arXiv subjects

Zhen Zhang

Publications and source records attributed to Zhen Zhang.

At least 469 records · Page 26Linked to original sources

arXiv2020

Learning Poisson systems and trajectories of autonomous systems via Poisson neural networks

We propose the Poisson neural networks (PNNs) to learn Poisson systems and trajectories of autonomous systems from data. Based on the Darboux-Lie theorem, the phase flow of a Poisson system can be written as the composition of (1) a coordinate transformation, (2) an extended symplectic map and (3) the inverse of the transformation. In this work, we extend this result to the unknotted trajectories of autonomous systems. We employ structured neural networks with physical priors to approximate the three aforementioned maps. We demonstrate through several simulations that PNNs are capable of handling very accurately several challenging tasks, including the motion of a particle in the electromagnetic potential, the nonlinear Schr{ö}dinger equation, and pixel observations of the two-body problem.

cs.LG↗

arXiv2020

$\textit{Ab initio}$ anharmonic thermodynamic properties of cubic CaSiO$_3$ perovskite

We present an $\textit{ab initio}$ study of the thermodynamic properties of cubic CaSiO$_3$ perovskite (CaPv) over the pressure and temperature range of the Earth's lower mantle. We compute the anharmonic phonon dispersions throughout the Brillouin zone by utilizing the phonon quasiparticle approach, which characterizes the intrinsic temperature dependence of phonon frequencies and, in principle, captures full anharmonicity. Such temperature-dependent phonon dispersions are used to calculate $\textit{ab initio}$ free energy in the thermodynamic limit ($N \rightarrow \infty$) within the framework of the phonon gas model. Accurate free energy calculations enable us to investigate cubic CaPv's thermodynamic properties and thermal equation of state, where anharmonic effects are demonstrated. The present methodology provides an important theoretical approach for exploring phase boundaries, thermodynamic, and thermoelastic properties of strongly anharmonic materials at high pressures and temperatures.

cond-mat.mtrl-sci↗

arXiv2020

Cyclic Label Propagation for Graph Semi-supervised Learning

Graph neural networks (GNNs) have emerged as effective approaches for graph analysis, especially in the scenario of semi-supervised learning. Despite its success, GNN often suffers from over-smoothing and over-fitting problems, which affects its performance on node classification tasks. We analyze that an alternative method, the label propagation algorithm (LPA), avoids the aforementioned problems thus it is a promising choice for graph semi-supervised learning. Nevertheless, the intrinsic limitations of LPA on feature exploitation and relation modeling make propagating labels become less effective. To overcome these limitations, we introduce a novel framework for graph semi-supervised learning termed as Cyclic Label Propagation (CycProp for abbreviation), which integrates GNNs into the process of label propagation in a cyclic and mutually reinforcing manner to exploit the advantages of both GNNs and LPA. In particular, our proposed CycProp updates the node embeddings learned by GNN module with the augmented information by label propagation, while fine-tunes the weighted graph of label propagation with the help of node embedding in turn. After the model converges, reliably predicted labels and informative node embeddings are obtained with the LPA and GNN modules respectively. Extensive experiments on various real-world datasets are conducted, and the experimental results empirically demonstrate that the proposed CycProp model can achieve relatively significant gains over the state-of-the-art methods.

cs.LG↗

arXiv2020

Comprehensive fabrication of SNAP microresonators by a femtosecond laser

Surface nanoscale axial photonics (SNAP) microresonators with nanoscale effective radius variation (ERV) along optical fiber axis can be fabricated by inscribing axially oriented lines inside the fiber with a femtosecond laser. The optimization of variable parameters in the femtosecond laser inscription technique is of great significance for flexible and ultra-high precision control of ERV, which is vital to the performance of SNAP devices. Here, we present the first systematical investigation on the relationships between the various controllable fabrication parameters and the introduced ERV of the SNAP microresonators. Specifically, both the qualitative and quantitative processing principles were revealed. As a proof-of-principle, by comprehensively optimizing the fabrication parameters, we realized a SNAP microresonator with the characteristics of both small axial size and maximal ERV, which is almost impossible to realize with other SNAP fabrication techniques. Our work promotes the fs laser inscription technology to be a flexible and versatile approach for fabricating the SNAP devices with ultra-high precision, ultra-low loss and high robustness.

physics.optics↗

arXiv2020

Deep Reinforcement Learning of Transition States

Combining reinforcement learning (RL) and molecular dynamics (MD) simulations, we propose a machine-learning approach (RL$^‡$) to automatically unravel chemical reaction mechanisms. In RL$^‡$, locating the transition state of a chemical reaction is formulated as a game, where a virtual player is trained to shoot simulation trajectories connecting the reactant and product. The player utilizes two functions, one for value estimation and the other for policy making, to iteratively improve the chance of winning this game. We can directly interpret the reaction mechanism according to the value function. Meanwhile, the policy function enables efficient sampling of the transition paths, which can be further used to analyze the reaction dynamics and kinetics. Through multiple experiments, we show that RL‡ can be trained tabula rasa hence allows us to reveal chemical reaction mechanisms with minimal subjective biases.

physics.chem-ph↗

arXiv2020

Femtosecond imbalanced time-stretch spectroscopy for ultrafast gas detection

Dual-comb spectroscopy is a promising method for precise optical spectrum analysis with fast data acquisition speed. However, its implementation and applications are often hindered by the complexity of optical comb systems. Here, as a compact and robust system, femtosecond imbalanced time-stretch spectroscopy (FITSS) with simple optical layout is proposed and demonstrated. The time-stretch interferometry from one femtosecond laser builds a mapping from the optical frequency domain to the radio frequency regime. In experiment, the absorption line of a hydrogen cyanide cell is encoded in the probing arm of a Mach-Zehnder interferometer (MZI). The down-converted radio frequency comb is transformed from a periodically chirped waveform, which is the interferogram of the MZI with different dispersion values on two arms. By turning the optical filter, the spectrum over a wide range is analyzed.

physics.optics↗

arXiv2020

CL-MAPF: Multi-Agent Path Finding for Car-Like Robots with Kinematic and Spatiotemporal Constraints

Multi-Agent Path Finding has been widely studied in the past few years due to its broad application in the field of robotics and AI. However, previous solvers rely on several simplifying assumptions. They limit their applicability in numerous real-world domains that adopt nonholonomic car-like agents rather than holonomic ones. In this paper, we give a mathematical formalization of Multi-Agent Path Finding for Car-Like robots (CL-MAPF) problem. For the first time, we propose a novel hierarchical search-based solver called Car-like Conflict-Based Search to address this problem. It applies a body conflict tree to address collisions considering shapes of the agents. We introduce a new algorithm called Spatiotemporal Hybrid-State A* as the single-agent path planner to generate path satisfying both kinematic and spatiotemporal constraints. We also present a sequential planning version of our method for the sake of efficiency. We compare our method with two baseline algorithms on a dedicated benchmark containing 3000 instances and validate it in real-world scenarios. The experiment results give clear evidence that our algorithm scales well to a large number of agents and is able to produce solutions that can be directly applied to car-like robots in the real world. The benchmark and source code are released in https://github.com/APRIL-ZJU/CL-CBS.

cs.RO↗

arXiv2020

Brain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraint

Delineating the brain tumor from magnetic resonance (MR) images is critical for the treatment of gliomas. However, automatic delineation is challenging due to the complex appearance and ambiguous outlines of tumors. Considering that multi-modal MR images can reflect different tumor biological properties, we develop a novel multi-modal tumor segmentation network (MMTSN) to robustly segment brain tumors based on multi-modal MR images. The MMTSN is composed of three sub-branches and a main branch. Specifically, the sub-branches are used to capture different tumor features from multi-modal images, while in the main branch, we design a spatial-channel fusion block (SCFB) to effectively aggregate multi-modal features. Additionally, inspired by the fact that the spatial relationship between sub-regions of tumor is relatively fixed, e.g., the enhancing tumor is always in the tumor core, we propose a spatial loss to constrain the relationship between different sub-regions of tumor. We evaluate our method on the test set of multi-modal brain tumor segmentation challenge 2020 (BraTs2020). The method achieves 0.8764, 0.8243 and 0.773 dice score for whole tumor, tumor core and enhancing tumor, respectively.

eess.IV↗

arXiv2020

Systematic study of proton radioactivity of spherical proton emitters with Skyrme interactions

Proton radioactivity is an important and common process of the natural radioactivity of proton-rich nuclei. In our previous work [J. H. Cheng et al., Nucl. Rhys. A 997, 121717 (2020)], we systematically studied the proton radioactivity half-lives of 53< Z <83 nuclei within the two-potential approach with Skyrme-Hartree-Fock. The calculations can well reproduce the experimental data of spherical nuclei. The present work is an extension of the previous work. We systematically study 32 proton radioactivity half-lives of spherical nuclei with the two-potential approach with Skyrme-Hartree-Fock (TPA-SHF) within 115 different versions of the Skyrme interactions. The calculated results indicate that the SLy7 Skyrme interaction gives the lowest r.m.s. deviation in the description of the experimental data of the spherical proton emitters among all the different versions of the Skyrme interactions. In addition, we use this model with the SLy7 Skyrme interaction to predict the proton radioactivity half-lives of 7 spherical nuclei in the same region, whose proton radioactivity is energetically allowed or has been observed but is not yet quantified.

nucl-th↗

arXiv2020

Sudden collapse of magnetic order in oxygen deficient nickelate films

Oxygen vacancies play a crucial role in the control of the electronic, magnetic, ionic, and transport properties of functional oxide perovskites. Rare earth nickelates (RENiO$_{3-x}$) have emerged over the years as a rich platform to study the interplay between the lattice, the electronic structure, and ordered magnetism. In this study, we investigate the evolution of the electronic and magnetic structure in thin films of RENiO$_{3-x}$, using a combination of X-ray absorption spectroscopy and imaging, resonant X-ray scattering, and extended multiplet ligand field theory modeling. We find that oxygen vacancies modify the electronic configuration within the Ni-O orbital manifolds, leading to a dramatic evolution of long-range electronic transport pathways despite the absence of nanoscale phase separation. Remarkably, magnetism is robust to substantial levels of carrier doping, and only a moderate weakening of the $(1/4, 1/4, 1/4)_{pc}$ antiferromagnetic order parameter is observed, whereas the magnetic transition temperature is largely unchanged. Only at a certain point long-range magnetism is abruptly erased without an accompanying structural transition. We propose the progressive disruption of the 3D magnetic superexchange pathways upon introduction of point defects as the mechanism behind the sudden collapse of magnetic order in oxygen-deficient nickelates. Our work demonstrates that, unlike most other oxides, ordered magnetism in RENiO$_{3-x}$ is mostly insensitive to carrier doping. The sudden collapse of ordered magnetism upon oxygen removal may provide a new mechanism for solid-state magneto-ionic switching and new applications in antiferromagnetic spintronics.

cond-mat.str-el↗

arXiv2020

Kagome quantum anomalous Hall effect with high Chern number and large band gap

Due to the potential applications in the low-power-consumption spintronic devices, the quantum anomalous Hall effect (QAHE) has attracted tremendous attention in past decades. However, up to now, QAHE was only observed experimentally in topological insulators with Chern numbers C= 1 and 2 at very low temperatures. Here, we propose three novel two-dimensional stable kagome ferromagnets Co3Pb3S2, Co3Pb3Se2and Co3Sn3Se2that can realize QAHE with high Chern number of |C|=3. Monolayers Co3Pb3S2, Co3Pb3Se2 and Co3Sn3Se2 possess the large band gap of 70, 77 and 63 meV with Curie temperature TC of 51, 42 and 46 K, respectively. By constructing a heterostructure Co3Sn3Se2/MoS2, its TC is enhanced to 60 K and the band gap keeps about 60 meV due to the tensile strain of 2% at the interface. For the bilayer compound Co6Sn5Se4, it becomes a half-metal, with a relatively flat plateau in its anomalous Hall conductivity corresponding to |C| = 3 near the Fermi level. Our results provide new topological nontrivial systems of kagome ferromagnetic monolayers and heterostructrues possessing QAHE with high Chern number |C| = 3 and large band gaps.

cond-mat.mtrl-sci↗

arXiv2020

Nuclear collective dynamics in transport model with the lattice Hamiltonian method

We review the recent progress on studying the nuclear collective dynamics by solving the Boltzmann-Uehling-Uhlenbeck (BUU) equation with the lattice Hamiltonian method treating the collision term by the full-ensemble stochastic collision approach. This lattice BUU (LBUU) method has recently been developed and implemented in a GPU parallel computing technique, and achieves a rather stable nuclear ground-state evolution and high accuracy in evaluating the nucleon-nucleon (NN) collision term. This new LBUU method has been applied to investigate the nuclear isoscalar giant monopole resonances and isovector giant dipole resonances. While the calculations with the LBUU method without the NN collision term (i.e., the lattice Hamiltonian Vlasov method) describe reasonably the excitation energies of nuclear giant resonances, the full LBUU calculations can well reproduce the width of the giant dipole resonance of $^{208}$Pb by including a collisional damping from NN scattering. The observed strong correlation between the width of nuclear giant dipole resonance and the NN elastic cross section suggests that the NN elastic scattering plays an important role in nuclear collective dynamics, and the width of nuclear giant dipole resonance provides a good probe of the in-medium NN elastic cross section.

nucl-th↗

arXiv2020

Voting Data-Driven Regression Learning for Discovery of Functional Materials and Applications to Two-Dimensional Ferroelectric Materials

Regression machine learning is widely applied to predict various materials. However, insufficient materials data usually leads to a poor performance. Here, we develop a new voting data-driven method that could generally improve the performance of regression learning model for accurately predicting properties of materials. We apply it to investigate a large family (2135) of two-dimensional hexagonal binary compounds focusing on ferroelectric properties and find that the performance of the model for electric polarization is indeed greatly improved, where 38 stable ferroelectrics with out-of-plane polarization including 31 metals and 7 semiconductors are screened out. By an unsupervised learning, actionable information such as how the number and orbital radius of valence electrons, ionic polarizability, and electronegativity of constituent atoms affect polarization was extracted. Our voting data-driven method not only reduces the size of materials data for constructing a reliable learning model but also enables to make precise predictions for targeted functional materials.

cond-mat.mtrl-sci↗

arXiv2020

Constraining isovector nuclear interactions with giant resonances within a Bayesian approach

We put a stringent constraint on the isovector nuclear interactions in the Skyrme-Hartree-Fock model from the centroid energy $E_{-1}$ of the isovector giant dipole resonance in $^{208}$Pb as well as its electric polarizability $α_D$. Using the Bayesian analysis method, $E_{-1}$ and $α_D$ are found to be mostly determined by the nuclear symmetry energy $E_{sym}$ at about $ρ^\star=0.05$ fm$^{-3}$ and the isovector nucleon effective mass $m_v^\star$ at the saturation density. At $90\%$ confidence level, we obtain $E_{sym}(ρ^\star) = 16.4 ^{+1.0}_{-0.9}$ MeV and $m_v^\star/m = 0.79 ^{+0.06}_{-0.06}$.

nucl-th↗

arXiv2020

Ultrathin broadband reflective optical limiter

Optical limiters are nonlinear devices that feature decreasing transmittance with increasing incident optical intensity, and thus can protect sensitive components from high-intensity illumination. The ideal optical limiter reflects rather than absorbs light in its active ("limiting") state, minimizing risk of damage to the limiter itself. Previous efforts to realize reflective limiters were based on embedding nonlinear layers into relatively thick multilayer photonic structures, resulting in substantial fabrication complexity, reduced speed and, in some instances, limited working bandwidth. We overcome these tradeoffs by using the insulator-to-metal transition in vanadium dioxide (VO2) to achieve intensity-dependent modulation of resonant transmission through aperture antennas. Due to the dramatic change of optical properties across the insulator-to-metal transition, low-quality-factor resonators were sufficient to achieve high on-off ratios in device transmittance. As a result, our ultra-thin reflective limiter (thickness ~1/100 of the free-space wavelength) is broadband in terms of operating wavelength (> 2 um at 10 um) and angle of incidence (up to ~50$°$ away from the normal).

physics.optics↗

arXiv2020

Antiferromagnetic and Electric Polarized States in Two-Dimensional Janus Semiconductor Fe2Cl3I3

Two-dimensional (2D) Janus semiconductors with mirror asymmetry can introduce novel properties, such as large spin-orbit coupling (SOC) and normal piezoelectric polarization, which have attracted a great interest for their potential applications. Inspired by the recently fabricated 2D ferromagnetic (FM) semiconductor CrI3, a stable 2D (in x-y plane) antiferromagnetic (AFM) Janus semiconductor Fe2Cl3I3 with normal sublattice magnetization (m//z) is obtained by density functional theory calculations. By applying a tensile strain, the four magnetic states sequentially occur: AFM with m//z of sublattice, AFM with m//xy of sublattice, FM with m//xy, and FM with m//z. Such novel magnetic phase diagram driven by strain can be well understood by the spin-spin interactions including the third nearest-neighbor hoppings with the single-ion anisotropy, in which the SOC of I atoms is found to play an essential role. In addition, the electric polarization of Fe2Cl3I3 preserves with strain due to the broken inversion symmetry. Our results predict the rare Janus material Fe2Cl3I3 as an example of 2D semiconductors with both spin and charge polarizations, and reveal the highly sensitive strain-controlled magnetic states and magnetization direction, which highlights the 2D magnetic Janus semiconductor as a new platform to design spintronic materials.

cond-mat.mtrl-sci↗

arXiv2020

Octupole based current horn suppresion in multi-stage bunch compression with emittance growth correction

High brightness linear accelerators typically produce electron beams with peaks in the head and/or tail of the current profile. These current horns are formed after bunch compression due to non-linear correlations in the longitudinal phase space and the higher order optics of the compressor. It has been suggested that this higher order compression can be corrected by inserting an octupole magnet near the center of a bunch compressor. However, this scheme provides a correlated transverse kick leading to growth of the projected emittance. We present here a method whereby octupole magnets are inserted into two sequential bunch compressors. By tuning a $π$ betatron phase advance between the two octupoles, the correlated transverse kick from the first octupole can be corrected by the second, while providing a cummulative adjustment of the higher order compression.

physics.acc-ph↗

arXiv2020

SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems

We propose new symplectic networks (SympNets) for identifying Hamiltonian systems from data based on a composition of linear, activation and gradient modules. In particular, we define two classes of SympNets: the LA-SympNets composed of linear and activation modules, and the G-SympNets composed of gradient modules. Correspondingly, we prove two new universal approximation theorems that demonstrate that SympNets can approximate arbitrary symplectic maps based on appropriate activation functions. We then perform several experiments including the pendulum, double pendulum and three-body problems to investigate the expressivity and the generalization ability of SympNets. The simulation results show that even very small size SympNets can generalize well, and are able to handle both separable and non-separable Hamiltonian systems with data points resulting from short or long time steps. In all the test cases, SympNets outperform the baseline models, and are much faster in training and prediction. We also develop an extended version of SympNets to learn the dynamics from irregularly sampled data. This extended version of SympNets can be thought of as a universal model representing the solution to an arbitrary Hamiltonian system.

cs.LG↗