arXiv subjects

Liangliang Zhu

Publications and source records attributed to Liangliang Zhu.

6 recordsLinked to original sources

arXiv2026

Quantitative Disentanglement of Terahertz Spin and Orbital Pumping in 3d Ferromagnetic Heterostructures

Spin and orbital pumping - the injection of spin and orbital angular momentum from a driven ferromagnet into an adjacent nonmagnetic layer - are fundamental processes underlying angular-momentum generation and transport in magnetic heterostructures. Femtosecond optical excitation extends these phenomena into the ultrafast regime, where spintronic terahertz emission spectroscopy (STES) detects picosecond angular-momentum currents in a contact-free manner through spin-to-charge and orbital-to-charge conversion. Microscopic theory predicts that orbital-pumping efficiency increases from Fe to Ni across the 3d series, yet whether these predictions hold under ultrafast excitation remains unclear. A central challenge is that spin and orbital currents are generated simultaneously and contribute additively to the same terahertz emission, preventing their quantitative separation. Here, we overcome this limitation by combining STES with wedge-sample thickness control in heterostructures whose nonmagnetic layers (Ta, W, and Nb) have spin Hall and orbital Hall angles of opposite signs. The two angular-momentum channels therefore exhibit distinct emission polarities and thickness dependences, enabling their quantitative decomposition. Systematic measurements on Fe, Co, and Ni heterostructures reveal that the orbital-pumping contribution increases progressively toward Ni, reaching several tens of percent of the spin-current contribution - far exceeding theoretical predictions. Even Fe generates a non-negligible orbital current that becomes essential in the thin-nonmagnetic-layer regime. The extracted orbital diffusion lengths are consistently shorter than spin diffusion lengths and increase with decreasing spin-orbit coupling strength of the nonmagnetic layer. These results establish a quantitative framework for ultrafast spin and orbital pumping in magnetic heterostructures.

cond-mat.mes-hall↗

arXiv2026

Generative Inverse Design of Cold Metals for Low-Power Electronics

Cold metals are a class of metals with an intrinsic energy gap located close to the Fermi level, which enables cold-carrier injection for steep-slope transistors and is therefore promising for low-power electronic applications. High-throughput screening has revealed 252 three-dimensional (3D) cold metals in the Materials Project database, but database searches are inherently limited to known compounds. Here we present an inverse-design workflow that generates 3D cold metals using MatterGPT, a conditional autoregressive Transformer trained on SLICES, an invertible and symmetry-invariant crystal string representation. We curate a training set of 26,309 metallic structures labeled with energy above hull and a unified band-edge distance descriptor that merges p-type and n-type cold-metal characteristics to address severe label imbalance. Property-conditioned generation targeting thermodynamic stability and 50-500 meV band-edge distances produces 148,506 unique candidates; 92.1% are successfully reconstructed to 3D structures and down-selected by symmetry, uniqueness and novelty filters, followed by high-throughput DFT validation. We identify 257 cold metals verified as novel with respect to the Materials Project database, with gaps around the Fermi level spanning 50-500 meV. First-principles phonon, electronic-structure, and work-function calculations for representative candidates confirm dynamical stability and contact-relevant work functions. Our results demonstrate that SLICES-enabled generative transformers can expand the chemical space of cold metals beyond high-throughput screening, providing a route to low-power electronic materials discovery.

cond-mat.mtrl-sci↗

arXiv2024

Complex Principle Kurtosis Analysis

Independent component analysis (ICA) is a fundamental problem in the field of signal processing, and numerous algorithms have been developed to address this issue. The core principle of these algorithms is to find a transformation matrix that maximizes the non-Gaussianity of the separated signals. Most algorithms typically assume that the source signals are mutually independent (orthogonal to each other), thereby imposing an orthogonal constraint on the transformation matrix. However, this assumption is not always valid in practical scenarios, where the orthogonal constraint can lead to inaccurate results. Recently, tensor-based algorithms have attracted much attention due to their ability to reduce computational complexity and enhance separation performance. In these algorithms, ICA is reformulated as an eigenpair problem of a statistical tensor. Importantly, the eigenpairs of a tensor are not inherently orthogonal, making tensor-based algorithms more suitable for nonorthogonal cases. Despite this advantage, finding exact solutions to the tensor's eigenpair problem remains a challenging task. In this paper, we introduce a non-zero volume constraint and a Riemannian gradient-based algorithm to solve the tensor's eigenpair problem. The proposed algorithm can find exact solutions under nonorthogonal conditions, making it more effective for separating nonorthogonal sources. Additionally, existing tensor-based algorithms typically rely on third-order statistics and are limited to real-valued data. To overcome this limitation, we extend tensor-based algorithms to the complex domain by constructing a fourth-order statistical tensor. Experiments conducted on both synthetic and real-world datasets demonstrate the effectiveness of the proposed algorithm.

eess.SP↗

arXiv2017

Unconventional Localization Prior to Wrinkles and Controllable Surface Patterns of Film Substrate Bilayers Through Patterned Defects in Substrate

A novel bilayer is introduced, consisting of a stiff film adhered to a soft substrate with patterned holes beneath the film and substrate interface. To uncover the transition of surface patterns, two dimensional plane strain simulations are performed on the defected bilayer subjected to uniaxial compression. Although the substrate is considered as the linear elastic material, the presence of defects can directly trigger the formation of locally ridged and then folding configurations from flat surface with a relatively small compressive strain. It is followed by the coexisting phases of folds and wrinkles under further overall compression. This phase transition reverses the traditional transition of wrinkle to ridge or fold for defect free substrates. It is also found that the onset of initial bifurcation is highly dependent on the spatial configuration and geometries of holes, since the interaction of defects allows more strain relief mechanisms beyond wrinkling. Furthermore, a rich diversity of periodic surface topologies, including overall waves, localizations, saw like and coexisting features of folds and wrinkles can be obtained by varying the diameter, depth and spacing of holes as well as compressive strain, which provides a potential approach to engineer various surface patterns for applications.

cond-mat.soft↗

arXiv2016

Architectures of Soft Robotic Locomotion Enabled by Simple Mechanical Principles

In nature, a variety of limbless locomotion patterns flourish from the small or basic life form (Escherichia coli, the amoeba, etc.) to the large or intelligent creatures (e.g., slugs, starfishes, earthworms, octopuses, jellyfishes, and snakes). Many bioinspired soft robots based on locomotion have been developed in the past decades. In this work, based on the kinematics and dynamics of two representative locomotion modes (i.e., worm-like crawling and snake-like slithering), we propose a broad set of innovative designs for soft mobile robots through simple mechanical principles. Inspired by and go beyond existing biological systems, these designs include 1-D (dimensional), 2-D, and 3-D robotic locomotion patterns enabled by simple actuation of continuous beams. We report herein over 20 locomotion modes achieving various locomotion functions, including crawling, rising, running, creeping, squirming, slithering, swimming, jumping, turning, turning over, helix rolling, wheeling, etc. Some of them are able to reach high speed, high efficiency, and overcome obstacles. All these locomotion strategies and functions can be integrated into a simple beam model. The proposed simple and robust models are adaptive for severe and complex environments. These elegant designs for diverse robotic locomotion patterns are expected to underpin future deployments of soft robots and to inspire series of advanced designs.

cond-mat.soft↗

arXiv2014

Exploiting Delay Correlation for Multi-Antenna-Assisted High Speed Train Communications

In High Speed Train Communications (HSTC), the most challenging issue is coping with the extremely fast fading channel. Compared with its static counterpart, channel estimation on the move consumes excessive energy and spectrum to achieve similar performance. To address this issue, we exploit the delay correlation inherent in the linear spatial-temporal structure of multi-antenna array, based on which the rapid fading channel may be approximated by a virtual slow-fading channel. Subsequently, error probability and spectral efficiency are re-examined for this staticized channel. In particular, we formulate the quantitative tradeoff between the two metrics of interest, by adjusting the pilot percentage in each frame. Numerical results verify the good performance of the proposed scheme and elucidate the tradeoff.

cs.IT↗