arXiv subjects

Wenmin Li

Publications and source records attributed to Wenmin Li.

14 recordsLinked to original sources

arXiv2026

Superconducting Hydride Mg2RhH6 Experimentally Achieved at Lower Pressure

Although tremendous progress has been made in recent years in the field of polyhydride superconductors, the realization of high critical temperature superconductivity still relies on formidable high pressures. Searching for superconducting hydrides at lower pressures is of particular importance. Here we report the first experimental synthesis of the Mg2RhH6, which achieves superconductivity under a significantly reduced pressure of 30 GPa. The synthesis of Mg2RhH6 proceeds via a two step process (1) preparation of the Mg2RhH5 precursor containing hydrogen atoms stabilized by covalent bonds, followed by (2) hydrogen supplementation resulting in the filling of electrons into anti bonding orbitals above 30 GPa, which was accompanied by the structural transition from RhH5 square pyramid to RhH6 octahedron. Superconductivity is achieved at 30 GPa with a Tc of 24 K, which is further enhanced to 29 K at 53 GPa, evidenced by a sharp drop of resistivity to zero and characteristic suppression of Tc under applied magnetic fields. Our experiments prove the Mg2RhH6 superconductor to be thermodynamically stable above 30 GPa, making it the first case exhibiting a Tc of approximately 30 K at a readily accessible pressure. This study pioneers a highly promising pathway for the rational design and discovery of high temperature superconductors within phonon mediated BCS framework.

cond-mat.supr-con↗

arXiv2026

Test Case Prioritization for DNNs via Neural Collapse Instability

With the widespread deployment of deep neural networks (DNNs) in safety-critical domains, reducing the cost of model validation under limited testing budgets has become increasingly important. Existing test case prioritization techniques often rely on single-checkpoint confidence signals derived from output probabilities. However, DNNs can be confidently wrong, and the confidence margin between the predicted and competing classes is frequently small, which weakens early fault discovery. To address this limitation, we propose a Neural-Collapse-Inspired Prioritization (NCIP) framework that replaces absolute confidence with cross-checkpoint prediction variability in the terminal training regime, where model geometry becomes highly structured. NCIP introduces two key components. First, it selects an NC-guided representative subset of training checkpoints using an equiangularity score of classifier weights, quantified as the standard deviation of pairwise cosine similarities among class weight vectors. Second, it prioritizes test inputs by their prediction variability across the selected checkpoints, surfacing boundary-adjacent and failure-prone samples that are unstable under checkpoint-induced decision boundary shifts. Extensive experiments across multiple datasets and architectures show that NCIP achieves strong performance in early fault discovery compared with competitive baselines, with 1.5 to 16.6 percent RAUC-ALL gains and 4.9 to 20.6 percent RAUC-500 gains under the same testing budget. NCIP further attains the best average performance across all dataset-model pairs.

cs.LG↗

arXiv2026

Pool-Select-Refine for Allocation-Aware Generative Dataset Distillation

Diffusion-based dataset distillation has recently emerged as a promising paradigm for condensing large-scale datasets into compact synthetic sets. By leveraging pretrained generative priors, these methods can produce realistic class-conditional samples more efficiently than traditional matching-based approaches. However, most existing diffusion-based methods still adopt a rigid ``Generate-and-Use'' strategy, where the generated samples are directly treated as the final distilled set under a fixed images-per-class budget. Such a design tightly couples candidate generation with final budget allocation, which may result in redundant waste of the limited budget or insufficiently informative samples. In this paper, we propose ``Pool-Select-Refine'', a two-stage framework for allocation-aware generative dataset distillation. First, instead of directly using a fixed number of generated samples, we construct an over-complete candidate pool and select a compact subset under the target budget. Second, we refine the selected samples in latent space using soft-label supervision derived from the teacher model, improving semantic alignment while preserving the generative prior. This design explicitly decouples generation, selection, and refinement, enabling more effective use of the distillation budget. Experiments on large-scale and fine-grained image classification benchmarks show that the proposed framework delivers consistent gains over diffusion-based baselines. The results suggest that introducing a curation stage before refinement is a simple yet effective way to improve diffusion-based dataset distillation.

cs.CV↗

arXiv2026

Outstanding TC Enhancement in 5d-3d Y2NiIrO6 by Compression

Understanding and predicting the properties of 5d compounds critically depend on the identification of the superexchange interactions from which their magnetism emerges. The study of pressure effects on double perovskite Y2NiIrO6 (YNIO) provides deep insight toward this goal. At ambient pressure, YNIO is a ferrimagnetic insulator with the Ir4+-5d Jeff = 1/2 Mott-insulating state. Under physical pressure up to 17 GPa, the compound exhibits concurrent compression on Ni/Ir-O bond lengths and Ni-O-Ir bond angles, leading to an increase of the Curie temperature from 192 to 243 K. On the contrary, external pressure increases distanced Ir-Ir interaction and in turn induces magnetic frustration in Sr2IrO4/Sr3Ir2O7 due to the extended 5d orbitals. In YNIO, the rock-salt ordered Ni-Ir naturally blocks extended superexchange beyond the nearest neighbor, and in turn suppresses such magnetic frustration. Moreover, the orthogonal Ni eg-Ir t2g pathway in YNIO is robust under lattice distortion, while the superexchange is weakened by bond bending in La2NiMnO6 with a similar half-filed eg-t2g configuration. Our findings establish a framework for elucidating the mechanism of 5d-3d superexchange and guide bond-engineered magnetism in iridate-related systems.

cond-mat.str-el↗

arXiv2026

3D Unconventional Superconductivity in Bulk LaO

Lanthanum-based compounds are cornerstones of superconductivity research, yet the La-5d orbitals typically remain empty spectator states far above the Fermi level (EF). While superconductivity has been induced in LaO up to 5.37 K in tensile epitaxy films, the intrinsic ground state of the bulk phase has remained controversial mostly due to synthetic challenges, with early reports suggesting a metallic nature. Here we report the high-pressure and high-temperature synthesis of pure bulk rock-salt LaO and unveil its intrinsic type-II superconductivity with a transition temperature (TC) of ~6 K at ambient pressure. The bulk TC is further enhanced to 6.9 K in La1-xYxO at x = 0.10, where Y doping leads to lattice contraction (chemical pressing) and a remarkable increase in electron carrier concentration. Strikingly, applying physical pressure further enhances the TC to a maximum of 12.7 K at 20 GPa, the highest TC in lanthanum monochalcogenides LaX (X = S, Se, Te, and O) to date. This pressure dependence is diametrically opposed to the behavior observed in films, and occurs despite a pressure-induced reduction in the density of states at EF - a trend that sharply contradicts the conventional phonon-mediated BCS mechanism. Our first-principles calculations reveal that compressive strain modifies the crystal field splitting to enhance La-5d/O-2p hybridization, fostering a three-dimensional multi-pocket Fermi surface favorable for spin/orbital fluctuation-mediated pairing. This work clarifies the intrinsic superconductivity of bulk LaO and provides a foundation for designing new rare-earth-based superconductors with higher TC

cond-mat.supr-con↗

arXiv2025

AJF: Adaptive Jailbreak Framework Based on the Comprehension Ability of Black-Box Large Language Models

Recent advancements in adversarial jailbreak attacks have exposed critical vulnerabilities in Large Language Models (LLMs), enabling the circumvention of alignment safeguards through increasingly sophisticated prompt manipulations. Our experiments find that the effectiveness of jailbreak strategies is influenced by the comprehension ability of the target LLM. Building on this insight, we propose an Adaptive Jailbreak Framework (AJF) based on the comprehension ability of black-box large language models. Specifically, AJF first categorizes the comprehension ability of the LLM and then applies different strategies accordingly: For models with limited comprehension ability (Type-I LLMs), AJF integrates layered semantic mutations with an encryption technique (MuEn strategy), to more effectively evade the LLM's defenses during the input and inference stages. For models with strong comprehension ability (Type-II LLMs), AJF employs a more complex strategy that builds upon the MuEn strategy by adding an additional layer: inducing the LLM to generate an encrypted response. This forms a dual-end encryption scheme (MuDeEn strategy), further bypassing the LLM's defenses during the output stage. Experimental results demonstrate the effectiveness of our approach, achieving attack success rates of \textbf{98.9\%} on GPT-4o (29 May 2025 release) and \textbf{99.8\%} on GPT-4.1 (8 July 2025 release). Our work contributes to a deeper understanding of the vulnerabilities in current LLMs alignment mechanisms.

cs.CL↗

arXiv2025

Contrastive Learning-Enhanced Trajectory Matching for Small-Scale Dataset Distillation

Deploying machine learning models in resource-constrained environments, such as edge devices or rapid prototyping scenarios, increasingly demands distillation of large datasets into significantly smaller yet informative synthetic datasets. Current dataset distillation techniques, particularly Trajectory Matching methods, optimize synthetic data so that the model's training trajectory on synthetic samples mirrors that on real data. While demonstrating efficacy on medium-scale synthetic datasets, these methods fail to adequately preserve semantic richness under extreme sample scarcity. To address this limitation, we propose a novel dataset distillation method integrating contrastive learning during image synthesis. By explicitly maximizing instance-level feature discrimination, our approach produces more informative and diverse synthetic samples, even when dataset sizes are significantly constrained. Experimental results demonstrate that incorporating contrastive learning substantially enhances the performance of models trained on very small-scale synthetic datasets. This integration not only guides more effective feature representation but also significantly improves the visual fidelity of the synthesized images. Experimental results demonstrate that our method achieves notable performance improvements over existing distillation techniques, especially in scenarios with extremely limited synthetic data.

cs.CV↗

arXiv2021

Crystal structure and properties of iron-based spin-chain compound Ba9Fe3Se15

We report the synthesis of a new quasi one-dimensional (1D) iron selenide. Ba9Fe3Se15 was synthesized at high temperature and high pressure of 5.5 GPa and systematically studied via structural, magnetic and transport measurements at ambient and at high-pressures. Ba9Fe3Se15 crystallizes in a monoclinic structure and consists of face-sharing FeSe6 octahedral chains along the c axis. At ambient pressure it exhibits an insulating behavior with a band gap ~460 meV and undergoes a ferrimagnet-like phase transition at 14 K. Under high pressure, a complete metallization occurs at ~29 GPa, which is accompanied by a spin state crossover from high spin (HS) state to low spin (LS) state. The LS appears for pressures P >36 GPa.

cond-mat.supr-con↗

arXiv2020

Pressure-induced inverse order-disorder transition in double perovskites

Given the consensus that pressure improves cation order in most of known materials, a discovery of pressure-induced disorder could require reconsideration of order-disorder transition in solid state physics/chemistry and geophysics. Double perovskites Y2CoIrO6 and Y2CoRuO6 synthesized at ambient pressure show B-site order, while the polymorphs synthesized at 6 and 15 GPa are partially-ordered and disordered respectively. With the decrease of ordering degrees, the lattices are shrunken and the crystal structures alter from monoclinic to orthorhombic symmetry. Correspondingly, long-range ferrimagnetic order in the B-site ordered phases are gradually overwhelmed by B-site disorder. Theoretical calculations suggest that unusual unit cell compressions under external pressures unexpectedly stabilize the disordered phases of Y2CoIrO6 and Y2CoRuO6.

cond-mat.mtrl-sci↗

arXiv2020

Anomalous critical point behavior in dilute magnetic semiconductor (Ca,Na)(Zn,Mn)2Sb2

In this paper we report successful synthesis and magnetic properties of (Ca,Na)(Zn,Mn)2Sb2 as a new ferromagnetic dilute magnetic semiconductor (DMS). In this DMS material the concentration of magnetic moments can be controlled independently from the concentration of electric charge carriers that are required for mediating magnetic interactions between these moments. This feature allows us to separately investigate the effect of carriers and of spins on the ferromagnetic properties of this new DMS alloy, and particularly of the critical ferromagnetic behavior. We use modified Arrott plot technique to establish critical exponents b, g, and d of this alloy. We find that at low Mn concentrations (< 10 at.%), it is governed by short-range 3D-Ising behavior, with experimental values of b, g, and d very close to theoretical 3D-Ising values of 0.325, 1.24, and 4.815. However, as the Mn concentration increases, this DMS material exhibits a mixed-phase behavior, with g retaining its 3D-Ising characteristics, but b crossing over to longer-range mean-field behavior.

cond-mat.mtrl-sci↗

arXiv2020

A substantial increase of Curie temperature in a new type of diluted magnetic semiconductors via effects of chemical pressure

Chemical pressure is an effective method to tune physical properties, particularly for diluted magnetic semiconductors (DMS) of which ferromagnetic ordering is mediated by charge carriers. Via substitution of smaller Ca for larger Sr, we introduce chemical pressure on (Sr,Na)(Cd,Mn)2As2 to fabricate a new DMS material (Ca,Na)(Cd,Mn)2As2. Carriers and spins are introduced by substitutions of (Ca,Na) and (Cd,Mn) respectively. The unit cell volume reduces by 6.2% after complete substitution of Ca for Sr, suggesting a subsistent chemical pressure. Importantly the local geometry of [Cd/MnAs4] tetrahedron is optimized via chemical compression that increases the Mn-As hybridization leading to enhanced ferromagnetic interactions. As a result, the maximum Curie temperature (TC) is increased by about 50% while the the maximum saturation moment increases by over 100% from (Sr,Na)(Cd,Mn)2As2 to (Ca,Na)(Cd,Mn)2As2. The chemical pressure estimated from the equation of state is equal to an external physical pressure of 3.6 GPa.

cond-mat.mtrl-sci↗

arXiv2020

A New Quasi One-Dimensional Compound Ba3TiTe5 and Superconductivity Induced by Pressure

We report systematical studies of a new quasi-one-dimensional (1D) compound Ba3TiTe5 and the high-pressure induced superconductivity therein. Ba3TiTe5 was synthesized at high pressure and high temperature. It crystallizes into a hexagonal structure (P63/mcm), which consists of infinite face-sharing octahedral TiTe6 chains and Te chains along the c axis, exhibiting a strong 1D characteristic structure. The first-principles calculations demonstrate that Ba3TiTe5 is a well-defined 1D conductor and thus, it can be considered a starting point to explore the exotic physics induced by pressure via enhancing the interchain hopping to move the 1D conductor to a high dimensional metal. For Ba3TiTe5, high-pressure techniques were employed to study the emerging physics dependent on interchain hopping, such as the Umklapp scattering effect, spin/charge density wave (SDW/CDW), superconductivity and non-Fermi Liquid behavior. Finally, a complete phase diagram was plotted. The superconductivity emerges from 8.8 GPa, near which the Umklapp gap is mostly suppressed. Tc is enhanced and reaches the maximum ~6 K at about 36.7 GPa, where the spin/charge density wave (SDW/CDW) is completely suppressed, and a non-Fermi Liquid behavior appears. Our results suggest that the appearance of superconductivity is associated with the fluctuation due to the suppression of Umklapp gap and the enhancement of Tc is related with the fluctuation of the SDW/CDW.

cond-mat.supr-con↗

arXiv2016

Volume-wise destruction of the antiferromagnetic Mott insulating state through quantum tuning

Metal-to-insulator transitions (MITs) are a dramatic manifestation of strong electron correlations in solids1. The insulating phase can often be suppressed by quantum tuning, i.e. varying a nonthermal parameter such as chemical composi- tion or pressure, resulting in a zero-temperature quantum phase transition (QPT) to a metallic state driven by quantum fluctuations, in contrast to conventional phase transitions driven by thermal fluctuations. Theories of exotic phenomena known to occur near the Mott QPT such as quantum criticality and high-temperature superconductivity often assume a second-order QPT, but direct experimental evidence for either first- or second-order behavior at the magnetic QPT associated with the Mott transition has been scarce and further masked by the superconducting phase in unconventional superconductors. Most measurements of QPTs have been performed by volume-integrated probes, such as neutron scattering, magnetization, and transport, in which discontinuous behavior, phase separation, and spatially inhomogeneous responses are averaged and smeared out, leading at times to misidentification as continuous second-order transitions. Here, we demonstrate through muon spin relaxation/rotation (MuSR) experiments on two archetypal Mott insulating systems, composition-tuned RENiO3 (RE=rare earth element) and pressured-tuned V2O3, that the QPT from antiferromagnetic insulator to paramagnetic metal is first-order: the magnetically ordered volume fraction decreases to zero at the QPT, resulting in a broad region of intrinsic phase separation, while the ordered magnetic moment retains its full value across the phase diagram until it is suddenly destroyed at the QPT. These findings call for further investigation into the role of inelastic soft modes and the nature of dynamic spin and charge fluctuations underlying the transition.

cond-mat.str-el↗

arXiv2013

Cryptanalysis and improvement of two certificateless three-party authenticated key agreement protocols

Recently, two certificateless three-party authenticated key agreement protocols were proposed, and both protocols were claimed they can meet the desirable security properties including forward security, key compromise impersonation resistance and so on. Through cryptanalysis, we show that one neither meets forward security and key compromise impersonation resistance nor resists an attack by an adversary who knows all users' secret values, and the other cannot resist key compromise impersonation attack. Finally, we propose improved protocols to make up two original protocols' security weaknesses, respectively. Further security analysis shows that our improved protocols can remove such security weaknesses.

cs.CR↗