arXiv subjects

Yizhi Hu

Publications and source records attributed to Yizhi Hu.

4 recordsLinked to original sources

arXiv2026

Spin-Valley-Mismatched Altermagnet for Giant Tunneling Magnetoresistance

Altermagnet-based heterojunctions have demonstrated magnetoresistive effects in experiments, however, a predictive theoretical model for non-ferromagnetic structures has remained elusive. In this work, we develop a tunneling-based spin-transport theory that explicitly incorporates the transverse-wavevector ($\bf{k}_\|$)-dependent spin polarization of an altermagnet's transport channels, enabling the prediction of giant tunneling magnetoresistance (TMR). Based on the theory, we predict that the altermagnet KV$_2$Se$_2$O can reach the extreme limit of magnetoresistance. By performing first-principles transport calculations, we verify that magnetic tunnel junctions using the metallic KV$_2$Se$_2$O as the electrodes and few-layer MgO as the spacer exhibit zero-bias magnetoresistance larger than $7.57\times10^7$\%, which is robust against the bias and thickness of the spacer. Our research provides a quantitative design principle for next-generation spin-electronic devices and establishes KV$_2$Se$_2$O/MgO/KV$_2$Se$_2$O as a leading candidate material system for room-temperature ultra-high-density non-volatile memory.

cond-mat.mtrl-sci↗

arXiv2026

Ultralow radiative heat flux by Anderson localization in quasiperiodic plasmonic chains

Anderson localization, arising from wave interference in disordered systems, profoundly hinders energy transport, yet its impact on radiative heat flux in many-body thermophotonic systems remains unclear. Here, we demonstrate a three-order-of-magnitude suppression of radiative heat transfer, resulting in ultralow radiative heat transfer, in a one-dimensional quasiperiodic chain of plasmonic nanoparticles. This suppression in radiative heat transfer is directly correlated with mode localization, as revealed by the mode decomposition of the transmission coefficient, which serves as evidence of Anderson localization. Furthermore, we elucidate the dependence of radiative thermal conductance reduction on interparticle spacing and material damping rates, uncovering the interplay between intrinsic Ohmic losses, mode localization, and long-range many-body interactions. Our findings advance the understanding of wave-mediated thermal transport in disordered photonic structures and suggest strategies for tailoring nanoscale heat management via engineered disorder.

physics.optics↗

arXiv2025

Giant tunneling magnetoresistance based on spin-valley-mismatched ferromagnetic metals

Half metals, which are amenable to perfect spin filtering, can be utilized for high-magnetoresistive devices. However, available half metals are very limited. Here, we demonstrate that materials with intrinsic spin-valley-mismatched (SVM) states can be used to block charge transport, resembling half metals and leading to giant tunneling magnetoresistance. As an example, by using first-principles transport calculations, we show that ferromagnetic 1\emph{T}-VSe$_2$, 1\emph{T}-VS$_2$, and 2\emph{H}-VS$_2$ are such spin-valley-mismatched metals, and giant magnetoresistance of more than 99\% can be realized in spin-valve van der Waals (vdW) junctions using these metals as electrodes. Owing to the intrinsic mismatch of spin states, the central-layer materials for the vdW junctions can be arbitrary nonmagnetic materials, in principle. Our research provides clear physical insights into the mechanism for high magnetoresistance and opens new avenues for the search and design of high-magnetoresistance devices.

cond-mat.mtrl-sci↗

arXiv2025

ReMeREC: Relation-aware and Multi-entity Referring Expression Comprehension

Referring Expression Comprehension (REC) aims to localize specified entities or regions in an image based on natural language descriptions. While existing methods handle single-entity localization, they often ignore complex inter-entity relationships in multi-entity scenes, limiting their accuracy and reliability. Additionally, the lack of high-quality datasets with fine-grained, paired image-text-relation annotations hinders further progress. To address this challenge, we first construct a relation-aware, multi-entity REC dataset called ReMeX, which includes detailed relationship and textual annotations. We then propose ReMeREC, a novel framework that jointly leverages visual and textual cues to localize multiple entities while modeling their inter-relations. To address the semantic ambiguity caused by implicit entity boundaries in language, we introduce the Text-adaptive Multi-entity Perceptron (TMP), which dynamically infers both the quantity and span of entities from fine-grained textual cues, producing distinctive representations. Additionally, our Entity Inter-relationship Reasoner (EIR) enhances relational reasoning and global scene understanding. To further improve language comprehension for fine-grained prompts, we also construct a small-scale auxiliary dataset, EntityText, generated using large language models. Experiments on four benchmark datasets show that ReMeREC achieves state-of-the-art performance in multi-entity grounding and relation prediction, outperforming existing approaches by a large margin.

cs.CV↗