arXiv subjects

Wancheng Zhang

Publications and source records attributed to Wancheng Zhang.

5 recordsLinked to original sources

arXiv2026

Anisotropic Spin Polarization and magnetic spin hall effect in Ferromagnets

Spin-dependent transport in ferromagnets underpins the development of high-density spintronic memories. Spin-dependent transport in strong spin-orbit-coupled ferromagnets exhibits a significant anisotropy. Both the overall spin polarization during charge transport and the magnetic spin Hall conductivity are found to exhibit pronounced anisotropy when the magnetization is tilted away from the crystallographic easy axis or when the electric field is rotated relative to the crystal axes. These anisotropic responses originate primarily from spin-orbit coupling, which is identified as the key driver of the large anisotropy observed in ferromagnet. Furthermore, strain tunability of the magnetic spin Hall anisotropy is demonstrated, with tensile strain progressively enhancing the oscillatory amplitude of the spin Hall conductivity. These findings establish strong spin-orbit-coupled ferromagnets as a platform for anisotropic spin-current generation and field-free spintronic devices that exploit intrinsic material anisotropy for improved performance and energy efficiency.

cond-mat.mes-hall↗

arXiv2026

Nearly Complete Charge--Spin Conversion via Strain-Eliminated Fermi Pockets in $d$-Wave Altermagnets

Ideal $d$-wave altermagnets with nearly orthogonal flat Fermi surfaces enable complete spin-channel separation and 100% theoretical charge--spin conversion efficiency (CSE). The metallic altermagnet $\mathrm{KV_2Se_2O}$ exemplifies this, but realistic samples host residual elliptical Fermi pockets that enhance charge conductivity while suppressing spin conductivity, drastically reducing CSE. Here we show that in-plane equibiaxial tensile strain systematically eliminates these parasitic pockets, restoring the flat-band geometry. Our first-principles calculations reveal that CSE increases monotonically with strain, reaching a record $\sim$96% at 4% strain. An effective tight-binding model confirms that pocket suppression, governed by reduced next-nearest-neighbor hoppings, is the dominant mechanism. We further identify an unconventional out-of-plane spin current component with CSE $\sim$55% at optimal orientations, enabling field-free perpendicular magnetization switching. Moreover, the same strain-driven removal of parasitic pockets yields giant TMR enhancement in $\mathrm{KV_2Se_2O}$-based magnetic tunnel junctions, from $10^{5}%$ to $10^{9}%$, and the giant TMR persists over a wide energy window near the Fermi level. These findings establish strain engineering as a clean, widely applicable strategy to maximize CSE and magnetoresistance in $d$-wave altermagnets, and provide predictive descriptors for screening high-efficiency spintronic materials.

cond-mat.mtrl-sci↗

arXiv2025

High-Efficiency Nonrelativistic Charge-Spin Conversion in X-Type Antiferromagnets

Antiferromagnetic materials with spin splitting have attracted considerable attention for their symmetry-enabled anisotropic spin textures that sustain a zero net magnetization, thereby facilitating efficient spin-current generation. In this work, the highly efficient generation of nonrelativistic spin currents is demonstrated to be facilitated by the distinctive Fermi surface geometry of X-type collinear antiferromagnets. As a prototype conducting X-type antiferromagnet, the Fermi surface of $\beta-\mathrm{Fe_2PO_5}$ exhibits a distinct $d$-wave altermagnetic characteristic, which compresses into a nearly X-shaped configuration. This results in highly efficient spin currents, achieving a charge-spin conversion efficiency of up to 90\%. Moreover, the spin current polarization is controlled by the orientation of the N\'eel vector. When the N\'eel vector tilts to the out-of-plane direction, an in-plane injected charge current can generate a special spin current component with both spin polarization and propagation along the out-of-plane direction, whose charge-spin conversion efficiency substantially exceeds that of known ferromagnets, altermagnets, noncollinear antiferromagnets, and low-symmetry materials. The highly efficient charge-spin conversion in X-type antiferromagnets provides a novel and highly effective spin source system for the development of low-power spintronic devices.

cond-mat.mtrl-sci↗

arXiv2025

Strain-induced nonrelativistic altermagnetic spin splitting effect

Recent studies reveal that $\mathcal{T}$-odd spin currents generated via the nonrelativistic altermagnetic spin splitting effect (ASSE) exhibit significant potential for spintronics applications, with both computational and experimental validations. Addressing the scarcity of conductive altermagnets, we propose strain engineering as a reliable method for inducing altermagnetism. Focusing on rutile-structured $\mathrm{OsO}_2$, first-principles calculations show that minor equibiaxial tensile strain ($\mathcal{E}_{\mathrm{ts}}$=3\%) induces nonmagnetic-to-altermagnetic transitions, achieving an ASSE-driven spin-charge conversion ratio ($θ_{\text{AS}}$) of $\sim$7\% -- far surpassing conventional spin Hall angles ($θ_{\text{IS}}$). Calculations reveal that substantial $θ_{\text{AS}}$ persists even in the absence of spin-orbit coupling, with its magnitude positively correlating to nonrelativistic spin splitting magnitude, which further confirms the strain-induced ASSE's nonrelativistic origin. Further investigation reveals that $\mathrm{RuO}_2$ exhibits analogous phenomena, which may resolve recent controversies regarding its magnetic properties. Our research opens new simple pathways for developing next-generation altermagnetic spintronic devices.

cond-mat.mtrl-sci↗

arXiv2023

Feature Space Renormalization for Semi-supervised Learning

Semi-supervised learning (SSL) has been proven to be a powerful method for leveraging unlabeled data to alleviate models'dependence on large labeled datasets. The common framework among recent approaches is to train the model on a large amount of unlabeled data with consistency regularization to constrain the model predictions to be invariant to input perturbation. This paper proposes a feature space renormalizati-on (FSR) mechanism for SSL, which imposes consistency on feature representations rather than on labels to enable the model to learn better discriminative features. In order to apply this mechanism to SSL, we design a dual-branch FSR module consisting of a dual-branch header and an FSR block. This module can be seamlessly plugged and played into existing SSL frameworks to enhance the performance of the base SSL. The experimental results show that our proposed FSR module helps the base SSL framework (e.g. CRMatch and FreeMatch), achieve better performance on a variety of standard SSL benchmark datasets, without incurring additional overhead in terms of computation time and GPU memory.

cs.LG↗