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

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Physical Review B 2023年第1期107卷 014101-014101页

作者： Hao-Cheng Thong XiaoYang Wang Jian Han Linfeng Zhang Bei Li Ke Wang Ben Xu State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and Engineering Tsinghua University Beijing 100084 People's Republic of China Graduate School China Academy of Engineering Physics Beijing 100193 People's Republic of China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing 100088 People's Republic of China Beijing Institute of Big Data Research Beijing 100871 People's Republic of China School of Materials Science and Engineering Research Center for Materials Genome Engineering Wuhan University of Technology Wuhan 430070 People's Republic of China

Ferroelectric perovskites have been ubiquitously applied in piezoelectric devices for decades, among which ecofriendly 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 data set 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 benefit the theoretical study of ferroelectrics.

关键词： Domain walls Elasticity Ferroelectricity First-principles calculations Phase transitions Phonons Piezoelectricity Machine learning Molecular dynamics

Comparative study on atomic ionization in bicircular laser fields by length and velocity gauges S-matrix theory

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Chinese physics B 2020年第2期29卷 254-261页

作者： Hong Xia Xin-Yan Jia Xiao-Lei Hao Li Guo Dai-He Fan Gen-Bai Chu Jing Chen School of Physical Science and Technology Southwest Jiaotong UniversityChengdu 610031China Institute of Theoretical Physics and Department of Physics Shanxi UniversityTaiyuan 030006China State Key Laboratory for Quantum Optics and Center for Cold Atom Physics Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Science and Technology on Plasma Physics Laboratory Laser Fusion Research CenterChina Academy of Engineering PhysicsMianyang 621900China HEDPS Center for Applied Physics and TechnologyPeking UniversityBeijing 100871China Institute of Applied Physics and Computational Mathematics Beijing 100088China

Ionization of atoms in counter-rotating and co-rotating bicircular laser fields is studied using the S-matrix theory in both length and velocity *** show that for both the bicircular fields,ionization rates are enhanced when the two circularly polarized lights have comparable *** addition,the curves of ionization rate versus the field amplitude ratio of the two colors for counter-rotating and co-rotating fields coincide with each other in the length gauge case at the total laser intensity 5×10^14 W/cm^2,which agrees with the experimental ***,the degree of the coincidence between the ionization rate curves of the two bicircular fields decreases with the increasing field amplitude ratio and decreasing total laser *** the help of the ADK theory,the above characteristics of the ionization rate curves can be well interpreted,which is related to the transition from the tunneling to multiphoton ionization mechanism.

关键词： above-threshold ionization(ATI) bicircular laser fields length gauge velocity gauge S-matrix theory

How Graph Neural Networks Learn: Lessons from Training Dynamics

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arXiv 2023年

作者： Yang, Chenxiao Wu, Qitian Wipf, David Sun, Ruoyu Yan, Junchi School of Artificial Intelligence Department of Computer Science and Engineering MoE Lab of AI Shanghai Jiao Tong University China Amazon Web Services United States School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data China

A long-standing goal in deep learning has been to characterize the learning behavior of black-box models in a more interpretable manner. For graph neural networks (GNNs), considerable advances have been made in formalizing what functions they can represent, but whether GNNs will learn desired functions during the optimization process remains less clear. To fill this gap, we study their training dynamics in function space. In particular, we find that the gradient descent optimization of GNNs implicitly leverages the graph structure to update the learned function, as can be quantified by a phenomenon which we call kernel-graph alignment. We provide theoretical explanations for the emergence of this phenomenon in the overparameterized regime and empirically validate it on real-world GNNs. This finding offers new interpretable insights into when and why the learned GNN functions generalize, highlighting their limitations in heterophilic graphs. Practically, we propose a parameter-free algorithm that directly uses a sparse matrix (i.e. graph adjacency) to update the learned function. We demonstrate that this embarrassingly simple approach can be as effective as GNNs while being orders-of-magnitude faster. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

Entanglement duality in spin-spin interactions

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Physical Review A 2022年第3期106卷 032407-032407页

作者： Vahid Azimi-Mousolou Anders Bergman Anna Delin Olle Eriksson Manuel Pereiro Danny Thonig Erik Sjöqvist Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics University of Isfahan Isfahan 81746-73441 Iran Department of Physics and Astronomy Uppsala University Box 516 SE-751 20 Uppsala Sweden Department of Applied Physics School of Engineering Sciences KTH Royal Institute of Technology AlbaNova University Center SE-10691 Stockholm Sweden Swedish e-Science Research Center (SeRC) KTH Royal Institute of Technology SE-10044 Stockholm Sweden School of Science and Technology Örebro University SE-701 82 Örebro Sweden

We examine entanglement of thermal states for spin-1/2 dimers in external magnetic fields. Entanglement transition in the temperature-magnetic-field plane demonstrates a duality in spin-spin interactions. This identifies a pair of dual categories of symmetric and antisymmetric dimers with each category classified into toric entanglement classes. The entanglement transition line is preserved from each toric entanglement class to its dual toric class. The toric classification is an indication of the topological signature of the entanglement, which bring about topological stability that could be relevant for quantum information processing.

关键词： Quantum correlations in quantum information Quantum entanglement Quantum information theory Spin dynamics

Object detection under the linear subspace model with application to cryo-EM images

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arXiv 2024年

作者： Eldar, Amitay Waknin, Keren Mor Davenport, Samuel Bendory, Tamir Schwartzman, Armin Shkolnisky, Yoel Department of Applied Mathematics Tel-Aviv University Israel Division of Biostatistics University of California San Diego United States School of Electrical Engineering Tel Aviv University Israel Halıcıoglu Data Science Institute Division of Biostatistics University of California San Diego United States

Detecting multiple unknown objects in noisy data is a key problem in many scientific fields, such as electron microscopy imaging. A common model for the unknown objects is the linear subspace model, which assumes that the objects can be expanded in some known basis (such as the Fourier basis). In this paper, we develop an object detection algorithm that under the linear subspace model is asymptotically guaranteed to detect all objects, while controlling the family wise error rate or the false discovery rate. Numerical simulations show that the algorithm also controls the error rate with high power in the non-asymptotic regime, even in highly challenging regimes. We apply the proposed algorithm to experimental electron microscopy data set, and show that it outperforms existing standard *** Codes 6008, 60G15, 60G10, 60G35, 62M20, 62M40, © 2024, CC BY-NC-ND.

关键词： Object detection

Kinetics of Rayleigh-Taylor instability in van der Waals fluid: the influence of compressibility

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arXiv 2024年

作者： Chen, Jie Xu, Aiguo Zhang, Yudong Chen, Dawei Chen, Zhihua National Key Laboratory of Transient Physics Nanjing University of Science and Technology Nanjing210094 China National Key Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics P. O. Box 8009-26 Beijing100088 China State Key Laboratory of Explosion Science and Safety Protection Beijing Institute of Technology Beijing100081 China HEDPS Center for Applied Physics and Technology College of Engineering Peking University Beijing100871 China National Key Laboratory of Shock Wave and Detonation Physics Mianyang621999 China School of Mechanics and Safety Engineering Zhengzhou University Zhengzhou450001 China

Early studies on Rayleigh-Taylor instability (RTI) primarily relied on the Navier-Stokes (NS) model. As research progresses, it becomes increasingly evident that the kinetic information that the NS model failed to capture is of great value for identifying and even controlling the RTI process;simultaneously, the lack of analysis techniques for complex physical fields results in a significant waste of data information. In addition, early RTI studies mainly focused on the incompressible case and the weakly compressible case. In the case of strong compressibility, the density of the fluid from the upper layer (originally heavy fluid) may become smaller than that of the surround- ing (originally light) fluid, thus invalidating the early method of distinguishing light and heavy fluids based on density. In this paper, tracer particles are incorporated into a single-fluid discrete Boltzmann method (DBM) model that considers the van der Waals potential. By using tracer par- ticles to label the matter-particle sources, a careful study of the matter-mixing and energy-mixing processes of the RTI evolution is realized in the single-fluid framework. The effects of compress- ibility on the evolution of RTI are examined mainly through the analysis of bubble and spike velocities, the ratio of area occupied by heavy fluid, and various entropy generation rates of the system. It is demonstrated that: (i) compressibility has a suppressive effect on the spike velocity, and this suppressive impact diminishes as the Atwood number (At) increases. The influence of compressibility on bubble velocity shows a staged behavior with increasing At. (ii) The impact of compressibility on the entropy production rate associated with the heat flow ( SNOEF ) is related to the stages of RTI evolution. Moreover, this staged impact of compressibility on SNOEF varies with At. Compressibility exhibits an inhibitory effect on the entropy production rate associated with viscous stresses ( SNOMF). (iii) By incor

关键词： Compressibility

Neural Canonical Transformations for Quantum Anharmonic Solids of Lithium

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arXiv 2024年

作者： Zhang, Qi Wang, Xiaoyang Shi, Rong Ren, Xinguo Wang, Han Wang, Lei Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Anhui Hefei230026 China HEDPS CAPT College of Engineering Peking University Beijing100871 China

Lithium is a typical quantum solid, characterized by cubic structures at ambient pressure. As the pressure increases, it forms more complex structures and undergoes a metal-to-semiconductor transformation, complicating theoretical and experimental analyses. We employ the neural canonical transformation approach, an ab initio variational method based on probabilistic generative models, to investigate the quantum anharmonic effects in lithium solids at finite temperatures. This approach combines a normalizing flow for phonon excited-state wave functions with a probabilistic model for the occupation of energy levels, optimized jointly to minimize the free energy. Our results indicate that quantum anharmonicity lowers the bcc-fcc transition temperature compared to classical molecular dynamics predictions. At high pressures, the predicted fractional coordinates of lithium atoms in the cI16 structure show good quantitative agreement with experimental observations. Finally, contrary to previous beliefs, we find that the poor metallic oC88 structure is stabilized by the potential energy surface obtained via high-accuracy electronic structure calculations, rather than thermal or quantum nuclear effects. Copyright © 2024, The Authors. All rights reserved.

关键词： Excited states

Insights into dendritic growth mechanisms in batteries: A combined machine learning and computational study

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arXiv 2025年

作者： Zhao, Zirui Xia, Junchao Wu, Si Wang, Xiaoke Xu, Guanping Zhu, Yinghao Sun, Jing Li, Hai-Feng Institute of Applied Physics and Materials Engineering University of Macau Avenida da Universidade Taipa999078 China School of Materials Science and Engineering Chang’an University Xi’an710061 China School of Physical Science and Technology Ningbo University Ningbo315211 China Faculty of Data Science City University of Macau Avenida Padre Tomas Pereira Taipa999078 China

In recent years, researchers have increasingly sought batteries as an efficient and cost-effective solution for energy storage and supply, owing to their high energy density, low cost, and environmental resilience. However, the issue of dendrite growth has emerged as a significant obstacle in battery development. Excessive dendrite growth during charging and discharging processes can lead to battery short-circuiting, degradation of electrochemical performance, reduced cycle life, and abnormal exothermic events. Consequently, understanding the dendrite growth process has become a key challenge for researchers. In this study, we investigated dendrite growth mechanisms in batteries using a combined machine learning approach, specifically a two-dimensional artificial convolutional neural network (CNN) model, along with computational methods. We developed two distinct computer models to predict dendrite growth in batteries. The CNN-1 model employs standard convolutional neural network techniques for dendritic growth prediction, while CNN-2 integrates additional physical parameters to enhance model robustness. Our results demonstrate that CNN-2 significantly enhances prediction accuracy, offering deeper insights into the impact of physical factors on dendritic growth. This improved model effectively captures the dynamic nature of dendrite formation, exhibiting high accuracy and sensitivity. These findings contribute to the advancement of safer and more reliable energy storage systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

Thermoelastic properties and thermal evolution of the Martian core from ab initio calculated ferromagnetic Fe-S liquid

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arXiv 2023年

作者： Li, Wei-Jie Li, Zi Ma, Zhe Zhou, Jie Wang, Cong Zhang, Ping Intelligent Science & Technology Academy Limited of CASIC Beijing100141 China Institute of Applied Physics and Computational Mathematics Beijing100088 China Scientific Research Key Laboratory of Aerospace Defence Intelligent Systems and Technology Beijing100141 China Tianfu Innovation Energy Establishment Chengdu610213 China Center for Applied Physics and Technology Peking University Beijing100871 China

The accurate thermoelastic properties and thermal conductivity are crucial in understanding the thermal evolution of the Martian core. A fitting method based on the ab initio calculated pressure-volume-temperature data is proposed in the formulation of the equation of state with high accuracy, by which the pressure and temperature dependent thermoelastic properties can be directly calculated by definitions. The ab initio results show that the liquid Fe0.75S0.25 under Martian core condition is thoroughly in the ferromagnetic state, without existing spin crossover. The liquid Fe0.75S0.25 in magnetic calculation owns a low thermal conductivity (21~23 W/m/K) when compared with non-magnetic calculation at the same state. Based on the Insight estimated and ab initio calculated properties of the Martian core, the iron snow model is verified when the current temperature at the core-mantle boundary is below the core melting temperature, and the simply secular cooling model is verified on the contrary. © 2023, CC BY.

关键词： Thermal conductivity