical Science and Engineering Division Thuwal23955-6900 Saudi Arabia Department of Mechanical and Aerospace Engineering Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Mathematics Department National Autonomous University of Mexico Mexico City04510 Mexico

We examine the tuning of cooperative behavior in repeated multi-agent games using an analytically tractable, continuous-time, nonlinear model of opinion dynamics. Each modeled agent updates its real-valued opinion about each available strategy in response to payoffs and other agent opinions, as observed over a network. We show how the model provides a principled and systematic means to investigate behavior of agents that select strategies using rationality and reciprocity, key features of human decision-making in social dilemmas. For two-strategy games, we use bifurcation analysis to prove conditions for the bistability of two equilibria and conditions for the first (second) equilibrium to reflect all agents favoring the first (second) strategy. We prove how model parameters, e.g., level of attention to opinions of others (reciprocity), network structure, and payoffs, influence dynamics and, notably, the size of the region of attraction to each stable equilibrium. We provide insights by examining the tuning of the bistability of mutual cooperation and mutual defection and their regions of attraction for the repeated prisoner's dilemma and the repeated multi-agent public goods game. Our results generalize to games with more strategies, heterogeneity, and additional feedback dynamics, such as those designed to elicit cooperation. Copyright © 2021, The Authors. All rights reserved.

关键词： Bifurcation (mathematics)

Antithetic Multilevel Methods for Elliptic and Hypo-Elliptic Diffusions with Applications

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arXiv 2024年

作者： Iguchi, Yuga Jasra, Ajay Maama, Mohamed Beskos, Alexandros Department of Statistical Science University College London LondonWC1E 6BT United Kingdom School of Data Science The Chinese University of Hong Kong Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we present a new antithetic multilevel Monte Carlo (MLMC) method for the estimation of expectations with respect to laws of diffusion processes that can be elliptic or hypo-elliptic. In particular, we consider the case where one has to resort to time discretization of the diffusion and numerical simulation of such schemes. Motivated by recent developments, we introduce a new MLMC estimator of expectations, which does not require simulation of intractable Lévy areas but has a weak error of order 2 and achieves the optimal computational complexity. We then show how this approach can be used in the context of the filtering problem associated to partially observed diffusions with discrete time observations. We illustrate with numerical simulations that our new approaches provide efficiency gains for several problems relative to some existing methods. © 2024, CC BY.

关键词： Diffusion

Learning binary latent variable models: A tensor eigenpair approach

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arXiv 2018年

作者： Jaffe, Ariel Weiss, Roi Carmi, Shai Kluger, Yuval Nadler, Boaz Dept. of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot7610001 Israel Braun School of Public Health and Community Medicine Hebrew University of Jerusalem Jerusalem9112102 Israel Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States Dept. of Pathology Yale Cancer Center Yale University School of Medicine New HavenCT06520 United States Program of Applied Mathematics Yale University New HavenCT06511 United States

Latent variable models with hidden binary units appear in various applications. Learning such models, in particular in the presence of noise, is a challenging computational problem. In this paper we propose a novel spectral approach to this problem, based on the eigenvectors of both the second order moment matrix and third order moment tensor of the observed data. We prove that under mild non-degeneracy conditions, our method consistently estimates the model parameters at the optimal parametric rate. Our tensor-based method generalizes previous orthogonal tensor decomposition approaches, where the hidden units were assumed to be either statistically independent or mutually exclusive. We illustrate the consistency of our method on simulated data and demonstrate its usefulness in learning a common model for population mixtures in genetics. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

Deep potentials for materials science

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材料展望（英文）

材料展望（英文） 2022年第2期 89-115页

作者： Tongqi Wen Linfeng Zhang Han Wang Weinan E David J Srolovitz Department of Mechanical Engineering The University of Hong KongHong KongHong Kong Special Administrative Region of China DP Technology BeijingPeople's Republic of China AI for Science Institute BeijingPeople's Republic of China Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijingPeople's Republic of China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingPeople's Republic of China AI for Science Institute BeijingPeople's Republic of China School of Mathematical Sciences Peking UniversityBeijingPeople's Republic of China Department of Mathematics and Program in Applied and Computational Mathematics Princeton UniversityPrincetonNJUnited States of America Department of Mechanical Engineering The University of Hong KongHong KongHong Kong Special Administrative Region of China International Digital Economy Academy(IDEA) ShenzhenPeople's Republic of China

To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and been widely applied;*** learning potentials(MLPs).One recently developed type of MLP is the deep potential(DP)*** this review,we provide an introduction to DP methods in computational materials *** theory underlying the DP method is presented along with a step-by-step introduction to their development and *** also review materials applications of DPs in a wide range of materials *** DP Library provides a platform for the development of DPs and a database of extant *** discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.

关键词： deep potential atomistic simulation machine learning potential neural network

Calibrated Langevin-dynamics simulations of intrinsically disordered proteins

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Physical Review E 2014年第4期90卷 042709-042709页

作者： W. Wendell Smith Po-Yi Ho Corey S. O'Hern Department of Physics Yale University New Haven Connecticut 06520-8120 USA Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven Connecticut 06520-8114 USA Department of Mechanical Engineering and Materials Science Yale University New Haven Connecticut 06520-8286 USA Department of Applied Physics Yale University New Haven Connecticut 06520-8267 USA Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA

We perform extensive coarse-grained (CG) Langevin dynamics simulations of intrinsically disordered proteins (IDPs), which possess fluctuating conformational statistics between that for excluded volume random walks and collapsed globules. Our CG model includes repulsive steric, attractive hydrophobic, and electrostatic interactions between residues and is calibrated to a large collection of single-molecule fluorescence resonance energy transfer data on the interresidue separations for 36 pairs of residues in five IDPs: α−, β−, and γ-synuclein, the microtubule-associated protein τ, and prothymosin α. We find that our CG model is able to recapitulate the average interresidue separations regardless of the choice of the hydrophobicity scale, which shows that our calibrated model can robustly capture the conformational dynamics of IDPs. We then employ our model to study the scaling of the radius of gyration with chemical distance in 11 known IDPs. We identify a strong correlation between the distance to the dividing line between folded proteins and IDPs in the mean charge and hydrophobicity space and the scaling exponent of the radius of gyration with chemical distance along the protein.

关键词： LANGEVIN equations SIMULATION methods & models CONFORMATIONAL analysis MICROTUBULE-associated proteins HYDROPHOBIC compounds

Atomic-number dependence of the magnetic-sublevel population in the autoionization state formed in dielectronic recombination

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Physical Review A 2014年第6期90卷 062702-062702页

作者： Zhimin Hu Yueming Li Xiaoying Han Daiji Kato Xiaomin Tong Hirofumi Watanabe Nobuyuki Nakamura Institute for Laser Science The University of Electro-Communications Chofu Tokyo 182-8585 Japan Research Center of Laser Fusion China Academy of Engineering Physics Mianyang 621900 China Physics Institute Heidelberg University Im Neuenheimer Feld 226 69120 Heidelberg Germany Institute of Applied Physics and Computational Mathematics P. O. Box 8009 Beijing 100088 China National Institute for Fusion Science Toki Gifu 509-5292 Japan Department of Fusion Science The Graduate University of Advanced Studies (SOKENDAI) Toki Gifu 509-5292 Japan Division of Materials Science Faculty of Pure and Applied Science University of Tsukuba Tsukuba Ibaraki 305-8573 Japan Center for Computational Sciences University of Tsukuba Tsukuba Ibaraki 305-8573 Japan Center of Applied Superconductivity and Sustainable Energy Research Chubu University Kasugai Aichi 487-8501 Japan

The magnetic-sublevel population of the autoionization state formed in dielectronic recombination (DR) of highly charged heavy ions has been experimentally investigated by combining two types of measurements with an electron beam ion trap. The two different measurements are the differential x-ray measurement at 90∘ with respect to the electron beam and the integral resonance-strength measurement. The alignment parameter, which denotes the magnetic-sublevel population distribution, has been obtained for the autoionization states [1s2s22p1/2]1 formed in DR of Li-like Pr (atomic number Z=59) and Ho (Z=67). This work is a complement to our previous work [Phys. Rev. Lett. 108, 073002 (2012)] on Li-like Au (Z=79). The experimental results are in reasonable agreement with theoretical predictions, and a strong Z-dependent tendency due to the Breit interaction has been confirmed.

关键词： PHOTON emission ELECTRON-electron interactions ION recombination ATOMIC number QUANTUM electrodynamics ELECTRON beams AUGER effect ION traps

Machine-learning interatomic potential for molecular dynamics simulation of ferroelectric KNbO3 perovskite

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arXiv 2022年

作者： Thong, Hao-Cheng Wang, XiaoYang Wang, Han Zhang, Linfeng Wang, Ke Xu, Ben State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and Engineering Tsinghua University Beijing100084 China Graduate School China Academy of Engineering Physics Beijing100193 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Beijing Institute of Big Data Research Beijing100871 China

Ferroelectric perovskites have been ubiquitously applied in piezoelectric devices for decades, among which, eco-friendly lead-free (K,Na)NbO3-based materials have been recently demonstrated to be an excellent candidate for sustainable development. Molecular dynamics is a versatile theoretical calculation approach for the investigation of the dynamical properties of ferroelectric perovskites. However, molecular dynamics simulation of ferroelectric perovskites has been limited to simple systems, since the conventional construction of interatomic potential is rather difficult and inefficient. In the present study, we construct a machine-learning interatomic potential of KNbO3 (as a representative system of (K,Na)NbO3) by using a deep neural network model. Including first-principles calculation data into the training dataset ensures the quantum-mechanics accuracy of the interatomic potential. The molecular dynamics based on machine-learning interatomic potential shows good agreement with the first-principles calculations, which can accurately predict multiple fundamental properties, e.g., atomic force, energy, elastic properties, and phonon dispersion. In addition, the interatomic potential exhibits satisfactory performance in the simulation of domain wall and temperature-dependent phase transition. The construction of interatomic potential based on machine learning could potentially be transferred to other ferroelectric perovskites and consequently benefits the theoretical study of ferroelectrics. © 2022, CC BY.

关键词： Molecular dynamics