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Benchmarking the effective one-component plasma model for warm dense neon and krypton within quantum molecular dynamics simulation

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Physical Review E 2020年第2期101卷 023302-023302页

作者： Zhao-Qi Wang Jun Tang Yong Hou Qi-Feng Chen Xiang-Rong Chen Jia-Yu Dai Xu-Jun Meng Yun-Jun Gu Lei Liu Guo-Jun Li Yang-Shun Lan Zhi-Guo Li College of Physics Sichuan University Chengdu 610065 People's Republic of China National Key Laboratory for Shock Wave and Detonation Physics Research Institute of Fluid Physics Chinese Academy of Engineering Physics Mianyang 621900 People's Republic of China Science and Technology on Surface Physics and Chemistry Laboratory Mianyang 621908 People's Republic of China Department of Physics National University of Defense Technology Changsha 410073 People's Republic of China Institute of Applied Physics and Computational Mathematics Beijing 100094 People's Republic of China

The effective one-component plasma (EOCP) model has provided an efficient approach to obtaining many important thermophysical parameters of hot dense matter [J. Clérouin, et al., Phys. Rev. Lett. 116, 115003 (2016)]. In this paper, we perform extensive quantum molecular dynamics (QMD) simulations to determine the equations of state, ionic structures, and ionic transport properties of neon and krypton within the warm dense matter (WDM) regime where the density ( ρ ) is up to 12 g/cm3 and the temperature (T) is up to 100 kK. The simulated data are then used as a benchmark to explicitly evaluate the EOCP and Yukawa models. It is found that, within present ρ-T regime, the EOCP model can excellently reproduce the diffusion and viscosity coefficients of neon and krypton due to the fact that this model defines a system which nearly reproduces the actual physical states of WDM. Therefore, the EOCP model may be a promising alternative approach to reasonably predicting the transport behaviors of matter in WDM regime at lower QMD computational cost. The evaluation of Yukawa model shows that the consideration of the energy level broadening effect in the average atom model is necessary. Finally, with the help of EOCP model, the Stokes-Einstein relationships about neon and krypton are discussed, and fruitful plasma parameters as well as a practical ρ-T-dependent formula of the effective coupling parameter are obtained. These results not only provide valuable information for future theoretical and experimental studies on dense neon and krypton but also reveal the applicability of the EOCP model and the limitation of the Yukawa model in WDM regime and further support the continuing search for a unified description of ionic transport in dense plasma.

关键词： Equations of state Ionic transport Structural properties Warm-dense matter First-principles calculations Molecular dynamics

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CellBiAge: Improved single-cell age classification using data binarization

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Cell Reports 2023年第12期42卷 113500页

作者： Yu, Doudou Li, Manlin Linghu, Guanjie Hu, Yihuan Hajdarovic, Kaitlyn H. Wang, An Singh, Ritambhara Webb, Ashley E. Molecular Biology Cell Biology and Biochemistry Graduate Program Brown University Providence 02912 RI United States Data Science Institute Brown University Providence 02912 RI United States Neuroscience Graduate Program Brown University Providence 02912 RI United States Department of Applied Mathematics & Statistics Johns Hopkins University Baltimore 21218 MD United States Department of Computer Science Brown University Providence 02912 RI United States Center for Computational Molecular Biology Brown University Providence 02912 RI United States Department of Molecular Biology Cell Biology and Biochemistry Brown University Providence 02912 RI United States Center on the Biology of Aging Brown University Providence 02912 RI United States Carney Institute for Brain Science Brown University Providence 02912 RI United States Center for Translational Neuroscience Brown University Providence 02912 RI United States

Aging is a major risk factor for many diseases. Accurate methods for predicting age in specific cell types are essential to understand the heterogeneity of aging and to assess rejuvenation strategies. However, classifying organismal age at single-cell resolution using transcriptomics is challenging due to sparsity and noise. Here, we developed CellBiAge, a robust and easy-to-implement machine learning pipeline, to classify the age of single cells in the mouse brain using single-cell transcriptomics. We show that binarization of gene expression values for the top highly variable genes significantly improved test performance across different models, techniques, sexes, and brain regions, with potential age-related genes identified for model prediction. Additionally, we demonstrate CellBiAge's ability to capture exercise-induced rejuvenation in neural stem cells. This study provides a broadly applicable approach for robust classification of organismal age of single cells in the mouse brain, which may aid in understanding the aging process and evaluating rejuvenation methods. © 2023 The Authors

关键词： aging brain CP: Cell biology hypothalamus machine learning single-cell RNA-seq

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High-Temperature Quantum Anomalous Hall Insulators in Lithium-Decorated Iron-Based Superconductor Materials

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Physical Review Letters 2020年第8期125卷 086401-086401页

作者： Yang Li Jiaheng Li Meng Ye Fawei Zheng Zetao Zhang Jingheng Fu Wenhui Duan Yong Xu State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 China Frontier Science Center for Quantum Information Beijing 100084 China Collaborative Innovation Center of Quantum Matter Beijing 100084 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing 100088 China Institute for Advanced Study Tsinghua University Beijing 100084 China Beijing Academy of Quantum Information Sciences Beijing 100193 China RIKEN Center for Emergent Matter Science (CEMS) Wako Saitama 351-0198 Japan

Quantum anomalous Hall (QAH) insulator is the key material to study emergent topological quantum effects, but its ultralow working temperature limits experiments. Here, by first-principles calculations, we find a family of stable two-dimensional (2D) structures generated by lithium decoration of layered iron-based superconductor materials Fe X(X=S,Se,Te), and predict room-temperature ferromagnetic semiconductors together with large-gap high-Chern-number QAH insulators in the 2D materials. The extremely robust ferromagnetic order is induced by the electron injection from Li to Fe and stabilized by strong ferromagnetic kinetic exchange in the 2D Fe layer. While in the absence of spin-orbit coupling (SOC), the ferromagnetism polarizes the system into a half Dirac semimetal state protected by mirror symmetry, the SOC effect results in a spontaneous breaking of mirror symmetry and introduces a Dirac mass term, which creates QAH states with sizable gaps (several tens of meV) and multiple chiral edge modes. We also find a 3D QAH insulator phase featured by a macroscopic number of chiral conduction channels in bulk LiOH−LiFe X. The findings open new opportunities to realize novel QAH physics and applications at high temperatures.

关键词： First-principles calculations Quantum anomalous Hall effect Topological materials Iron-based superconductors

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Constraining unmodeled physics with compact binary mergers from GWTC-1

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Physical Review D 2021年第4期103卷 042004-042004页

作者： Bruce Edelman F. J. Rivera-Paleo J. D. Merritt Ben Farr Zoheyr Doctor Jeandrew Brink Will M. Farr Jonathan Gair Joey Shapiro Key Jess McIver Alex B. Nielsen Department of Physics University of Oregon Eugene Oregon 97403 USA Departament d’Astronomia i Astrofsica Universitat de Valncia 46100 Burjassot Spain Department of Mathematics and Applied Mathematics University of the Free State PO Box 339 Bloemfontein 9300 South Africa Department of Physics and Astronomy Stony Brook University Stony Brook New York 11794 USA Center for Computational Astrophysics Flatiron Institute New York New York 10010 USA Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Am Mühlenberg 1 D-14476 Potsdam-Golm Germany University of Washington Bothell 18115 Campus Way NE Bothell Washington 98011 USA University of British Columbia Vancouver British Columbia V6T 1Z4 Canada Max-Planck-Institut für Gravitationsphysik (Albert-Einstein-Institut) D-30167 Hannover Germany Department of Mathematics and Physics University of Stavanger 4036 Stavanger Norway

We present a flexible model to describe the effects of generic deviations of observed gravitational-wave signals from modeled waveforms in the LIGO and Virgo gravitational-wave detectors. With the detection of 11 gravitational-wave events from the GWTC-1 catalog, we are able to constrain possible deviations from our modeled waveforms. In this paper, we present our coherent spline model that describes the deviations, then choose to validate our model on an example phenomenological and astrophysically motivated departure in waveforms based on extreme spontaneous scalarization. We find that the model is capable of recovering the simulated deviations. By performing model comparisons, we observe that the spline model effectively describes the simulated departures better than a normal compact binary coalescence (CBC) model. We analyze the entire GWTC-1 catalog of events with our model and compare it to a normal CBC model, finding that there are no significant departures from the modeled template gravitational waveforms used.

关键词： Gravitational wave sources Gravitational waves

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Active learning of uniformly accurate interatomic potentials for materials simulation

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Physical Review Materials 2019年第2期3卷 023804-023804页

作者： Linfeng Zhang De-Ye Lin Han Wang Roberto Car Weinan E Department of Mathematics and Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA and Beijing Institute of Big Data Research Beijing 100871 People's Republic of China

An active learning procedure called deep potential generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular modeling of materials. This procedure consists of three main components: exploration, generation of accurate reference data, and training. Application to the sample systems of Al, Mg, and Al-Mg alloys demonstrates that DP-GEN can produce uniformly accurate PES models with a minimal number of reference data.

关键词： Atomic & molecular structure Potential energy surfaces Alloys Amorphous materials Liquids Density functional theory First-principles calculations Molecular dynamics

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Adaptive Grids in the Context of Algebraic Stabilizations for Convection-Diffusion-Reaction Equations

arXiv

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arXiv 2020年

作者： Jha, Abhinav John, Volker Knobloch, Petr RWTH Aachen University Applied and Computational Mathematics Schinkelstraße 2 Aachen52062 Germany Mohrenstr. 39 Berlin10117 Germany Freie Universität Berlin Department of Mathematics and Computer Science Arnimallee 6 Berlin14195 Germany Department of Numerical Mathematics Faculty of Mathematics and Physics Charles University Sokolovská 83 Praha 8 18675 Czech Republic

MSC Codes 65N12, 65N30Three algebraically stabilized finite element schemes for discretizing convection-diffusion-reaction equations are studied on adaptively refined grids. These schemes are the algebraic flux correction (AFC) scheme with Kuzmin limiter, the AFC scheme with BJK limiter, and the recently proposed Monotone Upwind-type Algebraically Stabilized (MUAS) method. Both, conforming closure of the refined grids and grids with hanging vertices are considered. A non-standard algorithmic step becomes necessary before these schemes can be applied on grids with hanging vertices. The assessment of the schemes is performed with respect to the satisfaction of the global discrete maximum principle (DMP), the accuracy, e.g., smearing of layers, and the efficiency in solving the corresponding nonlinear problems. Copyright © 2020, The Authors. All rights reserved.

关键词： Algebra

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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The graph limit of the minimizer of the onsager-machlup functional and its computation

arXiv

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arXiv 2020年

作者： Du, Qiang Li, Tiejun Li, Xiaoguang Ren, Weiqing Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States LMAM and School of Mathematical Sciences Peking University Beijing100871 China Beijing Computational Science Research Center Beijing100193 China MOE-LCSM School of Mathematics and Statistics Hunan Normal University Changsha Hunan410081 China Department of Mathematics National University of Singapore Singapore119076 Singapore

The Onsager-Machlup (OM) functional is well-known for characterizing the most probable transition path of a diffusion process with non-vanishing noise. However, it suffers from a notorious issue that the functional is unbounded below when the specified transition time T goes to infinity. This hinders the interpretation of the results obtained by minimizing the OM functional. We provide a new perspective on this issue. Under mild conditions, we show that although the infimum of the OM functional becomes unbounded when T goes to infinity, the sequence of minimizers does contain convergent subsequences on the space of curves. The graph limit of this minimizing subsequence is an extremal of the abbreviated action functional, which is related to the OM functional via the Maupertuis principle with an optimal energy. We further propose an energy-climbing geometric minimization algorithm (EGMA) which identifies the optimal energy and the graph limit of the transition path simultaneously. This algorithm is successfully applied to several typical examples in rare event studies. Some interesting comparisons with the Freidlin-Wentzell action functional are also made. Copyright © 2020, The Authors. All rights reserved.

关键词： Optimization

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Quality of internal representation shapes learning performance in feedback neural networks

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Physical Review Research 2021年第1期3卷 013176-013176页

作者： Lee Susman Francesca Mastrogiuseppe Naama Brenner Omri Barak Interdisciplinary Program in Applied Mathematics Technion Israel Institute of Technology Haifa 32000 Israel Network Biology Research Laboratories Technion Israel Institute of Technology Haifa 32000 Israel Gatsby Computational Neuroscience Unit University College London London W1T 4JG United Kingdom Department of Chemical Engineering Technion Israel Institute of Technology Haifa 32000 Israel Rappaport Faculty of Medicine Technion Israel Institute of Technology Haifa 32000 Israel

A fundamental feature of complex biological systems is the ability to form feedback interactions with their environment. A prominent model for studying such interactions is reservoir computing, where learning acts on low-dimensional bottlenecks. Despite the simplicity of this learning scheme, the factors contributing to or hindering the success of training in reservoir networks are in general not well understood. In this work, we study nonlinear feedback networks trained to generate a sinusoidal signal, and analyze how learning performance is shaped by the interplay between internal network dynamics and target properties. By performing exact mathematical analysis of linearized networks, we predict that learning performance is maximized when the target is characterized by an optimal, intermediate frequency which monotonically decreases with the strength of the internal reservoir connectivity. At the optimal frequency, the reservoir representation of the target signal is high-dimensional, desynchronized, and thus maximally robust to noise. We show that our predictions successfully capture the qualitative behavior of performance in nonlinear networks. Moreover, we find that the relationship between internal representations and performance can be further exploited in trained nonlinear networks to explain behaviors which do not have a linear counterpart. Our results indicate that a major determinant of learning success is the quality of the internal representation of the target, which in turn is shaped by an interplay between parameters controlling the internal network and those defining the task.

关键词： Learning theory Artificial neural networks Dynamical systems Neuronal network models

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Multipartite Entanglement in Rabi Driven Superconducting Qubits

arXiv

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arXiv 2022年

作者： Lu, Marie Ville, Jean-Loup Cohen, Joachim Petrescu, Alexandru Schreppler, Sydney Chen, Larry Jüenger, Christian Pelletti, Chiara Marchenkov, Alexei Banerjee, Archan Livingston, William P. Kreikebaum, John Mark Santiago, David I. Blais, Alexandre Siddiqi, Irfan Department of Physics University of California BerkeleyCA94720 United States Institut Quantique Département de Physique Université de Sherbrooke SherbrookeQCJ1K 2R1 Canada Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory University of California BerkeleyCA94720 United States Bleximo Corp. 701 Heinz Ave BerkeleyCA94710 United States Canadian Institute for Advanced Research TorontoONM5G1M1 Canada

Exploring highly connected networks of qubits is invaluable for implementing various quantum algorithms and simulations as it allows for entangling qubits with reduced circuit depth. Here, we demonstrate a multi-qubit STAR (Sideband Tone Assisted Rabi driven) gate. Our scheme is inspired by the ion qubit Mølmer-Sørensen gate and is mediated by a shared photonic mode and Rabi-driven superconducting qubits, which relaxes restrictions on qubit frequencies during fabrication and supports scalability. We achieve a two-qubit gate with maximum state fidelity of 95% in 310 ns, a three-qubit gate with state fidelity 90.5% in 217 ns, and a four-qubit gate with state fidelity 66% in 200 ns. Furthermore, we develop a model of the gate that show the four-qubit gate is limited by shared resonator losses and the spread of qubit-resonator couplings, which must be addressed to reach high-fidelity operations. Copyright © 2022, The Authors. All rights reserved.

关键词： Qubits

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