arXiv subjects

Peng Song

Publications and source records attributed to Peng Song.

At least 19 recordsLinked to original sources

arXiv2026

Room-temperature ferroelectrically switchable quantum geometry in few-layer WTe2 for complementary in-memory computing

Quantum geometry, describing the inherent geometric structure of electron wavefunctions in momentum space, transcends the traditional charge degree of freedom and provides a novel physical basis for information encoding and processing. The key to such new computing paradigms is the non-volatile electrical programming of quantum geometric states at room temperature, which, however, has not been established. Here, we demonstrate ferroelectrically switchable quantum geometry in few-layer WTe2, which uniquely enables complementary convolutional processing. By employing the intrinsic coupling between ferroelectric polarization and quantum geometry in few-layer WTe2, we show that the second- and third-order nonlinear anomalous Hall effects (NLAHE) can be deterministically and electrically switched in a nonvolatile and correlated manner. The switching is robust at room temperature for ~104 cycles and retention of ~105 s. Furthermore, leveraging the opposite switching behaviors of second- and third-order NLAHE at room temperature, we demonstrate complementary in-memory computing and implement a hardware-level complementary convolution kernel. This kernel overcomes the inherent directional specificity of conventional convolutional networks and achieves a texture recognition accuracy of 98%, thereby illustrating a viable pathway towards physics-native computing through exploiting exotic physics in quantum materials.

cond-mat.mtrl-sci↗

arXiv2026

RL-Lock: Reinforcement Learning for Generating Interlocking Assemblies

An interlocking assembly is an assembly in which component parts are connected purely through their geometric arrangement, without relying on external connectors such as glue and nails. Such assemblies have been widely used in a variety of real-world applications due to their structural stability. The problem of generating interlocking assemblies is generally formulated as a shape decomposition problem, where a target 3D object represented as a voxel grid is partitioned into a prescribed number of interlocking pieces. We observe that generating interlocking assemblies is inherently a sequential decision-making problem, where an agent repeatedly decides which piece each voxel should be assigned to. Inspired by the observation, we propose the first reinforcement learning framework RL-Lock for generating interlocking assemblies, without relying on handcrafted search heuristics as existing works did. RL-Lock combines structured action chunking with MCTS-guided policy-value learning to efficiently navigate the large combinatorial search space for interlocking assembly generation. We demonstrate through experiments that RL-Lock allows effective generation of interlocking assemblies, especially for challenging cases in which existing approaches take too long or even fail to find a valid solution.

cs.AI↗

arXiv2025

BF-APNN: A Low-Memory Method for Accelerating the Solution of Radiative Transfer Equations

The Radiative Transfer Equations (RTEs) exhibit high dimensionality and multiscale characteristics, rendering conventional numerical methods computationally intensive. Existing deep learning methods perform well in low-dimensional or linear RTEs, but still face many challenges with high-dimensional or nonlinear RTEs. To overcome these challenges, we propose the Basis Function Asymptotically Preserving Neural Network (BF-APNN), a framework that inherits the advantages of Radiative Transfer Asymptotically Preserving Neural Network (RT-APNN) and accelerates the solution process. By employing basis function expansion on the microscopic component, derived from micro-macro decomposition, BF-APNN effectively mitigates the computational burden associated with evaluating high-dimensional integrals during training. Numerical experiments, which involve challenging RTE scenarios featuring, nonlinearity, discontinuities, and multiscale behavior, demonstrate that BF-APNN substantially reduces training time compared to RT-APNN while preserving high solution accuracy. Moreover, BF-APNN exhibits superior performance in addressing complex, high-dimensional RTE problems, underscoring its potential as a robust tool for radiative transfer computations.

physics.comp-ph↗

arXiv2025

A Surrogate model for High Temperature Superconducting Magnets to Predict Current Distribution with Neural Network

Finite element methods (FEM) for high-temperature superconducting (HTS) magnets become time-consuming at larger scales, restricting the rapid optimization of meter-scale REBCO solenoids. In this work, a surrogate model based on a fully connected residual neural network (FCRN) is developed to predict the current density distribution in REBCO solenoids. Trained on datasets generated from FEM simulations by the T-A formulation, the FCRN model is evaluated under both fast ramping and steady-state scenarios, showing a lower validation loss than the fully connected network (FCN). When extrapolating geometric parameters beyond the training set, the model achieves a relative error of below 10 % for magnetization losses in Case 1 and an average error of 1.2 % for the central magnetic field in Case 2. Furthermore, deploying the steady-state surrogate model for rapid magnet design found the optimal solution within the parameter space under constraints, with a relative central magnetic field error of 0.2 % compared to FEM results. With rapid predictions, this surrogate model offers an efficient tool for the intelligent design of large-scale HTS magnets.

cs.LG↗

arXiv2025

Experimental and numerical study on current distribution in parallel co-wound no-insulation coils

No-insulation (NI) coils are known for their high thermal stability and self-protection features due to turn-to-turn contacts. Parallel co-winding is a promising method to reduce the charging delay of NI coils while maintaining thermal stability, demonstrating significant potential for applications in fusion and other large-scale or high-field magnets. The non-uniform current distribution among parallel superconducting tapes in parallel co-wound NI coils may lead to thermal and mechanical stability issues. In this work, we conducted current measurement experiments on small parallel co-wound NI REBCO coils to investigate the non-uniform current distribution and its influencing factors. The parallel tapes in the input and output sections of the test coils were separated and a series of Rogowski coils was used to measure the current in each tape during ramping charging process. We combined a field-circuit coupled model based on the T-A formulation with an equivalent circuit model to calculate the current distribution in co-wound coils. Both the measured and calculated results indicated that the current distribution during ramping was highly non-uniform, with some tapes carrying reverse currents. We calculated the current distribution in co-wound coils with different insulation methods and analyzed the influencing factors of the reverse current. The influence of the terminal resistance on current distribution was also discussed. This work could contribute to a deeper understanding of current distribution behavior in co-wound coils and provide insights for their application in large-scale or high-field magnet systems.

physics.app-ph↗

arXiv2024

Slow, Nanometer Light Confinement Observed in Atomically Thin TaS2

Extreme light confinement down to the atomic scale has been theoretically predicted for ultrathin, Ta-based transition metal dichalcogenides (TMDs). In this work, we experimentally demonstrate in 2H-TaS$_2$ monolayers and bilayers a lateral confinement ratio up to 300 at large wave vectors of $q = 0.15 \, \r{A}^{-1}$, and slow light behaviour with a group velocity $\sim 10^{-4}c$. Quantitative momentum-resolved electron energy loss spectroscopy (q-EELS) with a momentum resolution of $0.0056 \, \r{A}^{-1}$ was used as a platform for the nanoscale optical measurements. With it, momentum-dispersed, two-dimensional (2D) plasmon resonances were experimentally observed, showing a transition from 2D to 3D Coulomb interaction in the high-momentum regime, equivalent to light confinement volumes of $1\text{-}2 \, \text{nm}^3$. Remarkably, the resonant modes do not enter the electron-hole continuum, predicting even further enhanced optical field confinements for this material at cryogenic temperatures.

cond-mat.mtrl-sci↗

arXiv2024

Multi-modal Speech Emotion Recognition via Feature Distribution Adaptation Network

In this paper, we propose a novel deep inductive transfer learning framework, named feature distribution adaptation network, to tackle the challenging multi-modal speech emotion recognition problem. Our method aims to use deep transfer learning strategies to align visual and audio feature distributions to obtain consistent representation of emotion, thereby improving the performance of speech emotion recognition. In our model, the pre-trained ResNet-34 is utilized for feature extraction for facial expression images and acoustic Mel spectrograms, respectively. Then, the cross-attention mechanism is introduced to model the intrinsic similarity relationships of multi-modal features. Finally, the multi-modal feature distribution adaptation is performed efficiently with feed-forward network, which is extended using the local maximum mean discrepancy loss. Experiments are carried out on two benchmark datasets, and the results demonstrate that our model can achieve excellent performance compared with existing ones.

cs.CV↗

arXiv2024

Weighted Squared Volume Minimization (WSVM) for Generating Uniform Tetrahedral Meshes

This paper presents a new algorithm, Weighted Squared Volume Minimization (WSVM), for generating high-quality tetrahedral meshes from closed triangle meshes. Drawing inspiration from the principle of minimal surfaces that minimize squared surface area, WSVM employs a new energy function integrating weighted squared volumes for tetrahedral elements. When minimized with constant weights, this energy promotes uniform volumes among the tetrahedra. Adjusting the weights to account for local geometry further achieves uniform dihedral angles within the mesh. The algorithm begins with an initial tetrahedral mesh generated via Delaunay tetrahedralization and proceeds by sequentially minimizing volume-oriented and then dihedral angle-oriented energies. At each stage, it alternates between optimizing vertex positions and refining mesh connectivity through the iterative process. The algorithm operates fully automatically and requires no parameter tuning. Evaluations on a variety of 3D models demonstrate that WSVM consistently produces tetrahedral meshes of higher quality, with fewer slivers and enhanced uniformity compared to existing methods. Check out further details at the project webpage: https://kaixinyu-hub.github.io/WSVM.github.io.

cs.GR↗

arXiv2024

Electrical Control Grain Dimensionality with Multilevel Magnetic Anisotropy

In alignment with the increasing demand for larger storage capacity and longer data retention, electrical control of magnetic anisotropy has been a research focus in the realm of spintronics. Typically, magnetic anisotropy is determined by grain dimensionality, which is set during the fabrication of magnetic thin films. Despite the intrinsic correlation between magnetic anisotropy and grain dimensionality, there is a lack of experimental evidence for electrically controlling grain dimensionality, thereby impeding the efficiency of magnetic anisotropy modulation. Here, we demonstrate an electric field control of grain dimensionality and prove it as the active mechanism for tuning interfacial magnetism. The reduction in grain dimensionality is associated with a transition from ferromagnetic to superparamagnetic behavior. We achieve a non-volatile and reversible modulation of the coercivity in both the ferromagnetic and superparamagnetic regimes. Subsequent electrical and elemental analysis confirms the variation in grain dimensionality upon the application of gate voltages, revealing a transition from a multidomain to a single-domain state accompanied by a reduction in grain dimensionality. Furthermore, we exploit the influence of grain dimensionality on domain wall motion, extending its applicability to multilevel magnetic memory and synaptic devices. Our results provide a strategy for tuning interfacial magnetism through grain size engineering for advancements in high-performance spintronics.

cond-mat.mtrl-sci↗

arXiv2024

Giant third-order nonlinear Hall effect in misfit layer compound (SnS)${1.17}$(NbS$_2$)$_3$

Nonlinear Hall effect (NLHE) holds immense significance in recognizing the band geometry and its potential applications in current rectification. Recent discoveries have expanded the study from second-order to third-order nonlinear Hall effect (THE), which is governed by an intrinsic band geometric quantity called the Berry Connection Polarizability (BCP) tensor. Here we demonstrate a giant THE in a misfit layer compound, (SnS)${1.17}$(NbS$_2$)$_3$. While the THE is prohibited in individual NbS$_2$ and SnS due to the constraints imposed by the crystal symmetry and their band structures, a remarkable THE emerges when a superlattice is formed by introducing a monolayer of SnS. The angular-dependent THE and its scaling relationship indicate that the phenomenon could be correlated to the band geometry modulation, concurrently with the symmetry breaking. The resulting strength of THE is orders of magnitude higher compared to recent studies. Our work illuminates the modulation of structural and electronic geometries for novel quantum phenomena through interface engineering.

cond-mat.mes-hall↗

arXiv2023

An efficient numerical method for charged particle transport based on hybrid collision model and machine learning

Charged particle transport is an important energy transport mode in the combustion process of inertial confinement fusion plasma. On the one hand, charged particles inside the hot spot have a strong non-equilibrium effect, so it is necessary to solve the Boltzmann transport equation to simulate the energy transport process of charged particles accurately. On the other hand, charged particle transport has the characteristics of high collision frequency and complex blocking power, so the calculation amount of the traditional Monte Carlo algorithm is difficult to bear under the existing calculation conditions. Aiming at the computational bottleneck caused by the large Coulomb potential collision cross-section, we developed a hybrid collision model which greatly reduced the computational cost while maintaining the second-order accuracy of the collision process. In order to solve the computational bottleneck caused by the complex blocking power model, we developed a neural network model based on machine learning to achieve formal unity and efficient calculation of different blocking power. Based on the calculation method, we developed the charged particle transport MC function modules of the RDMG program and LARED-S program and applied them to the study of critical target performance of inertial confinement fusion, which showed good computational efficiency and accuracy.

physics.plasm-ph↗

arXiv2023

An implicit unified gas-kinetic wave-particle method for radiative transport process

The unified gas-kinetic wave-particle method (UGKWP) has been developed for the multiscale gas, plasma, and multiphase flow transport processes for the past years. In this work, we propose an implicit unified gas-kinetic wave-particle (IUGKWP) method to remove the CFL time step constraint. Based on the local integral solution of the radiative transfer equation (RTE), the particle transport processes are categorized into the long-$\lambda$ streaming process and the short-$\lambda$ streaming process comparing to a local physical characteristic time $t_p$. In the construction of the IUGKWP method, the long-$\lambda$ streaming process is tracked by the implicit Monte Carlo (IMC) method; the short-$\lambda$ streaming process is evolved by solving the implicit moments equations; and the photon distribution is closed by a local integral solution of RTE. In the IUGKWP method, the multiscale flux of radiation energy and the multiscale closure of photon distribution are constructed based on the local integral solution. The IUGKWP method preserves the second-order asymptotic expansion of RTE in the optically thick regime and adapts its computational complexity to the flow regime. The numerical dissipation is well controlled, and the teleportation error is significantly reduced in the optically thick regime. The computational complexity of the IUGKWP method decreases exponentially as the Knudsen number approaches zero, and the computational efficiency is remarkably improved in the optically thick regime. The IUGKWP is formulated on a generalized unstructured mesh, and multidimensional 2D and 3D algorithms are developed. Numerical tests are presented to validate the capability of IUGKWP in capturing the multiscale photon transport process. The algorithm and code will apply in the engineering applications of inertial confinement fusion (ICF).

physics.comp-ph↗

arXiv2023

Unified Gas-Kinetic Particle Method for Frequency-dependent Radiation Transport

This paper proposes a unified gas-kinetic particle (UGKP) method for the frequency-dependent photon transport process. The photon transport is a typical multiscale process governed by the nonlinear radiative transfer equations (RTE). The flow regime of photon transport varies from the ballistic regime to the diffusive regime with respect to optical depth and photon frequency. The UGKP method is an asymptotic preserving (AP) scheme and a regime adaptive scheme. The teleportation error is significantly reduced, and the computational efficiency is remarkably improved in the diffusive regime. Distinguished from the standard multigroup treatment, the proposed UGKP method solves the frequency space in a non-discretized way. Therefore the Rosseland diffusion system can be precisely preserved in the optically thick regime. Taking advantage of the local integral solution of RTE, the distribution of the emitted photon can be constructed from its macroscopic moments. The Monte Carlo particles in the UGKP method need only be tracked before their first collision events, and a re-sampling process is performed to close the photon distribution for each time step. The large computational cost of excessive scattering events can be saved, especially in the optically thick regime. The proposed UGKP method is implicit and removes the light speed constraint on the time step. The particle tracking approach combining the implicit formulation makes the proposed UGKP method an efficient solution algorithm for frequency-dependent radiative transfer problems. We demonstrate with numerical examples the capability of the proposed multi-frequency UGKP method.

math.NA↗

arXiv2022

A fully asymptotic preserving decomposed multi-group method for the frequency-dependent radiative transfer equations

The opacity of FRTE depends on not only the material temperature but also the frequency, whose values may vary several orders of magnitude for different frequencies. The gray radiation diffusion and frequency-dependent diffusion equations are two simplified models that can approximate the solution to FRTE in the thick opacity regime. The frequency discretization for the two limit models highly affects the numerical accuracy. However, classical frequency discretization for FRTE considers only the absorbing coefficient. In this paper, we propose a new decomposed multi-group method for frequency discretization that is not only AP in both gray radiation diffusion and frequency-dependent diffusion limits, but also the frequency discretization of the limiting models can be tuned. Based on the decomposed multi-group method, a full AP scheme in frequency, time, and space is proposed. Several numerical examples are used to verify the performance of the proposed scheme.

math.NA↗

arXiv2022

(La,Th)H$_{10}$: the potential high-$T_{c}$ superconductors stabilized thermodynamically below 200 GPa

The recent high-pressure experimental discovery of superconductivity in (La,Y)H$_{10}$, (La,Ce)H$_{9}$, (La,Ce)H$_{10}$, (Y,Ce)H$_{9}$, and (La,Nd)H$_{10}$ shows that the ternary rare-earth clathrate hydride can be promising candidate for high-temperature superconductor. In this work, we theoretically demonstrate that the combination of actinide-metal thorium (Th) and rare-earth-metal lanthanum (La) with hydrogen can also form some ternary hydrides with cage-like structures to be stable at 200 GPa. Using the evolutionary algorithms combined with the first-principles calculations, we have predicted the pressure-dependent ternary phase diagram of La$_{x}$Th$_{y}$H$_{z}$, particularly including the case of (La$_{1-x}$Th$_{x}$)H$_{n}$ [or designated as (La,Th)H$_{n}$ for simplicity]. Our calculations show that the hydrogen-rich phases such as (La,Th)H$_{9}$ (only including $P\bar{6}m2$-LaThH$_{18}$) and (La,Th)H$_{10}$ (including $I4/mmm$-La$_{3}$ThH$_{40}$, $R\bar{3}m$-LaThH$_{20}$, and $I4/mmm$-LaTh$_{3}$H$_{40}$) with H$_{29}$ and H$_{32}$ cages can be thermodynamically stable below 200 GPa. However, the phase decomposition can happen to only (La,Th)H$_{9}$ when the pressure is above 150 GPa. More importantly, the electron-phonon coupling (EPC) calculations show that the (La,Th)H$_{10}$ series could the potential superconductors, of which $I4/mmm$-La$_{3}$ThH$_{40}$ at 200 GPa exhibits the large EPC constant $\lambda$ = 2.46 with a highest transition temperature ($T_\mathrm{c}$) of 210 K. Since there are few previous studies on ternary hydrides composed of actinide metals, the present work would greatly stimulate the further discovery of this type of ternary hydrides and provide useful guidance for the high-pressure experimental studies on them.

cond-mat.supr-con↗

arXiv2022

Online Discovery of Evolving Groups over Massive-Scale Trajectory Streams

The increasing pervasiveness of object tracking technologies leads to huge volumes of spatiotemporal data collected in the form of trajectory streams. The discovery of useful group patterns from moving objects' movement behaviours in trajectory streams is critical for real-time applications ranging from transportation management to military surveillance. Motivated by this, we first propose a novel pattern, called evolving group, which models the unusual group events of moving objects that travel together within density connected clusters in evolving streaming trajectories. Our theoretical analysis and empirical study on the Osaka Pedestrian data and Beijing Taxi data demonstrate its effectiveness in capturing the development, evolution, and trend of group events of moving objects in streaming context. Moreover, we propose a discovery method that efficiently supports online detection of evolving groups over massive-scale trajectory streams using a sliding window. It contains three phases along with a set of novel optimization techniques designed to minimize the computation costs. Furthermore, to scale to huge workloads over evolving streams, we extend our discovery method to a parallel framework by using a sector-based partition. Our comprehensive empirical study demonstrates that our online discovery framework is effective and efficient on real-world high-volume trajectory streams.

cs.DB↗

arXiv2022

High-pressure BaCN$_2$ phases explored by genetic algorithm

Polymers containing nitrogen have attracted much attention in connection with their application to high energy density materials (HEDMs), in which energy is inherent in the triple bond. It is an interesting question whether such polymerized phases appear in the high-pressure phase of metal carbodiimide MCN$_2$, of which synthesis have been reported in recent years, but few studies have investigated the crystal structure at high pressure. We have adopted a structure search based on the genetic algorithm coupled with ab initio electronic structure calculations to investigate possible crystal structures that may appear in the high-pressure phase of BaCN$_2$. The structure search successfully reproduced the previously reported crystal structures in the lower pressure range. With confirmed reliability of its predictive ability, the genetic search further predicts a polymerized phase with Ima2 appearing at higher pressure above 42 GPa. The polymerized phase takes the structure of a linear network of CN$_3$ planar triangular units. It is understood that the anion site units CN$_2$, which are close to each other under high pressure, form covalent bonds directly with each other and stabilize the phase.

cond-mat.mtrl-sci↗

arXiv2022

High-$T\rm_{c}$ superconductivity of clathrate Y$_{3}$EuH$_{24}$

Recent progress on theoretical predictions of ternary superhydrides and their subsequent experimental confirmations have introduced us with a new generation of superconductors, having the potential to realize the synthesis of the most anticipated room temperature superconductors. Motivated by the recent high pressure experiment on YH$_6$ and EuH$_6$, we have theoretically examined the stability as well as superconductivity by mixing the two compounds. Here we identified four phases capable of maintaining thermodynamic stability in the pressure range of 150-300 GPa, namely $Fm\bar{3}m$-Y$_{3}$EuH$_{24}$, $Cmmm$-YEuH$_{12}$, $Cmmm$-YEu$_{3}$H$_{24}$,$Immm$-YEu$_{3}$H$_{24}$. Among them, only $Fm\bar{3}m$-Y$_{3}$EuH$_{24}$ can maintain dynamically stability in the searched pressure range. The Allen-Dynes modified McMillan formula was used to further check the superconductivity of this phase, and the results reveal that the predicted superconducting transition temperature $T\rm_{c}$ of Y$_{3}$EuH$_{24}$ is approaching 220 K at 200 GPa. $Fm\bar{3}m$-Y$_{3}$EuH$_{24}$ has a nearly identical logarithmic average phonon frequency $\omega_{log}$ to YH$_{6}$, but its electron-phonon coupling (EPC) constant $\lambda$ is somewhat smaller than YH$_{6}$'s 2.56, resulting in a lower ultimate $T_{c}$ value.

cond-mat.supr-con↗