Showing 1–50 of 121 results for author: Song, C

Centimeter-scale fully suspended metal and metal oxide thin films by one-step transfer-free liquid metal capillary forming

Authors: Chunlei Song, Zhenqi Guo, Yuanting Su, Changren Tian, Yeqi Zhu, Liang Lei, Jianbo Tang

Abstract: Fully suspended thin films can decouple substrate effects and provide additional tuning degrees of freedom compared with their substrate-supported counterparts, making them unique platforms for next-generation thin film devices. Here we report one-step, transfer-free and substrate-free fabrication of centimeter-scale ultrathin fully suspended metal and metal oxide film structures via liquid metal… ▽ More Fully suspended thin films can decouple substrate effects and provide additional tuning degrees of freedom compared with their substrate-supported counterparts, making them unique platforms for next-generation thin film devices. Here we report one-step, transfer-free and substrate-free fabrication of centimeter-scale ultrathin fully suspended metal and metal oxide film structures via liquid metal capillary forming. We show that, analogous to soap film formation, the instantaneously developed few-nanometer-thick native surface oxide can laminate various liquid metals into micrometer-thick metallic films. Surprisingly, the surfactant-like metal oxide bilayer can survive dewetting-induced liquid metal drainage, forming suspended two-dimensional films featuring an enormous lateral size-to-thickness ratio on the order of 10^7. We further demonstrate rapid prototyping of metallic minimal-surface thin-walled structures and ultra-sensitive acoustic wave detection with these suspended thin film platforms. △ Less

Submitted 31 July, 2026; originally announced August 2026.

Tensor Train Decomposition-based 3D Implicit Full Waveform Inversion with Multi-scale Structural Similarity

Authors: Liangsheng He, Chao Song, Tiansheng Chen, Tao Liu, Cai Liu

Abstract: Three-dimensional full waveform inversion (3DFWI) is a powerful technique for reconstructing high-resolution subsurface velocity models. However, its application is often limited by high memory requirements, computational costs, and sensitivity to cycle skipping. To overcome these challenges, we propose a novel tensor train (TT) decomposition-based 3D implicit full waveform inversion framework (TT… ▽ More Three-dimensional full waveform inversion (3DFWI) is a powerful technique for reconstructing high-resolution subsurface velocity models. However, its application is often limited by high memory requirements, computational costs, and sensitivity to cycle skipping. To overcome these challenges, we propose a novel tensor train (TT) decomposition-based 3D implicit full waveform inversion framework (TT-3DIFWI) combined with a multi-scale structural similarity (M-SSIM) objective function. In this framework, the 3D velocity model is represented by TT decomposition as a product of a series of low-rank core tensors. Then, three axis-specific implicit neural network representations (INR) based on one-dimensional vector coordinates as input are constructed to predict these core tensors, rather than directly predicting the velocity model. This INR reparameterization method based on TT decomposition can significantly reduce the memory consumption of INR training while maintaining the accuracy and resolution of the 3D velocity model reconstruction. Meanwhile, the low-rank structure of TT decomposition also ensures the structural consistency of the reconstruction velocity, thereby improving the accuracy and continuity of the inversion result. Furthermore, the M-SSIM objective function can compare the multi-scale structural differences between predicted and observed data, and utilize the ultra-low frequency features to reduce cycle skipping. Numerical experiments on synthetic and challenging land datasets demonstrate that TT-3DIFWI with M-SSIM achieves accurate and continuous velocity reconstruction, even with poor initial models or missing low-frequency data. △ Less

Submitted 22 June, 2026; originally announced June 2026.

Neural Operator-Based Surrogate Model for CFD:Helical Coil Steam Generator in Small Modular Reactor

Authors: Minseo Lee, Seongmin Oh, Chaehyeon Song, Bumjin Cho, Shilaj Baral, Sangam Khanal, Minseop Song, Joongoo Jeon

Abstract: Real-time thermal-hydraulic simulation is essential for digital twin (DT) technology that supports the safe and efficient operation of small modular reactors (SMRs). Computational fluid dynamics (CFD) provides high-fidelity flow analysis, but its computational cost prevents direct use in DT applications. AI-based surrogate modeling has been actively investigated to address this limitation, yet neu… ▽ More Real-time thermal-hydraulic simulation is essential for digital twin (DT) technology that supports the safe and efficient operation of small modular reactors (SMRs). Computational fluid dynamics (CFD) provides high-fidelity flow analysis, but its computational cost prevents direct use in DT applications. AI-based surrogate modeling has been actively investigated to address this limitation, yet neural operator--based surrogates for CFD-level transient analysis of SMR-specific geometries have not been reported. This study presents an integrated framework that combines a reduced-order model (ROM) with neural operators, applied to the helical coil steam generator (HCSG) of the System-integrated Modular Advanced Reactor (SMART). Two ROM strategies tailored to each CFD data type were compared, an MLP-based autoencoder (AE) for unstructured mesh data and a convolutional autoencoder (CAE) for structured mesh data, and each was coupled with the deep operator network (DeepONet) to construct the latent DeepONet (L-DeepONet). The Fourier neural operator (FNO) was additionally adopted for comparison. A multi-scale technique was incorporated into both frameworks to mitigate spectral bias and improve the prediction of Kármán vortex streets developing inside the HCSG. The multi-scale L-DeepONet captured the instantaneous periodic vortex dynamics in both velocity and pressure fields, while the FNO and its multi-scale variant predicted the time-averaged mean flow and provided reliable pressure drop estimates. These complementary characteristics provide a practical model-selection guideline that links each architecture to specific DT objectives based on CFD data type and the required level of flow resolution. △ Less

Submitted 28 May, 2026; originally announced May 2026.

Extreme Energy Concentration of Band-Limited Superoscillatory Vortices for Efficient Optical Micromanipulation

Authors: Chengda Song, Jing He, Xi Xie, Qian Wang, Yijie Shen, Fangwen Sun, Guanghui Yuan

Abstract: The Abbe diffraction limit, tied to the fundamental spatial bandwidth constraint imposed by any physical aperture, remains the primary barrier to achieving ultimate far-field optical resolution and precise light-matter interactions. However, current efforts to engineer structured light fields beyond this limit often come at the cost of massive sacrifices in energy efficiency. In this work, we math… ▽ More The Abbe diffraction limit, tied to the fundamental spatial bandwidth constraint imposed by any physical aperture, remains the primary barrier to achieving ultimate far-field optical resolution and precise light-matter interactions. However, current efforts to engineer structured light fields beyond this limit often come at the cost of massive sacrifices in energy efficiency. In this work, we mathematically complete the family of non-zero azimuthal-order Circular Prolate Spheroidal Wave Functions (CPSWFs), introducing them as a complete class of band-limited superoscillatory optical vortices carrying helical phase. Compared with classical Laguerre-Gaussian (LG) beams, we rigorously prove that these eigenmodes achieve the theoretical upper bound for extreme energy concentration under strict band-limited constraints. At the scale of light-matter interactions, this optimal concentration directly amplifies the intensity gradients and angular momentum densities that govern optical forces. This advantage translates directly into a 29.9% reduction in the trapping power threshold and a 2.3-fold increase in the subdiffraction orbital rotation speed of nanoparticles. Looking forward, this fundamental physical framework not only establishes strict mathematical boundaries for structured light fields but also serves as an absolute theoretical benchmark for deep-learning inverse design, and next-generation extreme optical micro-manipulation systems. △ Less

Submitted 26 May, 2026; originally announced May 2026.

Comments: 22 pages, 5 figures in the main text

The critical role of negative-energy states in the Landé $g$-factor of lithium-like ions

Authors: Chang-Xian Song, Yong-Bo Tang

Abstract: We report relativistic many-body calculations of the interelectronic-interaction correction to the Landé $g$-factor of the $2s_{1/2}$, $2p_{1/2}$, $2p_{3/2}$, and $3s_{1/2}$ states in lithium-like ions with nuclear charge $Z = 4-20$. Starting from the Dirac-Coulomb-Breit Hamiltonian, we treat positive-energy contributions using the coupled-cluster method with single and double excitations and incl… ▽ More We report relativistic many-body calculations of the interelectronic-interaction correction to the Landé $g$-factor of the $2s_{1/2}$, $2p_{1/2}$, $2p_{3/2}$, and $3s_{1/2}$ states in lithium-like ions with nuclear charge $Z = 4-20$. Starting from the Dirac-Coulomb-Breit Hamiltonian, we treat positive-energy contributions using the coupled-cluster method with single and double excitations and include negative-energy contributions through third-order perturbation theory. We observe that negative-energy states give a state-dependent correction whose magnitude and sign vary with both Z and the state; for $2p_{1/2}$, the correction from the negative-energy states reaches 30\% of the total interelectronic-interaction contribution at $Z = 20$. Agreement with previous high-precision calculations is better than $0.1\%$, confirming the reliability of the present approach. This work may serve as a valuable reference for future precise calculations of $g$-factors for many-electron atomic systems. △ Less

Submitted 28 April, 2026; originally announced April 2026.

Comments: 11 pages, 1 figures

RG-Based Local Hopf Reduction and Slow-Manifold Reconstruction for Nonlinear Aeroelastic Systems

Authors: Gelin Chen, Chen Song, Chao Yang

Abstract: Self-excited limit-cycle oscillations (LCOs) from Hopf bifurcations are a key feature of nonlinear aeroelasticity and depend sensitively on structural and aerodynamic parameters. Classical center-manifold and normal-form theory describe this local behavior, but can be cumbersome to apply in large discretized models and standard reduced-order modeling (ROM) workflows. A renormalization-group (RG)-b… ▽ More Self-excited limit-cycle oscillations (LCOs) from Hopf bifurcations are a key feature of nonlinear aeroelasticity and depend sensitively on structural and aerodynamic parameters. Classical center-manifold and normal-form theory describe this local behavior, but can be cumbersome to apply in large discretized models and standard reduced-order modeling (ROM) workflows. A renormalization-group (RG)-based reduction is developed that directly yields a Hopf-type amplitude equation on a local invariant manifold, specialized for polynomial nonlinearities in tensor-based discretizations and compatible with finite-element-type settings. The method provides explicit coefficients governing the Hopf threshold, criticality, and leading LCO amplitude/frequency trends, and admits a companion slow-manifold approximation with selected stable modes retained as static coordinates. Representative nonlinear-aeroelastic examples illustrate how the proposed framework supplies compact, parameter-aware Hopf/LCO descriptors suitable for local ROM construction near flutter. △ Less

Submitted 22 April, 2026; originally announced April 2026.

Comments: 82 pages, 8 figures, 5 tables. Includes appendices on computational RG reduction, Hopf persistence, coefficient correspondence, and model definition

MSC Class: 37G15; 37M99; 70K50; 74H45; 93A30

Optofluidic light routing via analytically configuring streamlines of micro-flow

Authors: R. Yan, Y. Yang, X. Tu, T. Huang, Y. Liu, C. Song

Abstract: Transformation optics (TO) is a new method to design metamaterials that can manipulate electromagnetic fields. Inspired by the traditional TO techniques which is mostly based on the solid metamaterials with a limited range of tunability, a novel streamline tracing-based transformation optofluidics (STTOF) method is proposed to manipulate the light path by analytically designating the light-carryin… ▽ More Transformation optics (TO) is a new method to design metamaterials that can manipulate electromagnetic fields. Inspired by the traditional TO techniques which is mostly based on the solid metamaterials with a limited range of tunability, a novel streamline tracing-based transformation optofluidics (STTOF) method is proposed to manipulate the light path by analytically designating the light-carrying streamlines of the flow in a two-dimensional circular bounded domain. A dipole flow model is built to analytically calculate the streamlines of the flow field inside the domain which allocates the optical/fluidic source and sink pairs at arbitrary positions. Liquid core/liquid cladding (L2) configuration is used in the experiment to trace the light via a specific streamline. Experimental results verify that the light paths agree well with the theoretical predictions, and demonstrate that a good range of tunability can be achieved by adjusting the flow rates and the source-sink positions of optical/fluidic source and sink pairs. △ Less

Submitted 21 February, 2026; v1 submitted 17 February, 2026; originally announced February 2026.

Comments: 14 pages, 7 figs

Journal ref: Microfluid Nanofluid 23, 101 (2019)

Electric field switching of altermagnetic spin-splitting in multiferroic skyrmions

Authors: Gui Wang, Yuhang Li, Bin Li, Xianzhe Chen, Jianting Dong, Weizhao Chen, Xiaobing Chen, Naifu Zheng, Maosen Guo, Aomei Tong, Hua Bai, Hongrui Zhang, Yifan Gao, Kaiwen Shen, Jiangyuan Zhu, Jiahao Han, Yingfen Wei, Hao Jiang, Xumeng Zhang, Ming Wang, Kebiao Xu, Wu Shi, Pengfei Wang, Jia Zhang, Qihang Liu , et al. (4 additional authors not shown)

Abstract: Magnetic skyrmions are localized magnetic structures that retain their shape and stability over time, thanks to their topological nature. Recent theoretical and experimental progress has laid the groundwork for understanding magnetic skyrmions characterized by negligible net magnetization and ultrafast dynamics. Notably, skyrmions emerging in materials with altermagnetism, a novel magnetic phase f… ▽ More Magnetic skyrmions are localized magnetic structures that retain their shape and stability over time, thanks to their topological nature. Recent theoretical and experimental progress has laid the groundwork for understanding magnetic skyrmions characterized by negligible net magnetization and ultrafast dynamics. Notably, skyrmions emerging in materials with altermagnetism, a novel magnetic phase featuring lifted Kramers degeneracy-have remained unreported until now. In this study, we demonstrate that BiFeO3, a multiferroic renowned for its strong coupling between ferroelectricity and magnetism, can transit from a spin cycloid to a Neel-type skyrmion under antidamping spin-orbit torque at room temperature. Strikingly, the altermagnetic spin splitting within BiFeO3 skyrmion can be reversed through the application of an electric field, revealed via the Circular photogalvanic effect. This quasiparticle, which possesses a neutral topological charge, holds substantial promise for diverse applications-most notably, enabling the development of unconventional computing systems with low power consumption and magnetoelectric controllability. △ Less

Submitted 10 January, 2026; originally announced January 2026.

Theoretical study on excited states of ICl+ molecular ion considering spin-orbit coupling

Authors: Rui Li, Ronglong Dou, Ting Gao, Qinan Li, Chaoqun Song

Abstract: The electronic structure of the ICl+ molecular ion is investigated by using high-level multireference configuration interaction (MRCI) method. To improve computational accuracy, Davidson corrections, spin-orbit coupling (SOC), and core-valence electron correlations effects are incorporated into the calculations. The potential energy curves (PECs) of 21 electronic states associated with the two low… ▽ More The electronic structure of the ICl+ molecular ion is investigated by using high-level multireference configuration interaction (MRCI) method. To improve computational accuracy, Davidson corrections, spin-orbit coupling (SOC), and core-valence electron correlations effects are incorporated into the calculations. The potential energy curves (PECs) of 21 electronic states associated with the two lowest dissociation limits are obtained. The dipole moments (DMs) of the 21 electronic states of ICl+ are systematically studied, and the variations of DMs of the identical symmetry state in the avoided crossing regions are elucidated by analyzing the dominant electronic configuration. With the help of the calculated SOC matrix element, the interaction between crossing states can be elucidated. Spin-orbit coupling matrix elements involving the several states are calculated. By analyzing potential energy curves of these states and the nearby electronic states, the possible predissociation channels are provided. Based on the computed PECs, the spectroscopic constants of bound states are determined. The comparison of the spectroscopic constants including and excluding SOC effect indicates that the SOC effect has an obvious correction to the spectroscopic properties of low-lying states. Finally, the transition properties between excited states and the ground state are studied. Based on the computed transition dipole moments and Franck-Condon Factors, radiative lifetimes for the low-lying vibrational levels of excited states are evaluated. △ Less

Submitted 24 November, 2025; originally announced November 2025.

Comments: 28 pages, 7 figures

Journal ref: Acta Phys. Sin., 2025, 74(12): 123102

Wafer-scale integration of single nanodiamonds via electrostatic-trapping

Authors: Jixiang Jing, Yicheng Wang, Zhuoran Wang, Yumeng Luo, Linjie Ma, Tongtong Zhang, Chunlin Song, Jiangyu Li, Kwai Hei Li, Dong-Keun Ki, Ji Tae Kim, Zhiqin Chu

Abstract: Nanodiamonds (NDs) are key materials for building nanoscale quantum sensing, imaging and communication devices. Scalable configuration of single NDs on heterogeneous platforms, forming photonic quantum source arrays, will be an essential solution towards realizing next-generation practical and industrial quantum devices. However, NDs are challenging to manipulate because their size, shape and surf… ▽ More Nanodiamonds (NDs) are key materials for building nanoscale quantum sensing, imaging and communication devices. Scalable configuration of single NDs on heterogeneous platforms, forming photonic quantum source arrays, will be an essential solution towards realizing next-generation practical and industrial quantum devices. However, NDs are challenging to manipulate because their size, shape and surface chemistry vary substantially. Here, we show a simple method based on electrostatic-trapping to rapidly and reliably pattern single ND arrays on arbitrary substrates at scale. Our method, which uses carefully engineered microscale hole templates and electrostatic force, captures single NDs across 8-inch wafers with 82.5% yields within 5 min. Systematic experimental and theoretical studies show the number of deposited NDs primarily depends on the diameter of the hole trap. The method is compatible with mature CMOS technologies, enabling the mass production of scalable and integrable quantum devices. This advancement is expected to accelerate the commercialization and industrial adoption of ND-based technologies. △ Less

Submitted 26 September, 2025; originally announced September 2025.

Subdiffraction confinement and non-diffractive propagation of optical Stokes skyrmions enabled by a super-oscillatory metalens

Authors: Jing He, Chengda Song, Wei Li, Fangwen Sun, Guanghui Yuan

Abstract: Optical Stokes skyrmions have garnered extensive interest due to their intrinsic topological robustness and potential in informatics.However, most research remains confined to paraxial, low-numerical-aperture (low-NA) regimes, where their large transverse dimensions restrict broader applications.Under high-NA focusing, the polarization texture typically degrades or transforms abruptly as the beam… ▽ More Optical Stokes skyrmions have garnered extensive interest due to their intrinsic topological robustness and potential in informatics.However, most research remains confined to paraxial, low-numerical-aperture (low-NA) regimes, where their large transverse dimensions restrict broader applications.Under high-NA focusing, the polarization texture typically degrades or transforms abruptly as the beam traverses the focal region, hindering topology-preserving transport.In this work, we propose a strategy to generate a skyrmion needle field that maintains both subdiffraction confinement and non-diffractive propagation under high-NA conditions, thus preserving their topological characteristsics. Leveraging the polarization invariance of conventional optical needles, we realize the Stokes skyrmion needle using a single plasmonic metalens,designed to function as both a polarization filter and a super-resolving focusing element.Experimental and simulation results verify non-diffractive propagation over an extended depth of focus (up to 5 lambda), while the Stokes-vector texture retained at subdiffraction scales throughout propagation. This skyrmion needle not only addresses previous propagation constraints but also opens new avenues for diffraction-unlimited information transport. Such skyrmion needles exhibit substantial potential in fields including light-matter interaction, optical metrology, and informatics. △ Less

Submitted 24 September, 2025; originally announced September 2025.

Fast ground penetrating radar dual-parameter full waveform inversion method accelerated by hybrid compilation of CUDA kernel function and PyTorch

Authors: Lei Liu, Chao Song, Liangsheng He, Silin Wang, Xuan Feng, Cai Liu

Abstract: This study proposes a high-performance dual-parameter full waveform inversion framework (FWI) for ground-penetrating radar (GPR), accelerated through the hybrid compilation of CUDA kernel functions and PyTorch. The method leverages the computational efficiency of GPU programming while preserving the flexibility and usability of Python-based deep learning frameworks. By integrating customized CUDA… ▽ More This study proposes a high-performance dual-parameter full waveform inversion framework (FWI) for ground-penetrating radar (GPR), accelerated through the hybrid compilation of CUDA kernel functions and PyTorch. The method leverages the computational efficiency of GPU programming while preserving the flexibility and usability of Python-based deep learning frameworks. By integrating customized CUDA kernels into PyTorch's automatic differentiation mechanism, the framework enables accurate and efficient inversion of both dielectric permittivity and electrical conductivity. Experimental evaluations on synthetic data and real wavefield data demonstrate that the proposed method achieves dual-parameter FWI for GPR data while maintaining high accuracy. Moreover, the framework is flexible and extensible, supporting optional regularization strategies such as total variation and multi-scale inversion. These features make the proposed approach a practical and scalable framework for rapid GPR-based subsurface imaging in applications including civil engineering, environmental monitoring, and geophysical exploration. △ Less

Submitted 25 June, 2025; originally announced June 2025.

KP-PINNs: Kernel Packet Accelerated Physics Informed Neural Networks

Authors: Siyuan Yang, Cheng Song, Zhilu Lai, Wenjia Wang

Abstract: Differential equations are involved in modeling many engineering problems. Many efforts have been devoted to solving differential equations. Due to the flexibility of neural networks, Physics Informed Neural Networks (PINNs) have recently been proposed to solve complex differential equations and have demonstrated superior performance in many applications. While the L2 loss function is usually a de… ▽ More Differential equations are involved in modeling many engineering problems. Many efforts have been devoted to solving differential equations. Due to the flexibility of neural networks, Physics Informed Neural Networks (PINNs) have recently been proposed to solve complex differential equations and have demonstrated superior performance in many applications. While the L2 loss function is usually a default choice in PINNs, it has been shown that the corresponding numerical solution is incorrect and unstable for some complex equations. In this work, we propose a new PINNs framework named Kernel Packet accelerated PINNs (KP-PINNs), which gives a new expression of the loss function using the reproducing kernel Hilbert space (RKHS) norm and uses the Kernel Packet (KP) method to accelerate the computation. Theoretical results show that KP-PINNs can be stable across various differential equations. Numerical experiments illustrate that KP-PINNs can solve differential equations effectively and efficiently. This framework provides a promising direction for improving the stability and accuracy of PINNs-based solvers in scientific computing. △ Less

Submitted 11 June, 2025; v1 submitted 10 June, 2025; originally announced June 2025.

Comments: Accepted to IJCAI 2025

Journal ref: IJCAI 2025. Main Track. Pages 7904-7912

Simulating fluid vortex interactions on a superconducting quantum processor

Authors: Ziteng Wang, Jiarun Zhong, Ke Wang, Zitian Zhu, Zehang Bao, Chenjia Zhu, Wenwen Zhao, Yaomin Zhao, Yue Yang, Chao Song, Shiying Xiong

Abstract: Vortex interactions are commonly observed in atmospheric turbulence, plasma dynamics, and collective behaviors in biological systems. However, accurately simulating these complex interactions is highly challenging due to the need to capture fine-scale details over extended timescales, which places computational burdens on traditional methods. In this study, we introduce a quantum vortex method, re… ▽ More Vortex interactions are commonly observed in atmospheric turbulence, plasma dynamics, and collective behaviors in biological systems. However, accurately simulating these complex interactions is highly challenging due to the need to capture fine-scale details over extended timescales, which places computational burdens on traditional methods. In this study, we introduce a quantum vortex method, reformulating the Navier--Stokes (NS) equations within a quantum mechanical framework to enable the simulation of multi-vortex interactions on a quantum computer. We construct the effective Hamiltonian for the vortex system and implement a spatiotemporal evolution circuit to simulate its dynamics over prolonged periods. By leveraging eight qubits on a superconducting quantum processor with gate fidelities of 99.97\% for single-qubit gates and 99.76\% for two-qubit gates, we successfully reproduce natural vortex interactions. This method bridges classical fluid dynamics and quantum computing, offering a novel computational platform for studying vortex dynamics. Our results demonstrate the potential of quantum computing to tackle longstanding challenges in fluid dynamics and broaden applications across both natural and engineering systems. △ Less

Submitted 4 June, 2025; originally announced June 2025.

Comments: 19 pages, 10 figures

Alignment Phase Transition in Socially Driven Motion

Authors: Debasish Sarker, Yi Zhang, Lynn K. Perry, Daniel S. Messinger, Chaoming Song

Abstract: Collective human movement is a hallmark of complex systems, exhibiting emergent order across diverse settings, from pedestrian flows to biological collectives. In high-speed scenarios, alignment interactions ensure efficient flow and navigation. In contrast, alignment in low-speed, socially engaged contexts emerges not from locomotion goals but from interpersonal interaction. Using high-resolution… ▽ More Collective human movement is a hallmark of complex systems, exhibiting emergent order across diverse settings, from pedestrian flows to biological collectives. In high-speed scenarios, alignment interactions ensure efficient flow and navigation. In contrast, alignment in low-speed, socially engaged contexts emerges not from locomotion goals but from interpersonal interaction. Using high-resolution spatial and orientation data from preschool classrooms, we uncover a sharp, distance-dependent transition in pairwise alignment patterns that reflects a spontaneous symmetry breaking between distinct behavioral phases. Below a critical threshold of approximately 0.65\,m, individuals predominantly align side-by-side; beyond this range, face-to-face orientations prevail. We show that this transition arises from a distance-dependent competition among three alignment mechanisms: parallelization, opposition, and reciprocation, whose interplay generates a bifurcation structure in the effective interaction potential. A Fourier-based decomposition of empirical orientation distributions reveals these mechanisms, enabling the construction of a minimal pseudo-potential model that captures the alignment transition as a non-equilibrium phase transition. Monte Carlo simulations using the inferred interaction terms closely reproduce the empirical patterns. These findings establish a quantitative framework for social alignment in low-speed human motion, extending active matter theory to a previously unexplored regime of socially mediated orientation dynamics, with implications for modeling coordination and control in biological collectives and artificial swarms. △ Less

Submitted 2 June, 2025; originally announced June 2025.

Generic full-vector angular spectrum method for calculating diffraction of arbitrary electromagnetic fields

Authors: Chengda Song, Jing He, Guanghui Yuan

Abstract: Numerous vector angular spectrum methods have been presented to model the vectorial nature of diffractive electromagnetic field, facilitating optical field engineering in polarization-related and high numerical aperture systems. However, balancing accuracy and efficiency in state-of-the-art vector methods is challenging, especially with not well-defined incident fields. Here, we propose a full-vec… ▽ More Numerous vector angular spectrum methods have been presented to model the vectorial nature of diffractive electromagnetic field, facilitating optical field engineering in polarization-related and high numerical aperture systems. However, balancing accuracy and efficiency in state-of-the-art vector methods is challenging, especially with not well-defined incident fields. Here, we propose a full-vector angular spectrum method for accurate, efficient, robust diffraction computation, allowing truly arbitrary incidence by precisely modeling the projection rule among Cartesian polarization components. We address a prior oversight, that the longitudinal electric field's projection onto the diffracted field was insufficiently considered. Notably, our method inherently handles reflection and transmission at dielectric interfaces, which can be viewed as k-space filters. For rotationally symmetric system, it achieves unprecedented computation times of a few seconds, speeding up optical design via faster input-output mapping in optimization algorithms. △ Less

Submitted 19 May, 2025; originally announced May 2025.

Comments: 17 pages, 6 figures

Journal ref: Journal of Physics: Photonics 7(4), 045021, 2025

Seismic first-arrival traveltime simulation based on reciprocity-constrained PINN

Authors: Hang Geng, Chao Song, Umair bin Waheed, Cai Liu

Abstract: Simulating seismic first-arrival traveltime plays a crucial role in seismic tomography. First-arrival traveltime simulation relies on solving the eikonal equation. The accuracy of conventional numerical solvers is limited to a finite-difference approximation. In recent years, physics-informed neural networks (PINNs) have been applied to achieve this task. However, traditional PINNs encounter chall… ▽ More Simulating seismic first-arrival traveltime plays a crucial role in seismic tomography. First-arrival traveltime simulation relies on solving the eikonal equation. The accuracy of conventional numerical solvers is limited to a finite-difference approximation. In recent years, physics-informed neural networks (PINNs) have been applied to achieve this task. However, traditional PINNs encounter challenges in accurately solving the eikonal equation, especially in cases where the model exhibits directional scaling differences. These challenges result in substantial traveltime prediction errors when the traveling distance is long. To improve the accuracy of PINN in traveltime prediction, we incorporate the reciprocity principle as a constraint into the PINN training framework. Based on the reciprocity principle, which states that the traveltime between two points remains invariant when their roles as source and receiver are exchanged, we propose to apply this principle to multiple source-receiver pairs in PINN-based traveltime prediction. Furthermore, a dynamic weighting mechanism is proposed to balance the contributions of the eikonal equation loss and the reciprocity-constrained loss during the training process. This adaptive weighting evolves dynamically with the training epochs, enhancing the convergency of the training process. Experiments conducted on a simple lens velocity model, the Overthrust velocity model, and a 3D velocity model demonstrate that the introduction of the reciprocity-constrained PINN significantly improves the accuracy of traveltime predictions. △ Less

Submitted 8 May, 2025; originally announced May 2025.

Room-temperature high-average-power strong-field terahertz source based on industrial high-repetition-rate femtosecond laser

Authors: Deyin Kong, Yichen Su, Cheng Song, Xiaojun Wu

Abstract: Free-space strong-field terahertz (THz) pulses, generated via optical rectification of femtosecond lasers in nonlinear crystals, are pivotal in various applications. However, conventional Ti:sapphire lasers struggle to produce high-average-power THz due to their limited output power. While kilowatt ytterbium lasers are increasingly adopted, their application in THz generation faces challenges: low… ▽ More Free-space strong-field terahertz (THz) pulses, generated via optical rectification of femtosecond lasers in nonlinear crystals, are pivotal in various applications. However, conventional Ti:sapphire lasers struggle to produce high-average-power THz due to their limited output power. While kilowatt ytterbium lasers are increasingly adopted, their application in THz generation faces challenges: low optical-to-THz conversion efficiency (attributed to long pulse durations and low energy) and crystal damage under high pumping power. Here, we report a high-average-power strong-field THz source using a lithium niobate crystal pumped by a 1030-nm, 570-fs, 1-mJ, 50-kHz ytterbium femtosecond laser with tilted pulse front pumping (TPFP). By systematically optimizing TPFP implementations and comparing grating- and echelon-type configurations, we achieve a THz source with 64.5 mW average power at 42-W, 50-kHz pumping, and a focused peak electric field of 525 kV/cm at 0.83-mJ, 1-kHz operation. Additionally, we observe Zeeman torque signals in cobalt-iron ferromagnetic nanofilms. This high-repetition-rate, high-average-power THz system, combined with its potential capabilities in high signal-to-noise spectroscopy and imaging, promises transformative impacts in quantum matter manipulation, non-destructive testing, and biomedicine. △ Less

Submitted 19 April, 2025; originally announced April 2025.

Comments: 15 pages, 9 figures

MS_ATpV-FWI: Full Waveform Inversion based on Multi-scale Structural Similarity Index Measure and Anisotropic Total p-Variation Regularization

Authors: Liangsheng He, Chao Song, Cai Liu

Abstract: Full waveform inversion (FWI) is a high-resolution seismic inversion technique popularly used in oil and gas exploration. Traditional FWI employs the $l_2$ norm measurement to minimize the misfit between observed and predicted seismic data. However, when the background velocity is inaccurate or the seismic data lacks low-frequency components, the conventional FWI suffers from cycle skipping, leadi… ▽ More Full waveform inversion (FWI) is a high-resolution seismic inversion technique popularly used in oil and gas exploration. Traditional FWI employs the $l_2$ norm measurement to minimize the misfit between observed and predicted seismic data. However, when the background velocity is inaccurate or the seismic data lacks low-frequency components, the conventional FWI suffers from cycle skipping, leading to inaccurate inversion results. This paper introduces a multiscale structural similarity index measure (M-SSIM) objective function for FWI. We also incorporate anisotropic total p-variation regularization (ATpV) to further improve the accuracy of FWI. M-SSIM extracts multi-scale structural features of seismic data in terms of both phase and amplitude. These features can reduce the risk of cycle skipping and improve the stability of FWI. Additionally, ATpV applies structural constraints to the velocity gradients, which helps suppress artifacts and preserve the sharp boundaries of geological formations. We propose to use the automatic differentiation (AD) to efficiently and stably optimize this novelly introduced FWI objective function. Both synthetic and field seismic data demonstrate that the proposed method accurately characterizes complex subsurface velocity structures, even when the background velocity is crude, the data lacks low-frequency components, or contains noise. △ Less

Submitted 2 April, 2025; originally announced April 2025.

Why do Opinions and Actions Diverge? A Dynamic Framework to Explore the Impact of Subjective Norms

Authors: Chen Song, Vladimir Cvetkovic, Rong Su

Abstract: Socio-psychological studies have identified a common phenomenon where an individual's public actions do not necessarily coincide with their private opinions, yet most existing models fail to capture the dynamic interplay between these two aspects. To bridge this gap, we propose a novel agent-based modeling framework that integrates opinion dynamics with a decision-making mechanism. More precisely,… ▽ More Socio-psychological studies have identified a common phenomenon where an individual's public actions do not necessarily coincide with their private opinions, yet most existing models fail to capture the dynamic interplay between these two aspects. To bridge this gap, we propose a novel agent-based modeling framework that integrates opinion dynamics with a decision-making mechanism. More precisely, our framework generalizes the classical Hegselmann-Krause model by combining it with a utility maximization problem. Preliminary results from our model demonstrate that the degree of opinion-action divergence within a population can be effectively controlled by adjusting two key parameters that reflect agents' personality traits, while the presence of social network amplifies the divergence. In addition, we study the social diffusion process by introducing a small number of committed agents into the model, and identify three key outcomes: adoption of innovation, rejection of innovation, and the enforcement of unpopular norms, consistent with findings in socio-psychological literature. The strong relevance of the results to real-world phenomena highlights our framework's potential for future applications in understanding and predicting complex social behaviors. △ Less

Submitted 22 March, 2025; originally announced March 2025.

Comments: This work has been submitted to the IEEE for possible publication. 12 pages, 12 figures

Switching exploration modes in human mobility

Authors: Lu Zhong, Lei Dong, Qi Wang, Chaoming Song, Jianxi Gao

Abstract: Recent advances in human mobility research have revealed consistent pairwise characteristics in movement behavior, yet existing mobility models often overlook the spatial and topological structure of mobility networks. By analyzing millions of devices' anonymized cell phone trajectories, we uncover a distinct modular organization within these networks, demonstrating that movements within spatial m… ▽ More Recent advances in human mobility research have revealed consistent pairwise characteristics in movement behavior, yet existing mobility models often overlook the spatial and topological structure of mobility networks. By analyzing millions of devices' anonymized cell phone trajectories, we uncover a distinct modular organization within these networks, demonstrating that movements within spatial modules differ significantly from those between modules. This finding challenges the conventional assumption of uniform mobility dynamics and underscores the influence of heterogeneous environments on human movement. Inspired by switching behaviors in animal movement patterns, we introduce a novel "switch mechanism" to differentiate movement modes, allowing our model to accurately reproduce both the modular structures of trajectory networks and spatial mobility patterns. Our results provide new insights into the dynamics of human mobility and its impact on network formation, with broad applications in traffic prediction, disease transmission modeling, and urban planning. Beyond advancing the theoretical and practical understanding of mobility networks, this work opens new avenues for understanding societal dynamics at large. △ Less

Submitted 20 May, 2025; v1 submitted 13 March, 2025; originally announced March 2025.

Comments: 27 pages, 7 figures

A new practical and effective source-independent full-waveform inversion with a velocity-distribution supported deep image prior: Applications to two real datasets

Authors: Chao Song, Tariq Alkhalifah, Umair Bin Waheed, Silin Wang, Cai Liu

Abstract: Full-waveform inversion (FWI) is an advanced technique for reconstructing high-resolution subsurface physical parameters by progressively minimizing the discrepancy between observed and predicted seismic data. However, conventional FWI encounters challenges in real data applications, primarily due to its conventional objective of direct measurements of the data misfit. Accurate estimation of the s… ▽ More Full-waveform inversion (FWI) is an advanced technique for reconstructing high-resolution subsurface physical parameters by progressively minimizing the discrepancy between observed and predicted seismic data. However, conventional FWI encounters challenges in real data applications, primarily due to its conventional objective of direct measurements of the data misfit. Accurate estimation of the source wavelet is essential for effective data fitting, alongside the need for low-frequency data and a reasonable initial model to prevent cycle skipping. Additionally, wave equation solvers often struggle to accurately simulate the amplitude of observed data in real applications. To address these challenges, we introduce a correlation-based source-independent objective function for FWI that aims to mitigate source uncertainty and amplitude dependency, which effectively enhances its practicality for real data applications. We develop a deep-learning framework constrained by this new objective function with a velocity-distribution supported deep image prior, which reparameterizes velocity inversion into trainable parameters within an autoencoder, thereby reducing the nonlinearity in the conventional FWI's objective function. We demonstrate the superiority of our proposed method using synthetic data from benchmark velocity models and, more importantly, two real datasets. These examples highlight its effectiveness and practicality even under challenging conditions, such as missing low frequencies, a crude initial velocity model, and an incorrect source wavelet. △ Less

Submitted 1 March, 2025; originally announced March 2025.

Comments: 23 pages, 25 figures

Modeling shared micromobility as a label propagation process for detecting the overlapping communities

Authors: Peng Luo, Chengyu Song, Hao Li, Di Zhu, Fabio Duarte

Abstract: Shared micro-mobility such as e-scooters has gained significant popularity in many cities. However, existing methods for detecting community structures in mobility networks often overlook potential overlaps between communities. In this study, we conceptualize shared micro-mobility in urban spaces as a process of information exchange, where locations are connected through e-scooters, facilitating t… ▽ More Shared micro-mobility such as e-scooters has gained significant popularity in many cities. However, existing methods for detecting community structures in mobility networks often overlook potential overlaps between communities. In this study, we conceptualize shared micro-mobility in urban spaces as a process of information exchange, where locations are connected through e-scooters, facilitating the interaction and propagation of community affiliations. As a result, similar locations are assigned the same label. Based on this concept, we developed a Geospatial Interaction Propagation model (GIP) by designing a Speaker-Listener Label Propagation Algorithm (SLPA) that accounts for geographic distance decay, incorporating anomaly detection to ensure the derived community structures reflect meaningful spatial patterns. We applied this model to detect overlapping communities within the e-scooter system in Washington, D.C. The results demonstrate that our algorithm outperforms existing model of overlapping community detection in both efficiency and modularity. However, existing methods for detecting community structures in mobility networks often overlook potential overlaps between communities. In this study, we conceptualize shared micro-mobility in urban spaces as a process of information exchange, where locations are connected through e-scooters, facilitating the interaction and propagation of community affiliations. As a result, similar locations are assigned the same label. Based on this concept, we developed a Geospatial Interaction Propagation model (GIP) by designing a Speaker-Listener Label Propagation Algorithm (SLPA) that accounts for geographic distance decay, incorporating anomaly detection to ensure the derived community structures reflect meaningful spatial patterns. △ Less

Submitted 26 January, 2025; originally announced January 2025.

Correlated Growth of Causal Networks

Authors: Jiazhen Liu, Kunal Tamang, Dashun Wang, Chaoming Song

Abstract: The study of causal structure in complex systems has gained increasing attention, with many recent studies exploring causal networks that capture cause-effect relationships across diverse fields. Despite increasing empirical evidence linking causal structures to network topological correlations, the mechanisms underlying the emergence of these correlations in causal networks remain poorly understo… ▽ More The study of causal structure in complex systems has gained increasing attention, with many recent studies exploring causal networks that capture cause-effect relationships across diverse fields. Despite increasing empirical evidence linking causal structures to network topological correlations, the mechanisms underlying the emergence of these correlations in causal networks remain poorly understood. In this work, we propose a general growth framework for causal networks, incorporating two key types of correlations: causal and dynamic. We analytically demonstrate that degree correlations emerge as a consequence of marginal dependencies on these correlations. Our theoretical predictions align quantitatively with empirical data from four large-scale innovation networks. Our theory not only sheds light on the origins of topological correlations but also provides a general framework for understanding correlated growth across causal systems. △ Less

Submitted 24 February, 2025; v1 submitted 21 December, 2024; originally announced December 2024.

Quantifying the Dynamics of Innovation Abandonment Across Scientific, Technological, Commercial, and Pharmacological Domains

Authors: Binglu Wang, Ching Jin, Chaoming Song, Johannes Bjelland, Brian Uzzi, Dashun Wang

Abstract: Despite the vast literature on the diffusion of innovations that impacts a broad range of disciplines, our understanding of the abandonment of innovations remains limited yet is essential for a deeper understanding of the innovation lifecycle. Here, we analyze four large-scale datasets that capture the temporal and structural patterns of innovation abandonment across scientific, technological, com… ▽ More Despite the vast literature on the diffusion of innovations that impacts a broad range of disciplines, our understanding of the abandonment of innovations remains limited yet is essential for a deeper understanding of the innovation lifecycle. Here, we analyze four large-scale datasets that capture the temporal and structural patterns of innovation abandonment across scientific, technological, commercial, and pharmacological domains. The paper makes three primary contributions. First, across these diverse domains, we uncover one simple pattern of preferential abandonment, whereby the probability for individuals or organizations to abandon an innovation increases with time and correlates with the number of network neighbors who have abandoned the innovation. Second, we find that the presence of preferential abandonment fundamentally alters the way in which the underlying ecosystem breaks down, inducing a novel structural collapse in networked systems commonly perceived as robust against abandonments. Third, we derive an analytical framework to systematically understand the impact of preferential abandonment on network dynamics, pinpointing specific conditions where it may accelerate, decelerate, or have an identical effect compared to random abandonment, depending on the network topology. Together, these results deepen our quantitative understanding of the abandonment of innovation within networked social systems, with implications for the robustness and functioning of innovation communities. Overall, they demonstrate that the dynamics of innovation abandonment follow simple yet reproducible patterns, suggesting that the uncovered preferential abandonment may be a generic property of the innovation lifecycle. △ Less

Submitted 9 December, 2024; originally announced December 2024.

Dynamical Phase Transitions in Non-equilibrium Networks

Authors: Jiazhen Liu, Nathaniel M. Aden, Debasish Sarker, Chaoming Song

Abstract: Dynamical phase transitions (DPTs) characterize critical changes in system behavior occurring at finite times, providing a lens to study nonequilibrium phenomena beyond conventional equilibrium physics. While extensively studied in quantum systems, DPTs have remained largely unexplored in classical settings. Recent experiments on complex systems, from social networks to financial markets, have rev… ▽ More Dynamical phase transitions (DPTs) characterize critical changes in system behavior occurring at finite times, providing a lens to study nonequilibrium phenomena beyond conventional equilibrium physics. While extensively studied in quantum systems, DPTs have remained largely unexplored in classical settings. Recent experiments on complex systems, from social networks to financial markets, have revealed abrupt dynamical changes analogous to quantum DPTs, motivating the search for a theoretical understanding. Here, we present a minimal model for nonequilibrium networks, demonstrating that nonlinear interactions among network edges naturally give rise to DPTs. Specifically, we show that network degree diverges at a finite critical time, following a universal hyperbolic scaling, consistent with empirical observations. Our analytical results predict that key network properties, including degree distributions and clustering coefficients, exhibit critical scaling as criticality approaches. These findings establish a theoretical foundation for understanding emergent nonequilibrium criticality across diverse complex systems. △ Less

Submitted 9 December, 2024; originally announced December 2024.

In-situ Study of Understanding the Resistive Switching Mechanisms of Nitride-based Memristor Devices

Authors: Di Zhang, Rohan Dhall, Matthew M. Schneider, Chengyu Song, Hongyi Dou, Sundar Kunwar, Natanii R. Yazzie, Jim Ciston, Nicholas G. Cucciniello, Pinku Roy, Michael T. Pettes, John Watt, Winson Kuo, Haiyan Wang, Rodney J. McCabe, Aiping Chen

Abstract: Interface-type resistive switching (RS) devices with lower operation current and more reliable switching repeatability exhibits great potential in the applications for data storage devices and ultra-low-energy computing. However, the working mechanism of such interface-type RS devices are much less studied compared to that of the filament-type devices, which hinders the design and application of t… ▽ More Interface-type resistive switching (RS) devices with lower operation current and more reliable switching repeatability exhibits great potential in the applications for data storage devices and ultra-low-energy computing. However, the working mechanism of such interface-type RS devices are much less studied compared to that of the filament-type devices, which hinders the design and application of the novel interface-type devices. In this work, we fabricate a metal/TiOx/TiN/Si (001) thin film memristor by using a one-step pulsed laser deposition. In situ transmission electron microscopy (TEM) imaging and current-voltage (I-V) characteristic demonstrate that the device is switched between high resistive state (HRS) and low resistive state (LRS) in a bipolar fashion with sweeping the applied positive and negative voltages. In situ scanning transmission electron microscopy (STEM) experiments with electron energy loss spectroscopy (EELS) reveal that the charged defects (such as oxygen vacancies) can migrate along the intrinsic grain boundaries of TiOx insulating phase under electric field without forming obvious conductive filaments, resulting in the modulation of Schottky barriers at the metal/semiconductor interfaces. The fundamental insights gained from this study presents a novel perspective on RS processes and opens up new technological opportunities for fabricating ultra-low-energy nitride-based memristive devices. △ Less

Submitted 30 October, 2024; originally announced October 2024.

Lead-free Hybrid Perovskite: An Efficient Room Temperature Spin Generator via Large Interfacial Rashba effect

Authors: Lei Han, Qian Wang, Ying Lu, Sheng Tao, Wenxuan Zhu, Xiaoyu Feng, Shixuan Liang, Hua Bai, Chong Chen, Kai Wang, Zhou Yang, Xiaolong Fan, Cheng Song, Feng Pan

Abstract: Two-dimensional (2D) hybrid organic-inorganic perovskite (HOIP) demonstates great potential for developing flexible and wearable spintronic devices, by serving as spin sources via the bulk Rashba effect (BRE). However, the practical application of BRE in 2D HOIP faces huge challenges, particularly due to the toxicity of lead, which is crucial for achieving large spin-orbit coupling, and the restri… ▽ More Two-dimensional (2D) hybrid organic-inorganic perovskite (HOIP) demonstates great potential for developing flexible and wearable spintronic devices, by serving as spin sources via the bulk Rashba effect (BRE). However, the practical application of BRE in 2D HOIP faces huge challenges, particularly due to the toxicity of lead, which is crucial for achieving large spin-orbit coupling, and the restrictions in 2D HOIP candidates to meet specific symmetry-breaking requirements. To overcome these obstacles, we design a strategy to exploit the interfacial Rashba effect (IRE) of lead-free 2D HOIP (C6H5CH2CH2NH3)2CuCl4 (PEA-CuCl), manifesting as an efficient spin generator at room temperature. IRE of PEA-CuCl originates from the large orbital hybridization at the interface between PEA-CuCl and adjacent ferromagnetic layers. Spin-torque ferromagnetic resonance measurements further quantify a large Rashba effective field of 14.04 Oe per 10^11 A m-2, surpassing those of lead-based HOIP and traditional all-inorganic heterojunctions with noble metals. Our lead-free 2D HOIP PEA-CuCl, which harnesses large IRE for spin generation, is efficient, nontoxic, and economic, offering huge promise for future flexible and wearable spintronic devices. △ Less

Submitted 24 October, 2024; originally announced October 2024.

Machine learning of the Ising model on a spherical Fibonacci lattice

Authors: Zheng Zhou, Chen-Hui Song, Xu-Yang Hou, Hao Guo

Abstract: We investigate the Ising model on a spherical surface, utilizing a Fibonacci lattice to approximate uniform coverage. This setup poses challenges in achieving consistent lattice distribution across the sphere for comparison with planar models. We employ Monte Carlo simulations, principal component analysis (PCA), graph convolutional networks (GCNs) to study spin configurations across a range of te… ▽ More We investigate the Ising model on a spherical surface, utilizing a Fibonacci lattice to approximate uniform coverage. This setup poses challenges in achieving consistent lattice distribution across the sphere for comparison with planar models. We employ Monte Carlo simulations, principal component analysis (PCA), graph convolutional networks (GCNs) to study spin configurations across a range of temperatures and to determine phase transition temperatures. The Fibonacci lattice, despite its uniformity, contains irregular sites that influence spin behavior. In the ferromagnetic case, sites with fewer neighbors exhibit a higher tendency for spin flips at low temperatures, though this effect weakens as temperature increases, leading to a higher phase transition temperature than in the planar Ising model. In the antiferromagnetic case, lattice irregularities induce geometric frustration, resulting in highly degenerate ground states and the phase transition temperature lower than the planar square lattice. Phase transition temperatures are derived through specific heat, magnetic susceptibility analysis and GCNs predictions, yielding $T_c$ values for both ferromagnetic and antiferromagnetic scenarios. This work emphasizes the impact of the Fibonacci lattice's geometric properties-namely curvature and connectivity-on spin interactions in non-planar systems, with relevance to microgravity environments. △ Less

Submitted 8 December, 2025; v1 submitted 15 October, 2024; originally announced October 2024.

Comments: 12 pages, 15 figures

Journal ref: New J. Phys. 27 124601(2025)

Ferroelectricity-Driven Metallicity and Magnetic Skyrmions in van der Waals Cr2Ge2Te6/Hf2Ge2Te6 Multiferroic Heterostructure

Authors: Zheng Chen, Hongliang Hu, Wenjun Zhang, Xiaoping Wu, Ping Li, Changsheng Song

Abstract: Two-dimensional (2D) multiferroic heterostructures present a promising platform for advanced spin devices by leveraging the coexisting ferromagnetic (FM) and ferroelectric (FE) orders. Through first-principles calculations and micromagnetic simulations, we reveal non-volatile control of metallicity and topological spin textures in the Cr2Ge2Te6/Hf2Ge2Te6(CGT/HGT) heterostructure. Notably, manipula… ▽ More Two-dimensional (2D) multiferroic heterostructures present a promising platform for advanced spin devices by leveraging the coexisting ferromagnetic (FM) and ferroelectric (FE) orders. Through first-principles calculations and micromagnetic simulations, we reveal non-volatile control of metallicity and topological spin textures in the Cr2Ge2Te6/Hf2Ge2Te6(CGT/HGT) heterostructure. Notably, manipulating ferroelectric polarization in HGT significantly modulates the magnetic anisotropy energy (MAE) and Dzyaloshinskii-Moriya interaction (DMI) of CGT/HGT, reversing the easy magnetization axis from in-plane to out-of-plane. By analyzing the atomic-resolved SOC energy (ΔEsoc), it is found that the cause of the change comes from the Fert-Levy mechanism. Additionally, this polarization control enables the creation and annihilation of bimerons and skyrmions, with interlayer sliding further altering magnetic ordering. Our findings offer valuable insights into magnetoelectric coupling and spin texture manipulation in 2D magnets, highlighting their potential for next-generation spintronic and memory devices. △ Less

Submitted 11 October, 2024; v1 submitted 29 September, 2024; originally announced September 2024.

Comments: 9 pages,5 figures

Deep-learning real-time phase retrieval of imperfect diffraction patterns from X-ray free-electron lasers

Authors: Sung Yun Lee, Do Hyung Cho, Chulho Jung, Daeho Sung, Daewoong Nam, Sangsoo Kim, Changyong Song

Abstract: Machine learning is attracting surging interest across nearly all scientific areas by enabling the analysis of large datasets and the extraction of scientific information from incomplete data. Data-driven science is rapidly growing, especially in X-ray methodologies, where advanced light sources and detection technologies accumulate vast amounts of data that exceed meticulous human inspection capa… ▽ More Machine learning is attracting surging interest across nearly all scientific areas by enabling the analysis of large datasets and the extraction of scientific information from incomplete data. Data-driven science is rapidly growing, especially in X-ray methodologies, where advanced light sources and detection technologies accumulate vast amounts of data that exceed meticulous human inspection capabilities. Despite the increasing demands, the full application of machine learning has been hindered by the need for data-specific optimizations. In this study, we introduce a new deep-learning-based phase retrieval method for imperfect diffraction data. This method provides robust phase retrieval for simulated data and performs well on weak-signal single-pulse diffraction data from X-ray free-electron lasers. Moreover, the method significantly reduces data processing time, facilitating real-time image reconstructions that are crucial for high-repetition-rate data acquisition. Thus, this approach offers a reliable solution to the phase problem and is expected to be widely adopted across various research areas. △ Less

Submitted 24 September, 2024; originally announced September 2024.

MSC Class: 68T07 ACM Class: J.2

Electrical detection in two-terminal perpendicularly magnetized devices via geometric anomalous Nernst effect

Authors: Jiuming Liu, Bin Rong, Hua Bai, Xinqi Liu, Yanghui Liu, Yifan Zhang, Yujie Xiao, Yuzhen Liang, Qi Yao, Liyang Liao, Yumeng Yang, Cheng Song, Xufeng Kou

Abstract: The non-uniform current distribution arisen from either current crowding effect or hot spot effect provides a method to tailor the interaction between thermal gradient and electron transport in magnetically ordered systems. Here we apply the device structural engineering to realize an in-plane inhomogeneous temperature distribution within the conduction channel, and the resulting geometric anomalo… ▽ More The non-uniform current distribution arisen from either current crowding effect or hot spot effect provides a method to tailor the interaction between thermal gradient and electron transport in magnetically ordered systems. Here we apply the device structural engineering to realize an in-plane inhomogeneous temperature distribution within the conduction channel, and the resulting geometric anomalous Nernst effect (GANE) gives rise to a non-zero 2nd -harmonic resistance whose polarity corresponds to the out-of-plane magnetization of Co/Pt multi-layer thin film, and its amplitude is linearly proportional to the applied current. By optimizing the aspect ratio of convex-shaped device, the effective temperature gradient can reach up to 0.3 K/$μ$m along the y-direction, leading to a GANE signal of 28.3 $μ$V. Moreover, we demonstrate electrical write and read operations in the perpendicularly-magnetized Co/Pt-based spin-orbit torque device with a simple two-terminal structure. Our results unveil a new pathway to utilize thermoelectric effects for constructing high-density magnetic memories △ Less

Submitted 14 September, 2024; originally announced September 2024.

Machine Learning with Physics Knowledge for Prediction: A Survey

Authors: Joe Watson, Chen Song, Oliver Weeger, Theo Gruner, An T. Le, Kay Pompetzki, Ahmed Hendawy, Oleg Arenz, Will Trojak, Miles Cranmer, Carlo D'Eramo, Fabian Bülow, Tanmay Goyal, Jan Peters, Martin W. Hoffman

Abstract: This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast, with a focus on partial differential equations. These methods have attracted significant interest due to their potential impact on advancing scientific research and industrial practices by improving predictive models with small- or large-scale datasets and e… ▽ More This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast, with a focus on partial differential equations. These methods have attracted significant interest due to their potential impact on advancing scientific research and industrial practices by improving predictive models with small- or large-scale datasets and expressive predictive models with useful inductive biases. The survey has two parts. The first considers incorporating physics knowledge on an architectural level through objective functions, structured predictive models, and data augmentation. The second considers data as physics knowledge, which motivates looking at multi-task, meta, and contextual learning as an alternative approach to incorporating physics knowledge in a data-driven fashion. Finally, we also provide an industrial perspective on the application of these methods and a survey of the open-source ecosystem for physics-informed machine learning. △ Less

Submitted 15 May, 2025; v1 submitted 19 August, 2024; originally announced August 2024.

Comments: 61 pages, 8 figures, 2 tables. Accepted at the Transactions of Machine Learning Research (TMLR)

PINNPStomo: Simultaneous P- and S-wave seismic traveltime tomography using physics-informed neural networks with a new factored eikonal equation

Authors: Chao Song, Hang Geng, Umair bin Waheed, Cai Liu

Abstract: Seismic tomography has long been an effective tool for constructing reliable subsurface structures. However, simultaneous inversion of P- and S-wave velocities presents a significant challenge for conventional seismic tomography methods, which depend on numerical algorithms to calculate traveltimes. A physics informed neural network (PINN)-based seismic tomography method (PINNtomo) has been propos… ▽ More Seismic tomography has long been an effective tool for constructing reliable subsurface structures. However, simultaneous inversion of P- and S-wave velocities presents a significant challenge for conventional seismic tomography methods, which depend on numerical algorithms to calculate traveltimes. A physics informed neural network (PINN)-based seismic tomography method (PINNtomo) has been proposed to solve the eikonal equation and construct the velocity model. Leveraging the powerful approximation capabilities of neural networks, we propose extending PINNtomo to perform multiparameter inversion of P- and S-wave velocities jointly, which we refer to as PINNPStomo. In PINNPStomo, we employ two neural networks: one for the P- and S-wave traveltimes, and another for the P- and S-wave velocities. By optimizing the misfits of P- and S-wave first-arrival traveltimes calculated from the eikonal equations, we can obtain the predicted P- and S-wave velocities that determine these traveltimes. Recognizing that the original PINNtomo utilizes a multiplicative factored eikonal equation, which depends on background traveltimes corresponding to a homogeneous velocity at the source location. We propose a new factored eikonal equation for PINNPStomo to eliminate this dependency. The proposed PINNPStomo, incorporating the new factored eikonal equation, demonstrates superior convergence speed and multiparameter inversion accuracy. We validate these improvements using 2D Marmousi and 2D/3D Overthrust elastic velocity models across three different seismic data acquisition geometries. △ Less

Submitted 23 July, 2024; originally announced July 2024.

Comments: submitted to Geophysics

Universal expansion of human mobility across urban scales

Authors: Lu Zhong, Lei Dong, Qi Wang, Chaoming Song, Jianxi Gao

Abstract: Human mobility is a fundamental process underpinning socioeconomic life and urban structure. Classic theories, such as egocentric activity spaces and central place theory, provide crucial insights into specific facets of movement, like home-centricity and hierarchical spatial organization. However, identifying universal characteristics or an underlying principle that quantitatively links these dis… ▽ More Human mobility is a fundamental process underpinning socioeconomic life and urban structure. Classic theories, such as egocentric activity spaces and central place theory, provide crucial insights into specific facets of movement, like home-centricity and hierarchical spatial organization. However, identifying universal characteristics or an underlying principle that quantitatively links these disparate perspectives has remained a challenge. Here, we reveal such a connection by analyzing the spatial structure of individual daily mobility trajectories using network-based modules. We discover a universal scaling law: the spatial extent (radius) of these mobility modules expands sublinearly with increasing distance from home, a pattern consistent across three orders of magnitude. Furthermore, we demonstrate that these modules precisely map onto the nested hierarchy of urban systems, corresponding to local, city-level, and regional scales as distance from home increases. These findings deepen our understanding of human mobility dynamics and demonstrate the profound connection between classical urban theory, human geography, and mobility studies. △ Less

Submitted 20 May, 2025; v1 submitted 10 June, 2024; originally announced June 2024.

Comments: 19 pages, 6 figures

Data quality control system and long-term performance monitor of the LHAASO-KM2A

Authors: Zhen Cao, F. Aharonian, Axikegu, Y. X. Bai, Y. W. Bao, D. Bastieri, X. J. Bi, Y. J. Bi, W. Bian, A. V. Bukevich, Q. Cao, W. Y. Cao, Zhe Cao, J. Chang, J. F. Chang, A. M. Chen, E. S. Chen, H. X. Chen, Liang Chen, Lin Chen, Long Chen, M. J. Chen, M. L. Chen, Q. H. Chen, S. Chen , et al. (263 additional authors not shown)

Abstract: The KM2A is the largest sub-array of the Large High Altitude Air Shower Observatory (LHAASO). It consists of 5216 electromagnetic particle detectors (EDs) and 1188 muon detectors (MDs). The data recorded by the EDs and MDs are used to reconstruct primary information of cosmic ray and gamma-ray showers. This information is used for physical analysis in gamma-ray astronomy and cosmic ray physics. To… ▽ More The KM2A is the largest sub-array of the Large High Altitude Air Shower Observatory (LHAASO). It consists of 5216 electromagnetic particle detectors (EDs) and 1188 muon detectors (MDs). The data recorded by the EDs and MDs are used to reconstruct primary information of cosmic ray and gamma-ray showers. This information is used for physical analysis in gamma-ray astronomy and cosmic ray physics. To ensure the reliability of the LHAASO-KM2A data, a three-level quality control system has been established. It is used to monitor the status of detector units, stability of reconstructed parameters and the performance of the array based on observations of the Crab Nebula and Moon shadow. This paper will introduce the control system and its application on the LHAASO-KM2A data collected from August 2021 to July 2023. During this period, the pointing and angular resolution of the array were stable. From the observations of the Moon shadow and Crab Nebula, the results achieved using the two methods are consistent with each other. According to the observation of the Crab Nebula at energies from 25 TeV to 100 TeV, the time averaged pointing errors are estimated to be $-0.003^{\circ} \pm 0.005^{\circ}$ and $0.001^{\circ} \pm 0.006^{\circ}$ in the R.A. and Dec directions, respectively. △ Less

Submitted 13 June, 2024; v1 submitted 20 May, 2024; originally announced May 2024.

Comments: 15 pages, 9 figures

Performance of Superconducting Resonators Suspended on SiN Membranes

Authors: Trevor Chistolini, Kyunghoon Lee, Archan Banerjee, Mohammed Alghadeer, Christian Jünger, M. Virginia P. Altoé, Chengyu Song, Sudi Chen, Feng Wang, David I. Santiago, Irfan Siddiqi

Abstract: Suspending devices on thin SiN membranes can limit their interaction with the bulk substrate and reduce parasitic capacitance to ground. While suspending devices on membranes is used in many fields including radiation detection using superconducting circuits, there has been less investigation into maximum membrane aspect ratios and achievable suspended device quality, metrics important to establis… ▽ More Suspending devices on thin SiN membranes can limit their interaction with the bulk substrate and reduce parasitic capacitance to ground. While suspending devices on membranes is used in many fields including radiation detection using superconducting circuits, there has been less investigation into maximum membrane aspect ratios and achievable suspended device quality, metrics important to establish the applicable scope of the technique. Here, we investigate these metrics by fabricating superconducting coplanar waveguide resonators entirely atop thin ($\sim$110 nm) SiN membranes, where the membrane's shortest length to thickness yields an aspect ratio of approximately $7.4 \times 10^3$. We compare these membrane resonators to on-substrate resonators on the same chip, finding similar internal quality factors $\sim$$10^5$ at single photon levels. Furthermore, we confirm that these membranes do not adversely affect resonator thermalization and conduct further materials characterization. By achieving high quality superconducting circuit devices fully suspended on thin SiN membranes, our results help expand the technique's scope to potential uses including incorporating higher aspect ratio membranes for device suspension and creating larger footprint, high impedance, and high quality devices. △ Less

Submitted 20 January, 2025; v1 submitted 2 May, 2024; originally announced May 2024.

Comments: 17 pages, 12 figures

Journal ref: Appl. Phys. Lett. 125, 204001 (2024)

Simulating unsteady fluid flows on a superconducting quantum processor

Authors: Zhaoyuan Meng, Jiarun Zhong, Shibo Xu, Ke Wang, Jiachen Chen, Feitong Jin, Xuhao Zhu, Yu Gao, Yaozu Wu, Chuanyu Zhang, Ning Wang, Yiren Zou, Aosai Zhang, Zhengyi Cui, Fanhao Shen, Zehang Bao, Zitian Zhu, Ziqi Tan, Tingting Li, Pengfei Zhang, Shiying Xiong, Hekang Li, Qiujiang Guo, Zhen Wang, Chao Song , et al. (2 additional authors not shown)

Abstract: Recent advancements of intermediate-scale quantum processors have triggered tremendous interest in the exploration of practical quantum advantage. The simulation of fluid dynamics, a highly challenging problem in classical physics but vital for practical applications, emerges as a good candidate for showing quantum utility. Here, we report an experiment on the digital simulation of unsteady flows,… ▽ More Recent advancements of intermediate-scale quantum processors have triggered tremendous interest in the exploration of practical quantum advantage. The simulation of fluid dynamics, a highly challenging problem in classical physics but vital for practical applications, emerges as a good candidate for showing quantum utility. Here, we report an experiment on the digital simulation of unsteady flows, which consists of quantum encoding, evolution, and detection of flow states, with a superconducting quantum processor. The quantum algorithm is based on the Hamiltonian simulation using the hydrodynamic formulation of the Schrödinger equation. With the median fidelities of 99.97% and 99.67% for parallel single- and two-qubit gates respectively, we simulate the dynamics of a two-dimensional (2D) compressible diverging flow and a 2D decaying vortex with ten qubits. The experimental results well capture the temporal evolution of averaged density and momentum profiles, and qualitatively reproduce spatial flow fields with moderate noises. This work demonstrates the potential of quantum computing in simulating more complex flows, such as turbulence, for practical applications. △ Less

Submitted 24 April, 2024; originally announced April 2024.

Electrical-controllable antiferromagnet-based tunnel junction

Authors: Lei Han, Xuming Luo, Yingqian Xu, Hua Bai, Wenxuan Zhu, Yuxiang Zhu, Guoqiang Yu, Cheng Song, Feng Pan

Abstract: Electrical-controllable antiferromagnet tunnel junction is a key goal in spintronics, holding immense promise for ultra-dense and ultra-stable antiferromagnetic memory with high processing speed for modern information technology. Here, we have advanced towards this goal by achieving an electrical-controllable antiferromagnet-based tunnel junction of Pt/Co/Pt/Co/IrMn/MgO/Pt. The exchange coupling b… ▽ More Electrical-controllable antiferromagnet tunnel junction is a key goal in spintronics, holding immense promise for ultra-dense and ultra-stable antiferromagnetic memory with high processing speed for modern information technology. Here, we have advanced towards this goal by achieving an electrical-controllable antiferromagnet-based tunnel junction of Pt/Co/Pt/Co/IrMn/MgO/Pt. The exchange coupling between antiferromagnetic IrMn and Co/Pt perpendicular magnetic multilayers results in the formation of interfacial exchange bias and exchange spring in IrMn. Encoding information states 0 and 1 is realized through the exchange spring in IrMn, which can be electrically written by spin-orbit torque switching with high cyclability and electrically read by antiferromagnetic tunneling anisotropic magnetoresistance. Combining spin-orbit torque switching of both exchange spring andexchange bias, 16 Boolean logic operation is successfully demonstrated. With both memory and logic functionalities integrated into our electrical-controllable antiferromagnetic-based tunnel junction, we chart the course toward high-performance antiferromagnetic logic-in-memory. △ Less

Submitted 1 April, 2024; originally announced April 2024.

Comments: 20 pages, 4 figures

Filter-free high-performance single photon emission from a quantum dot in a Fabry-Perot microcavity

Authors: Zhixuan Rao, Jiawei Yang, Changkun Song, Mujie Rao, Ziyang Zheng, Luyu Liu, Xuebin Peng, Ying Yu, Siyuan Yu

Abstract: Combining resonant excitation with Purcell-enhanced single quantum dots (QDs) stands out as a prominent strategy for realizing high performance solid-state single photon sources. However, optimizing photon efficiency requires addressing challenges associated with effectively separating the excitation laser from QDs' emission. Traditionally, this involves polarization filtering, which limits the ac… ▽ More Combining resonant excitation with Purcell-enhanced single quantum dots (QDs) stands out as a prominent strategy for realizing high performance solid-state single photon sources. However, optimizing photon efficiency requires addressing challenges associated with effectively separating the excitation laser from QDs' emission. Traditionally, this involves polarization filtering, which limits the achievable polarization directions and the scalability of photonic states. In this study, we have successfully tackled this challenge by employing spatially-orthogonal resonant excitation of QDs, deterministically coupled to monolithic Fabry-Perot microcavities. Leveraging the membrane cavity structures, we have achieved filter-free single photon resonant fluorescence. The resulting source produces single photons with a simultaneous high extraction efficiency of 0.87, purity of 0.9045(4), and indistinguishability of 0.963(4). △ Less

Submitted 17 March, 2024; v1 submitted 18 February, 2024; originally announced February 2024.

Comments: 9 pages, 4 figures

Electrical switching of the perpendicular Neel order in a collinear antiferromagnet

Authors: Wenqing He, Tianyi Zhang, Yongjian Zhou, Caihua Wan, Hao Wu, Baoshan Cui, Jihao Xia, Ran Zhang, Tengyu Guo, Peng Chen, Mingkun Zhao, Leina Jiang, Alexander Grutter, Purnima P. Balakrishnan, Andrew J. Caruana, Christy J. Kinane, Sean Langridge, Guoqiang Yu, Cheng Song, Xiufeng Han

Abstract: Electrical manipulation of magnetic order by current-induced spin torques lays the foundation for spintronics. One promising approach is encoding information in the Néel vector of antiferromagnetic (AFM) materials, particularly to collinear antiferromagnets with the perpendicular magnetic anisotropy (PMA), as the negligible stray fields and terahertz spin dynamics can enable memory devices with hi… ▽ More Electrical manipulation of magnetic order by current-induced spin torques lays the foundation for spintronics. One promising approach is encoding information in the Néel vector of antiferromagnetic (AFM) materials, particularly to collinear antiferromagnets with the perpendicular magnetic anisotropy (PMA), as the negligible stray fields and terahertz spin dynamics can enable memory devices with higher integration density and ultrafast speed. Here we demonstrate that the Néel order information in a prototypical collinear AFM insulator with PMA, Cr2O3, can be reliably readout via the anomalous Hall effect and efficiently switched by the spin-orbit torque (SOT) effect with a low current density of 5.8*106 A/cm2. Moreover, using Cr2O3 as a mediator, we electrically switch the magnetization of a Y3Fe5O12 film exchange-coupled to the Cr2O3 layer, unambiguously confirming the Néel order switching of the Cr2O3 layer. This work provides a significant basis for developing AFM memory devices based on collinear AFM materials with PMA. △ Less

Submitted 25 January, 2024; originally announced January 2024.

High-topological-number skyrmions and phase transition in two-dimensional frustrated $J_1$-$J_2$ magnets

Authors: Hongliang Hu, Zhong Shen, Zheng Chen, Xiaoping Wu, Tingting Zhong, Changsheng Song

Abstract: With the rapidly expanded field of two-dimensional(2D) magnetic materials, the frustrated magnetic skyrmions are attracting growing interest recently. Here, based on hexagonal close-packed (HCP) lattice of $J_1$-$J_2$ Heisenberg spins model, we systematically investigate the frustrated skyrmions and phase transition by micromagnetic simulations and first-principles calculations. The results show t… ▽ More With the rapidly expanded field of two-dimensional(2D) magnetic materials, the frustrated magnetic skyrmions are attracting growing interest recently. Here, based on hexagonal close-packed (HCP) lattice of $J_1$-$J_2$ Heisenberg spins model, we systematically investigate the frustrated skyrmions and phase transition by micromagnetic simulations and first-principles calculations. The results show that four spin phases of antiferromagnetic, labyrinth domain, skyrmion and ferromagnetic textures are determined by the identified ranges of $J_1$-$J_2$. Importantly, skyrmion phase with an increasing topological number ($Q$) covers a wider $J_1$-$J_2$ area. Then, the diameter of skyrmions can be tuned by the frustration strength ($|J_2/J_1|$) or external magnetic field. Besides, a phase transition from N$\acute{e}$el to Bloch type skyrmion is observed due to the change of the helicity with the variation of $|J_2/J_1|$. Furthermore, as increasing magnetic field, the skyrmions with high $Q$ ($\ge 3$) tend to split into the ones with $Q=1$, thereby achieving a lower systematic energy. Additionally, we find that the CoCl$_2$ monolayer satisfies the requirement of the frustrated $J_1$-$J_2$ magnet, and the related magnetic behaviors agree with the above conclusions. The frustration-induced skyrmions are stable without the manipulation of temperature and magnetic field. Our results may open a possible way toward spintronic applications based on High-topological-number and nanoscale topological spin textures of skyrmions. △ Less

Submitted 20 January, 2024; v1 submitted 11 January, 2024; originally announced January 2024.

Comments: 7 pages, 5 figures

Seismic traveltime simulation for variable velocity models using physics-informed Fourier neural operator

Authors: Chao Song, Tianshuo Zhao, Umair bin Waheed, Cai Liu, Tian You

Abstract: Seismic traveltime is critical information conveyed by seismic waves, widely utilized in various geophysical applications. Conventionally, the simulation of seismic traveltime involves solving the eikonal equation. However, the efficiency of traditional numerical solvers is hindered, as they are typically capable of simulating seismic traveltime for only a single source at a time. Recently, deep l… ▽ More Seismic traveltime is critical information conveyed by seismic waves, widely utilized in various geophysical applications. Conventionally, the simulation of seismic traveltime involves solving the eikonal equation. However, the efficiency of traditional numerical solvers is hindered, as they are typically capable of simulating seismic traveltime for only a single source at a time. Recently, deep learning tools, particularly physics-informed neural networks (PINNs), have proven effective in simulating seismic traveltimes for multiple sources. Nonetheless, PINNs face challenges such as limited generalization capabilities across different models and difficulties in training convergence. To address these issues, we have developed a method for simulating multi-source seismic traveltimes in variable velocity models using a deep-learning technique, known as the physics-informed Fourier neural operator (PIFNO). The PIFNO-based method for seismic traveltime generation takes both velocity and background traveltime as inputs, generating the perturbation traveltime as the output. This method incorporates a factorized eikonal equation as the loss function and relies solely on physical laws, eliminating the need for labeled training data. We demonstrate that our proposed method is not only effective in calculating seismic traveltimes for velocity models used during training but also shows promising prediction capabilities for test velocity models. We validate these features using velocity models from the OpenFWI dataset. △ Less

Submitted 8 April, 2024; v1 submitted 7 November, 2023; originally announced November 2023.

Comments: 13 pages, 12 figures, submitted to IEEE TGRS

Classification of skyrmionic textures and extraction of Hamiltonian parameters via machine learning

Authors: Dushuo Feng, Zhihao Guan, Xiaoping Wu, Yan Wu, Changsheng Song

Abstract: Classifying skyrmionic textures and extracting magnetic Hamiltonian parameters are fundamental and demanding endeavors within the field of two-dimensional (2D) spintronics. By using micromagnetic simulation and machine learning (ML) methods, we theoretically realize the recognition of nine skyrmionic textures and the mining of magnetic Hamiltonian parameters from massive spin texture images in 2D… ▽ More Classifying skyrmionic textures and extracting magnetic Hamiltonian parameters are fundamental and demanding endeavors within the field of two-dimensional (2D) spintronics. By using micromagnetic simulation and machine learning (ML) methods, we theoretically realize the recognition of nine skyrmionic textures and the mining of magnetic Hamiltonian parameters from massive spin texture images in 2D Heisenberg model. For textures classification, a deep neural network (DNN) trained according to transfer learning is proposed to distinguish nine different skyrmionic textures. For parameters extraction, based on the textures generated by different Heisenberg exchange stiffness (J), Dzyaloshinskii-Moriya strength (D), and anisotropy constant (K), we apply a multi-input single-output (MISO) deep learning model (handling with both images and parameters) and a support vector regression (SVR) model (dealing with Fourier features) to extract the parameters embedded in the spin textures. The models for classification and extraction both achieve great results with the accuracy of 98% (DNN),90% (MISO) and 80% (SVR). Importantly, via our ML methods, the skyrmionic textures with blurred phase boundaries can be effectively distinguished, and the concluded formation conditions of various skyrmionic textures, especially the skyrmion crystal, are consistent with previous reports. Besides, our models demonstrate the mapping relationship between spin texture images and magnetic parameters, which proves the feasibility of extracting microscopic mechanisms from experimental images and has guiding significance for the experiments of spintronics. △ Less

Submitted 27 September, 2023; originally announced September 2023.

Tunable quantum dots in monolithic Fabry-Perot microcavities for high-performance single-photon sources

Authors: Jiawei Yang, Yan Chen, Zixuan Rao, Ziyang Zheng, Changkun Song, Yujie Chen, Kaili Xiong, Pingxing Chen, Chaofan Zhang, Wei Wu, Ying Yu, Siyuan Yu

Abstract: Cavity-enhanced single quantum dots (QDs) are the main approach towards ultra-high-performance solid-state quantum light sources for scalable photonic quantum technologies. Nevertheless, harnessing the Purcell effect requires precise spectral and spatial alignment of the QDs' emission with the cavity mode, which is challenging for most cavities. Here we have successfully integrated miniaturized Fa… ▽ More Cavity-enhanced single quantum dots (QDs) are the main approach towards ultra-high-performance solid-state quantum light sources for scalable photonic quantum technologies. Nevertheless, harnessing the Purcell effect requires precise spectral and spatial alignment of the QDs' emission with the cavity mode, which is challenging for most cavities. Here we have successfully integrated miniaturized Fabry-Perot microcavities with a piezoelectric actuator, and demonstrated a bright single photon source derived from a deterministically coupled QD within this microcavity. Leveraging the cavity-membrane structures, we have achieved large spectral-tunability via strain tuning. On resonance, we have obtained a high Purcell factor of approximately 9. The source delivers single photons with simultaneous high extraction efficiency of 0.58, high purity of 0.956(2) and high indistinguishability of 0.922(4). Together with a small footprint, our scheme facilitates the scalable integration of indistinguishable quantum light sources on-chip, and therefore removes a major barrier to the solid-state quantum information platforms based on QDs. △ Less

Submitted 24 September, 2023; originally announced September 2023.

Comments: 12 pages, 4 figures

Layer-dependent exciton polarizability and the brightening of dark excitons in few-layer black phosphorus

Authors: Yuchen Lei, Junwei Ma, Jiaming Luo, Shenyang Huang, Boyang Yu, Chaoyu Song, Qiaoxia Xing, Fanjie Wang, Yuangang Xie, Jiasheng Zhang, Lei Mu, Yixuan Ma, Chong Wang, Hugen Yan

Abstract: The evolution of excitons from 2D to 3D is of great importance in photo-physics, yet the layer-dependent exciton polarizability has not been investigated in 2D semiconductors. Here, we determine the exciton polarizabilities for 3- to 11-layer black phosphorus-a direct bandgap semiconductor regardless of the thickness-through frequency-resolved photocurrent measurements on dual-gate devices and unv… ▽ More The evolution of excitons from 2D to 3D is of great importance in photo-physics, yet the layer-dependent exciton polarizability has not been investigated in 2D semiconductors. Here, we determine the exciton polarizabilities for 3- to 11-layer black phosphorus-a direct bandgap semiconductor regardless of the thickness-through frequency-resolved photocurrent measurements on dual-gate devices and unveil the carrier screening effect in relatively thicker samples. By taking advantage of the broadband photocurrent spectra, we are also able to reveal the exciton response for higher-index subbands under the gate electrical field. Surprisingly, dark excitons are brightened with intensity even stronger than the allowed transitions above certain electrical field. Our study not only sheds light on the exciton evolution with sample thickness, but also paves a way for optoelectronic applications of few-layer BP in modulators, tunable photodetectors, emitters and lasers. △ Less

Submitted 19 September, 2023; originally announced September 2023.

Comments: 30 pages, 4 figures

Self-Supervised Knowledge-Driven Deep Learning for 3D Magnetic Inversion

Authors: Yinshuo Li, Zhuo Jia, Wenkai Lu, Cao Song

Abstract: The magnetic inversion method is one of the non-destructive geophysical methods, which aims to estimate the subsurface susceptibility distribution from surface magnetic anomaly data. Recently, supervised deep learning methods have been widely utilized in lots of geophysical fields including magnetic inversion. However, these methods rely heavily on synthetic training data, whose performance is lim… ▽ More The magnetic inversion method is one of the non-destructive geophysical methods, which aims to estimate the subsurface susceptibility distribution from surface magnetic anomaly data. Recently, supervised deep learning methods have been widely utilized in lots of geophysical fields including magnetic inversion. However, these methods rely heavily on synthetic training data, whose performance is limited since the synthetic data is not independently and identically distributed with the field data. Thus, we proposed to realize magnetic inversion by self-supervised deep learning. The proposed self-supervised knowledge-driven 3D magnetic inversion method (SSKMI) learns on the target field data by a closed loop of the inversion and forward models. Given that the parameters of the forward model are preset, SSKMI can optimize the inversion model by minimizing the mean absolute error between observed and re-estimated surface magnetic anomalies. Besides, there is a knowledge-driven module in the proposed inversion model, which makes the deep learning method more explicable. Meanwhile, comparative experiments demonstrate that the knowledge-driven module can accelerate the training of the proposed method and achieve better results. Since magnetic inversion is an ill-pose task, SSKMI proposed to constrain the inversion model by a guideline in the auxiliary loop. The experimental results demonstrate that the proposed method is a reliable magnetic inversion method with outstanding performance. △ Less

Submitted 23 August, 2023; originally announced August 2023.

Comments: 11 pages, 14 figures

Twist-angle and thickness-ratio tuning of plasmon polaritons in twisted bilayer van der Waals films

Authors: Chong Wang, Yuangang Xie, Junwei Ma, Guangwei Hu, Qiaoxia Xing, Shenyang Huang, Chaoyu Song, Fanjie Wang, Yuchen Lei, Jiasheng Zhang, Lei Mu, Tan Zhang, Yuan Huang, Cheng-Wei Qiu, Yugui Yao, Hugen Yan

Abstract: Stacking bilayer structures is an efficient way to tune the topology of polaritons in in-plane anisotropic films, e.g., by leveraging the twist angle (TA). However, the effect of another geometric parameter, film thickness ratio (TR), on manipulating the plasmon topology in bilayers is elusive. Here, we fabricate bilayer structures of WTe2 films, which naturally host in-plane hyperbolic plasmons i… ▽ More Stacking bilayer structures is an efficient way to tune the topology of polaritons in in-plane anisotropic films, e.g., by leveraging the twist angle (TA). However, the effect of another geometric parameter, film thickness ratio (TR), on manipulating the plasmon topology in bilayers is elusive. Here, we fabricate bilayer structures of WTe2 films, which naturally host in-plane hyperbolic plasmons in the terahertz range. Plasmon topology is successfully modified by changing the TR and TA synergistically, manifested by the extinction spectra of unpatterned films and the polarization dependence of the plasmon intensity measured in skew ribbon arrays. Such TR- and TA-tunable topological transitions can be well explained based on the effective sheet optical conductivity by adding up those of the two films. Our study demonstrates TR as another degree of freedom for the manipulation of plasmonic topology in nanophotonics, exhibiting promising applications in bio-sensing, heat transfer and the enhancement of spontaneous emission. △ Less

Submitted 26 July, 2023; originally announced July 2023.

Comments: 18 pages, 4 figures

Journal ref: Nano Letters, 2023

Mobile phone data reveal spatiotemporal dynamics of Omicron infections in Beijing after relaxing zero-COVID policy

Authors: Xiaorui Yan, Ci Song, Tao Pei, Erjia Ge, Le Liu, Xi Wang, Linfeng Jiang

Abstract: The swift relaxation of the zero-COVID policy in December 2022 led to an unprecedented surge in Omicron variant infections in China. With the suspension of mandatory testing, tracking this epidemic outbreak was challenging because infections were often underrepresented in survey and testing results, which only involved partial populations. We used large-scale mobile phone data to estimate daily in… ▽ More The swift relaxation of the zero-COVID policy in December 2022 led to an unprecedented surge in Omicron variant infections in China. With the suspension of mandatory testing, tracking this epidemic outbreak was challenging because infections were often underrepresented in survey and testing results, which only involved partial populations. We used large-scale mobile phone data to estimate daily infections in Beijing from November 2022 to January 2023. We demonstrated that an individual's location records of mobile phone could be used to infer his or her infectious status. Then, the derived status of millions of individuals could be summed to reconstruct the citywide spatiotemporal dynamics of infections. We found that the infection incidence peaked on 21 December, and 80.1% of populations had been infected by 14 January 2023 in Beijing. Furthermore, infection dynamics exhibited significant demographic and spatiotemporal disparities. Our work provides a ubiquitous and high-coverage data source for monitoring epidemic outbreaks. △ Less

Submitted 25 June, 2023; originally announced July 2023.

Mechanical transistors for logic-with-memory computing

Authors: Huyue Chen, Chao Song, Jiahao Wu, Bihui Zou, Zhihan Zhang, An Zou, Yuljae Cho, Zhaoguang Wang, Wenming Zhang, Lei Shao, Jaehyung Ju

Abstract: As a potential revolutionary topic in future information processing, mechanical computing has gained tremendous attention for replacing or supplementing conventional electronics vulnerable to power outages, security attacks, and harsh environments. Despite its potential for constructing intelligent matter towards nonclassical computing systems beyond the von Neumann architecture, most works on mec… ▽ More As a potential revolutionary topic in future information processing, mechanical computing has gained tremendous attention for replacing or supplementing conventional electronics vulnerable to power outages, security attacks, and harsh environments. Despite its potential for constructing intelligent matter towards nonclassical computing systems beyond the von Neumann architecture, most works on mechanical computing demonstrated that the ad hoc design of simple logic gates cannot fully realize a universal mechanical processing framework involving interconnected arithmetic logic components and memory. However, such a logic-with-memory computing architecture is critical for complex and persistent state-dependent computations such as sequential logic. Here we propose a mechanical transistor (M-Transistor), abstracting omnipresent temperatures as the input-output mechanical bits, which consists of a metamaterial thermal channel as the gate terminal driving a nonlinear bistable soft actuator to selectively connect the output terminal to two other variable thermal sources. This M-Transistor is an elementary unit to modularly form various combinational and sequential circuits, such as complex logic gates, registers (volatile memory), and long-term memories (non-volatile memory) with much fewer units than the electronic counterparts. Moreover, they can establish a universal processing core comprising an arithmetic circuit and a register in a compact, reprogrammable network involving periodic read, write, memory, and logic operations of the mechanical bits. Our work contributes to realizing a non-electric universal mechanical computing architecture that combines multidisciplinary engineering with structural mechanics, materials science, thermal engineering, physical intelligence, and computational science. △ Less

Submitted 4 June, 2023; originally announced June 2023.

Comments: 25 pages, 4 figures, Article