arXiv subjects

Bo Gu

Publications and source records attributed to Bo Gu.

At least 19 recordsLinked to original sources

arXiv2026

SA-RA-JSCC: SNR-Adaptive and Semantic-Rate-Aware Joint Source-Channel Coding

In joint source-channel coding (JSCC)-based semantic communication systems, achieving stable and reliable image semantic transmission under channel constraints remains a key challenge. In most channel adaptation modules, the signal-to-noise ratio (SNR) is often injected into each layer of a channel-adaptation model in an independent and layer-wise manner, which undermines global coordination across layers. Therefore, consistent noise-robust representations may fail to be learned throughout the model. To address this problem, we propose SA-RA-JSCC, a novel channel-adaptive JSCC model. SA-RA-JSCC maps SNR into a unified semantic vector in the feature space and then applies a one-shot global reweighting to the encoded features, thereby enabling globally consistent and learnable channel adaptation. Moreover, in order to further enhance the anti-channel capability of semantic information, a semantic-rate-aware module is introduced, enabling the adaptive policy to respond simultaneously to fluctuations in channel quality and changes in semantic-rate constraints, thereby enhancing global network coordination and channel adaptivity. Extensive experiment results across multiple channels and datasets demonstrate that SA-RA-JSCC significantly outperforms existing semantic communication models in terms of reconstruction metrics such as PSNR and MS-SSIM, exhibiting stronger robustness across a broad range of SNR regimes.

cs.IT↗

arXiv2026

SpaceMind++: Toward Allocentric Cognitive Maps for Spatially Grounded Video MLLMs

Recent multimodal large language models (MLLMs) have made remarkable progress in visual understanding and language-based reasoning, yet they lack a persistent world-centered representation for spatially consistent reasoning in 3D environments. Inspired by the mammalian dual-stream system, where semantic and spatial cues are processed separately and integrated into an allocentric cognitive map, we propose SpaceMind++, a video MLLM architecture that explicitly builds a voxelized cognitive map from RGB videos. This map reorganizes fragmented egocentric observations into a shared 3D metric representation, enabling the model to preserve object permanence and spatial topology across changing viewpoints. To make this allocentric representation usable by a pretrained video MLLM without disrupting its native visual-token interface, we introduce Coordinate-Guided Deep Iterative Fusion, a new mechanism that relays map-level spatial knowledge back into the original 2D visual features. This fusion is explicitly guided by coordinate embeddings and 3D Rotary Positional Encoding, which ground semantic interactions in metric 3D space, resembling the entorhinal binding of sensory features to metric space. Extensive experiments show that SpaceMind++ achieves new state-of-the-art performance on VSI-Bench. Furthermore, it demonstrates superior out-of-distribution generalization on SPBench, SITE-Bench, and SPAR-Bench, underscoring its robustness in unseen 3D environments.

cs.CV↗

arXiv2026

Digital Twin Enabled Simultaneous Learning and Modeling for UAV-assisted Secure Communications with Eavesdropping Attacks

This paper focuses on secure communications in UAV-assisted wireless networks, which comprise multiple legitimate UAVs (LE-UAVs) and an intelligent eavesdropping UAV (EA-UAV). The intelligent EA-UAV can observe the LE-UAVs'transmission strategies and adaptively adjust its trajectory to maximize information interception. To counter this threat, we propose a mode-switching scheme that enables LE-UAVs to dynamically switch between the data transmission and jamming modes, thereby balancing data collection efficiency and communication security. However, acquiring full global network state information for LE-UAVs' decision-making incurs significant overhead, as the network state is highly dynamic and time-varying. To address this challenge, we propose a digital twin-enabled simultaneous learning and modeling (DT-SLAM) framework that allows LE-UAVs to learn policies efficiently within the DT, thereby avoiding frequent interactions with the real environment. To capture the competitive relationship between the EA-UAV and the LE-UAVs, we model their interactions as a multi-stage Stackelberg game and jointly optimize the GUs' transmission control, UAVs' trajectory planning, mode selection, and network formation to maximize overall secure throughput. Considering potential model mismatch between the DT and the real environment, we propose a robust proximal policy optimization (RPPO) algorithm that encourages LE-UAVs to explore service regions with higher uncertainty. Numerical results demonstrate that the proposed DT-SLAM framework effectively supports the learning process. Meanwhile, the RPPO algorithm converges about 12% faster and the secure throughput can be increased by 8.6% compared to benchmark methods.

cs.NI↗

arXiv2026

Generative Artificial Intelligence Assisted Multi-modal Semantic Extraction for NOMA-based Image Transmissions

In this paper, we investigate a generative artificial intelligence (GAI)-assisted semantic communication framework for non-orthogonal multiple access (NOMA)-based image transmissions. Semantic users (SUs) extract cross-modal semantic features from the raw images, which are then used for image recovery by leveraging a GAI model. The GAI enhances the generalization and recovery of semantic image transmissions, while NOMA efficiently allocates transmission capacities to SUs based on their traffic demands. Thus, the semantic extraction and transmission control jointly affect both semantic recovery performance and transmission overhead. We maximize a weighted performance of transmission latency and semantic recovery accuracy by jointly optimizing the semantic feature selection at the semantic level, as well as the receive beamforming and NOMA decoding order at the transmission level. To reduce potential redundancy in semantic features and improve optimization efficiency, we develop an importance-aware and model-driven proximal policy optimization (IM-PPO) framework. Specifically, we quantify and retain high-importance semantic features to enhance the learning efficiency of PPO, while model-based optimization methods are used to adapt the transmission control variables. Numerical results validate that the joint adjustment of the semantic feature selection and the transmission control significantly improves the semantic recovery accuracy and the transmission latency performance. Moreover, the IM-PPO framework effectively leverages the model information to improve the learning efficiency compared to benchmark methods.

cs.NI↗

arXiv2026

DRL-driven Online Optimization for Joint Traffic Reshaping and Channel Reconfiguration in RIS-assisted Semantic NOMA Communications

This paper explores a reconfigurable intelligent surface (RIS)-assisted and semantic-aware wireless network, where multiple semantic users (SUs) transmit semantic information to an access point (AP) using the non-orthogonal multiple access (NOMA) method. The RIS reconfigures channel conditions, while semantic extraction reshapes traffic demands, providing enhanced control flexibility for NOMA transmissions. To enable efficient long-term resource allocation, we propose a deferrable semantic extraction scheme that can distribute the semantic extraction tasks across multiple time slots. We formulate a long-term energy efficiency maximization problem by jointly optimizing the RIS's passive beamforming, the SUs' semantic extraction, and the NOMA decoding order. Note that this problem involves multiple and coupled control variables, which can incur significant computational overhead in time-varying network environments. To support low-complexity online optimization, a deep reinforcement learning (DRL)-driven online optimization framework is developed. Specifically, the DRL module facilitates the adaptive selection and optimization of the most suitable option from traffic reshaping, channel reconfiguration, or NOMA decoding order assignment based on the dynamic network status. Numerical results demonstrate that the deferrable semantic extraction scheme significantly improves the long-term energy efficiency. Meanwhile, the DRL-driven online optimization framework effectively reduces the running time while maintaining superior learning performance compared to state-of-the-art methods.

cs.NI↗

arXiv2026

Learning to Optimize Joint Source and RIS-assisted Channel Encoding for Multi-User Semantic Communication Systems

In this paper, we explore a joint source and reconfigurable intelligent surface (RIS)-assisted channel encoding (JSRE) framework for multi-user semantic communications, where a deep neural network (DNN) extracts semantic features for all users and the RIS provides channel orthogonality, enabling a unified semantic encoding-decoding design. We aim to maximize the overall energy efficiency of semantic communications across all users by jointly optimizing the user scheduling, the RIS's phase shifts, and the semantic compression ratio. Although this joint optimization problem can be addressed using conventional deep reinforcement learning (DRL) methods, evaluating semantic similarity typically relies on extensive real environment interactions, which can incur heavy computational overhead during training. To address this challenge, we propose a truncated DRL (T-DRL) framework, where a DNN-based semantic similarity estimator is developed to rapidly estimate the similarity score. Moreover, the user scheduling strategy is tightly coupled with the semantic model configuration. To exploit this relationship, we further propose a semantic model caching mechanism that stores and reuses fine-tuned semantic models corresponding to different scheduling decisions. A Transformer-based actor network is employed within the DRL framework to dynamically generate action space conditioned on the current caching state. This avoids redundant retraining and further accelerates the convergence of the learning process. Numerical results demonstrate that the proposed JSRE framework significantly improves the system energy efficiency compared with the baseline methods. By training fewer semantic models, the proposed T-DRL framework significantly enhances the learning efficiency.

cs.NI↗

arXiv2025

Spin PN Junctions: Giant Magnetoresistance, Tunable Circular Polarization, and Spin Zener Filter

We demonstrate that spin PN junctions-magnetic semiconductor homojunctions with spin splitting-induced band offsets-fundamentally redefine carrier transport via spin-dependent recom bination probabilities. By integrating this mechanism into the Shockley model, we predict a near 100 enhancement in magnetoresistance sensitivity under small forward bias, where exponen tial modulation of recombination lifetimes by magnetic fields amplifies resistance changes. Angular momentum conservation enables magnetically tunable circularly polarized luminescence: exclusive conduction-band or valence-band splitting in both neutral regions achieves near-half po larization, while global splitting degrades emission coherence. Furthermore, we propose a "spin Zener filter" exploiting 1eV valence band splitting in (Ga, Mn)As, where spin-dependent barrier heights generate near 100% spin-polarized tunneling currents within a voltage-selective win dow. These results establish spin PN junctions as a universal design paradigm for magnetically amplified electronics, polarization-programmable optoelectronics, and voltage-gated spin injection without ferromagnetic contacts.

cond-mat.mtrl-sci↗

arXiv2025

Bilateral hydrogenation induced high-Chern-number quantum anomalous Hall state in monolayer Cr$_2$Ge$_2$Te$_6$

The pursuit of high-temperature quantum anomalous Hall (QAH) insulators faces fundamental challenges, including narrow topological gaps and low Curie temperatures ($T_C$) in existing materials. Here, we propose a strategy using bilateral hydrogenation to engineer a robust QAH state in the topologically trivial ferromagnetic semiconductor Cr$_2$Ge$_2$Te$_6$ via covalent orbital reconstruction. First-principles calculations reveal that by rewiring the orbital hybridization network, hydrogenation alters orbital occupations to shift preexisting Dirac points, originally embedded in the conduction bands, to the vicinity of the Fermi level in Cr$_2$Ge$_2$Te$_6$H$_6$. This electronic restructuring, coupled with spin-orbit coupling, opens a global topological gap of 118.1 meV, establishing a robust QAH state with Chern number $C=3$. Concurrently, this orbital reconstruction tunes the energy difference between the ligand $p$ and transition metal $d$ orbitals. This shift enhances ferromagnetic superexchange via the $d{z^2}-p_z-d_{xz}$ channel, strengthening the nearest-neighbor coupling $J_1$ by 3.06 times and switching $J_2$ from antiferromagnetic to ferromagnetic. Monte Carlo simulations based on extracted exchange parameters indicate a pronounced enhancement of ferromagnetic stability compared with pristine Cr$_2$Ge$_2$Te$_6$. While absolute Curie temperatures depend on the mapping to an effective spin model and represent relative trends, the enhanced stability after hydrogenation is a salient effect. This work establishes targeted orbital reconstruction driven by surface hydrogenation as a powerful route to simultaneously control topology and magnetism in 2D materials, providing a general route to engineer QAH phases with large gaps and high Chern numbers in van der Waals ferromagnetic semiconductors.

cond-mat.mes-hall↗

arXiv2025

Diameter-Controlled High-Order Vortex States and Magnon Hybridization in VSe2 Nanotubes

Curved magnets offer a rich phase diagram and hold great promise for next-generation spintronic technologies. This study establishes the paramount significance of high-order vortex states (e.g., 3$\varphi$ with winding number $n$ > 1) in VSe2 nanotubes, which uniquely enable magnonic functionalities fundamentally inaccessible to conventional magnetic systems. These states arise from diameter-dependent competition between the nearest-neighbor ferromagnetic ($J_1$) and longer-range antiferromagnetic ($J_2$/$J_3$) couplings, as rigorously validated through density-functional theory calculations and Heisenberg modeling of phase diagrams. Critically, by the Landau-Lifshitz-Gilbert equation, we find that high-order vortex configurations unlock an intrinsic hybridization mechanism governed by strict orbital angular momentum (OAM) selection rules ($\Delta l = \pm 2(n-1)$) -- a process strictly forbidden in fundamental vortices ($n$ = 1) -- generating complex high-OAM magnons with measurable topological charge. This is vividly demonstrated in the 3$\varphi$ state, where hybridization between $l$ = -4, 0 and 4 modes produces eight-petal magnon density patterns. Such states provide an essential platform-free solution for generating high-OAM magnons, wchich is crucial for spin-wave-based information transport. These findings establish a predictive theoretical framework for controlling high-order vortex states in curved magnets and highlight VSe2 nanotubes as a promising platform for exploring complex magnetism and developing future magnonic and spintronic devices.

cond-mat.mtrl-sci↗

arXiv2025

Intercalation-Induced Near Room-Temperature Ferromagnetism in CrI3 via Synergistic Exchange Pathways

The development of room-temperature magnetic semiconductors is critical for advancing spintronic technologies, yet van der Waals magnets like CrI3 exhibit intrinsically low Curie temperatures (Tc = 45 K). This study employs first-principles calculations to demonstrate that atom intercalation, particularly lithium (Li), dramatically enhances magnetic exchange couplings in CrI3, achieving near room-temperature ferromagnetism with a predicted Tc of 286 K-aligning with experimental reports of 420 K. The underlying mechanism involves synergistic superexchange and double-exchange interactions: intercalation reduces the |Ep-Ed| energy difference between iodine p-orbitals and chromium d-orbitals, strengthening superexchange pathways, while charge transfer induces valence mixing (e.g., Cr3+ to Cr2+, as confirmed by experimental X-ray photoelectron spectrometry data), promoting double-exchange. Theoretical predictions extend to other intercalants including Cu and Na, with Cu0.25CrI3 and Na0.25CrI3 exhibiting Tc of 267 K and 247 K, respectively, establishing a versatile strategy for designing high-Tc magnetic semiconductors. This work bridges theoretical insights with experimental validation, offering a transferable framework for intercalation-driven material design and accelerating practical spintronic device realization.

cond-mat.mtrl-sci↗

arXiv2025

Joint Traffic Reshaping and Channel Reconfiguration in RIS-assisted Semantic NOMA Communications

In this paper, we consider a semantic-aware reconfigurable intelligent surface (RIS)-assisted wireless network, where multiple semantic users (SUs) simultaneously transmit semantic information to an access point (AP) by using the non-orthogonal multiple access (NOMA) method. The SUs can reshape their traffic demands by modifying the semantic extraction factor, while the RIS can reconfigure the channel conditions via the passive beamforming. This provides the AP with greater flexibility to decode the superimposed signals from the SUs. We aim to minimize the system's overall energy consumption, while ensuring that each SU's traffic demand is satisfied. Hence, we formulate a joint optimization problem of the SUs' decoding order and semantic control, as well as the RIS's passive beamforming strategy. This problem is intractable due to the complicated coupling in constraints. To solve this, we decompose the original problem into two subproblems and solve them by using a series of approximate methods. Numerical results show that the joint traffic reshaping and channel reconfiguration scheme significantly improves the energy saving performance of the NOMA transmissions compared to the benchmark methods.

cs.NI↗

arXiv2025

Ferroelectric control of bipolar magnetic semiconductor with room Curie temperature

The development of room-temperature tunable magnetic semiconductors is crucial for the advancement of low-power, high-performance information technologies. Using density functional theory calculations, we propose a series of two-dimensional magnetic semiconductors with critical temperature above room temperature, including three ferromagnetic and two antiferromagnetic semiconductors.Their stability is confirmed through phonon spectra, molecular dynamics simulations, and formation energy calculations. In particular, we demonstrate a ferromagnetic bipolar magnetic semiconductor (BMS), Cr2NiSe4, formed via Ni intercalation into bilayer CrSe2, which exhibits a 0.40 eV band gap and a Curie temperature of 352 K. Nonvolatile carrier spin polarization control in Cr2NiSe4 is achieved by switching the ferroelectric polarization of an Al2Se3 substrate. Switching the ferroelectric state of monolayer Al2Se3 induces a BMS-to-half-metal transition. Reversing the polarization of bilayer Al2Se3 yields a half-metallic Cr2NiSe4 with fully opposite carrier spin polarization. Furthermore, we propose a multiferroic nonvolatile memory design: write operations are controlled by the ferroelectric polarization state of bilayer Al2Se3, while read operations rely on detecting the distinct carrier spin polarizations of Cr2NiSe4. Our work reports a two dimensional BMS with Curie temperature above room temperature and presents a feasible strategy for its nonvolatile electrical control.

cond-mat.mtrl-sci↗

arXiv2025

Unifying Strain-driven and Pressure-driven Superconductivity in La$_{3}$Ni$_{2}$O$_{7}$: Suppressed charge/spin density waves and enhanced interlayer coupling

Recent strain-stabilized superconductivity at ambient pressure in La$_3$Ni$_2$O$_{7}$ films opens new avenues for nickelates research, in parallel with its pressure-induced counterpart. Using density functional theory calculations, we elucidate the critical factors bridging strain- and pressure-driven superconductivity in La$_3$Ni$_2$O$_{7}$ by comprehensively analyzing structural, electronic, magnetic, and density wave characteristics. Consistent with recent scanning transmission electron microscopy observations, we find an $I4/mmm$ structural transition at $-0.9\%$ strain, preceding superconductivity onset. Electronic analysis shows compressive strain lowers Ni-$d_{z^2}$ orbital energy levels, while interfacial Sr diffusion effectively reconstructs the $d_{z^2}$ pockets, quantitatively matching angle-resolved photoemission spectroscopy experiments. The interlayer antiferromagnetic coupling $J_\perp$ under pressure or strain closely tracks experimental superconducting $T_c$ variation. The dome-shaped pressure dependence and monotonic strain dependence of $J_\perp$ mainly arise from modulations in the apical oxygen $p_z$ energy levels. Moreover, compressive strain suppresses both charge density waves (CDW) and spin density waves (SDW) instabilities analogous to pressure effects, with SDW vanishing concurrently with the structural transition and CDW disappearing at $\sim-3.3\%$ strain. Our results indicate that suppressed density waves and enhanced $J_\perp$ are crucial for both strain- and pressure-driven superconductivity. Accordingly, we propose several candidate substrates capable of achieving greater compressive strain, thereby potentially increasing $T_c$.

cond-mat.supr-con↗

arXiv2025

Learning Joint Source-Channel Encoding in IRS-assisted Multi-User Semantic Communications

In this paper, we investigate a joint source-channel encoding (JSCE) scheme in an intelligent reflecting surface (IRS)-assisted multi-user semantic communication system. Semantic encoding not only compresses redundant information, but also enhances information orthogonality in a semantic feature space. Meanwhile, the IRS can adjust the spatial orthogonality, enabling concurrent multi-user semantic communication in densely deployed wireless networks to improve spectrum efficiency. We aim to maximize the users' semantic throughput by jointly optimizing the users' scheduling, the IRS's passive beamforming, and the semantic encoding strategies. To tackle this non-convex problem, we propose an explainable deep neural network-driven deep reinforcement learning (XD-DRL) framework. Specifically, we employ a deep neural network (DNN) to serve as a joint source-channel semantic encoder, enabling transmitters to extract semantic features from raw images. By leveraging structural similarity, we assign some DNN weight coefficients as the IRS's phase shifts, allowing simultaneous optimization of IRS's passive beamforming and DNN training. Given the IRS's passive beamforming and semantic encoding strategies, user scheduling is optimized using the DRL method. Numerical results validate that our JSCE scheme achieves superior semantic throughput compared to the conventional schemes and efficiently reduces the semantic encoder's mode size in multi-user scenarios.

eess.SP↗

arXiv2025

Enhancement of temperature of quantum anomalous Hall effect in two-dimensional germanene/magnetic semiconductor heterostructures

Quantum anomalous Hall effect (QAHE) is significant for future low-power electronics devices, where a main challenge is realizing QAHE at high temperatures. In this work, based on experimentally reported two-dimensional (2D) germanene and magnetic semiconductors Cr$_2$Ge$_2$Te$_6$ and Cr$_2$Si$_2$Te$_6$, and the first principle calculations, germanene/magnetic semiconductor heterostructures are investigated. Topologically nontrivial edge states and quantized anomalous Hall conductance are demonstrated. It is shown that the QAHE temperature can be enhanced to approximately 62 K in germanene/monolayer (ML) Cr$_2$Ge$_2$Te$_6$ with 2.1\% tensile strain, 64 K in germanene/bilayer (BL) Cr$_2$Ge$_2$Te$_6$ with 1.4\% tensile strain, and 50 K in germanene/ML Cr$_2$Si$_2$Te$_6$ with 1.3\% tensile strain. With increasing tensile strain of these heterostructures, the band gap decreases and the Curie temperature rises, and the highest temperature of QAHE is obtained. Since these 2D materials were discovered in recent experiments, our results provide promising materials for achieving high-temperature QAHE.

cond-mat.mes-hall↗

arXiv2025

Ferromagnetic Semiconductor Nanotubes with Room Curie Temperatures

Realizing ferromagnetic semiconductors with room Curie temperature $T\rm_C$ remains a challenge in spintronics. Inspired by the recent experimental progress on the nanotubes based on 2D van der Waals non-magnetic transition-metal dichalcogenides, magnetic nanotubes based on monolayer ferromagnetic materials are highly possible. Here, we proposed a way how to obtain high $T\rm_C$ ferromagnetic semiconductor nanotubes. Some high $T\rm_C$ ferromagnetic semiconductors are predicted in the MX$_2$ nanotubes (M = V, Cr, Mn, Fe, Co, Ni; X = S, Se, Te), including CrS$_2$ and CrTe$_2$ zigzag nanotubes with the diameter of 18 unit cells showing $T\rm_C$ above 300 K. In addition, due to the strain gradient in walls of nanotubes, an electrical polarization at level of $0.1$ eV/\AA~inward of the radial direction is obtained. Our results not only present novel ferromagnetic semiconductor nanotubes with room Curie temperature but also be indicative of how to obtain such nanotubes based on experimentally obtained 2D high $T\rm_C$ ferromagnetic metals.

cond-mat.mtrl-sci↗

arXiv2024

Exploiting NOMA Transmissions in Multi-UAV-assisted Wireless Networks: From Aerial-RIS to Mode-switching UAVs

In this paper, we consider an aerial reconfigurable intelligent surface (ARIS)-assisted wireless network, where multiple unmanned aerial vehicles (UAVs) collect data from ground users (GUs) by using the non-orthogonal multiple access (NOMA) method. The ARIS provides enhanced channel controllability to improve the NOMA transmissions and reduce the co-channel interference among UAVs. We also propose a novel dual-mode switching scheme, where each UAV equipped with both an ARIS and a radio frequency (RF) transceiver can adaptively perform passive reflection or active transmission. We aim to maximize the overall network throughput by jointly optimizing the UAVs' trajectory planning and operating modes, the ARIS's passive beamforming, and the GUs' transmission control strategies. We propose an optimization-driven hierarchical deep reinforcement learning (O-HDRL) method to decompose it into a series of subproblems. Specifically, the multi-agent deep deterministic policy gradient (MADDPG) adjusts the UAVs' trajectory planning and mode switching strategies, while the passive beamforming and transmission control strategies are tackled by the optimization methods. Numerical results reveal that the O-HDRL efficiently improves the learning stability and reward performance compared to the benchmark methods. Meanwhile, the dual-mode switching scheme is verified to achieve a higher throughput performance compared to the fixed ARIS scheme.

cs.NI↗

arXiv2024

Two-dimensional room temperature ferromagnetic semiconductors

To realize ferromagnetic semiconductors with high Curie temperature TC is still a challenge in spintronics. Recent experiments have obtained two-dimensional (2D) room temperature ferromagnetic metals, such as monolayers MnSe2 and Cr3Te6. In this paper, by the density functional theory (DFT) calculations, we proposed a way to obtain 2D high TC ferromagnetic semiconductors through element replacement in these ferromagnetic metals. High TC ferromagnetic semiconductors are predicted in the monolayers (Mn, D)Se2 and (Cr, D)3Te6, where element D is taken as vacancy, 3d, 4d and 5d transition metal elements. For the concentrations of D from 1/9 to 1/3, there are about 10 ferromagnetic semiconductors with TC above 200 K, including (Cr5/6, W1/6)3Te6 and (Cr4/6, Mo2/6)3Te6 with TC above 300 K. In addition, Mn(Se6/8, Sb2/8)2 is also predicted to be a 2D ferromagnetic semiconductor with TC above 300 K. Considering the fast developments on fabrication and manipulation of 2D materials, our theoretical results propose a way to explore the high temperature ferromagnetic semiconductors from experimentally obtained 2D high temperature ferromagnetic metals through element replacement approach.

cond-mat.mtrl-sci↗