arXiv subjects

Haitao Yang

Publications and source records attributed to Haitao Yang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Tunable bifurcation of magnetic anisotropy and bi-oriented antiferromagnetic order in kagome metal GdTi3Bi4

The novel kagome family RTi3Bi4 (R: rare-earth) offers a unique platform for exploring distinctive physical phenomena such as anisotropy, spin density wave, and anomalous Hall effect. In particular, the magnetic frustration and behavior of magnetic anisotropy in antiferromagnetic (AFM) kagome materials are of great interest for the fundamental studies and hold promise for next-generation device applications. Here, we report a tunable bifurcation of magnetic anisotropic and bi-oriented AFM order observed in the quasi-1D kagome antiferromagnet GdTi3Bi4. The magnetic domain evolutions during two plateau transition processes are directly visualized, unveiling a pronounced in-plane anisotropy along the a-axis. Temperature-dependent characterization reveals a bifurcation transition of anisotropy at approximately 2 K, where the a-axis anisotropy splits into two special orientations, revealing a hidden bi-oriented in-plane AFM order deviating from the high-symmetry direction by 7 degree. More intriguingly, the characteristics of the bifurcated anisotropy are clearly illustrated through vector magnetic field modulation, revealing three distinct in-plane domain phases in the transverse magnetic field phase diagram. Our results not only provide valuable insights into the tunable bifurcation of magnetic anisotropic in GdTi3Bi4, but also pave a novel pathway for AFM spintronics development.

cond-mat.str-el↗

arXiv2026

Building an Affordable Self-Driving Lab: Practical Machine Learning Experiments for Physics Education Using Internet-of-Things

Machine learning (ML) is transforming modern physics research, but practical, hands-on experience with ML techniques remains limited due to cost and complexity barriers. To address this gap, we introduce an affordable, autonomous, Internet-of-Things (IoT)-enabled experimental platform designed specifically for applied physics education. Utilizing an Arduino microcontroller, a customizable multi-wavelength light emitting diode (LED) array, and photosensors, our setup generates diverse, real-time optical datasets ideal for training and evaluating foundational ML algorithms, including traversal methods, Bayesian inference, and deep learning. The platform facilitates a closed-loop, self-driving experimental workflow, encompassing automated data collection, preprocessing, model training, and validation. Through systematic performance comparisons, we demonstrate the superior ability of deep learning to capture complex nonlinear relationships compared to traversal and Bayesian methods. At approximately $60, this open-source IoT platform provides an accessible, practical pathway for students to master advanced ML concepts, promoting deeper conceptual insights and essential technical skills required for the next generation of physicists and engineers.

physics.ed-ph↗

arXiv2026

LaVR: Scene Latent Conditioned Generative Video Trajectory Re-Rendering using Large 4D Reconstruction Models

Given a monocular video, the goal of video re-rendering is to generate views of the scene from a novel camera trajectory. Existing methods face two distinct challenges. Geometrically unconditioned models lack spatial awareness, leading to drift and deformation under viewpoint changes. On the other hand, geometrically-conditioned models depend on estimated depth and explicit reconstruction, making them susceptible to depth inaccuracies and calibration errors. We propose to address these challenges by using the implicit geometric knowledge embedded in the latent space of a large 4D reconstruction model to condition the video generation process. These latents capture scene structure in a continuous space without explicit reconstruction. Therefore, they provide a flexible representation that allows the pretrained diffusion prior to regularize errors more effectively. By jointly conditioning on these latents and source camera poses, we demonstrate that our model achieves state-of-the-art results on the video re-rendering task. Project webpage is https://lavr-4d-scene-rerender.github.io/.

cs.CV↗

arXiv2026

$\mathrm{Cs_3V_9Te_{13}}$: A New Vanadium-Based Material with a Reuleaux-Triangle-Like Lattice and a Possible Phase Transition near 48 K

Exploring and synthesizing materials with new crystal structures provides an important route to discovering exotic quantum phenomena. However, materials with unconventional lattice geometries remain largely unexplored. Here, we report the discovery of a new vanadium-based material, $\mathrm{Cs_3V_9Te_{13}}$, featuring a Reuleaux-triangle-like lattice. Electrical transport and magnetic measurements consistently reveal an anomaly near 48 K, and this feature shows little sensitivity to the applied magnetic field. A corresponding anomaly is also observed in the Hall coefficient near 48 K, indicating a marked change in the carrier response. In addition, temperature-dependent x-ray diffraction results indicate no obvious structural change across 48 K. Taken together, these results suggest that the anomaly is not induced by the structural transition, but associated to a possible electronic and/or magnetic phase transition. High-pressure transport measurements and first-principles calculations further reveal a highly tunable electronic state in $\mathrm{Cs_3V_9Te_{13}}$, with the kagome-like electronic feature and pressure-suppressed antiferromagnetism. These results demonstrate this material, with its structurally novel Reuleaux-triangle-like lattice, as a new platform for exploring the interplay between nontrivial lattice geometry and emergent physical phenomena.

cond-mat.mtrl-sci↗

arXiv2026

Machine Learning-Driven High-Precision Model for $α$-Decay Energy and Half-Life Prediction of superheavy nuclei

Based on Extreme Gradient Boosting (XGBoost) framework optimized via Bayesian hyperparameter tuning, we investigated the α-decay energy and half-life of superheavy nuclei. By incorporating key nuclear structural features-including mass number, proton-to-neutron ratio, magic number proximity, and angular momentum transfer-the optimized model captures essential physical mechanisms governing $α$-decay. On the test set, the model achieves significantly lower mean absolute error (MAE) and root mean square error (RMSE) compared to empirical models such as Royer and Budaca, particularly in the low-energy region. SHapley Additive exPlanations (SHAP) analysis confirms these mechanisms are dominated by decay energy, angular momentum barriers, and shell effects. This work establishes a physically consistent, data-driven tool for nuclear property prediction and offers valuable insights into $α$-decay processes from a machine learning perspective.

nucl-th↗

arXiv2026

Systematic Study on the $α$-particle preformation factor in the theory of $α$-decay based on the Tabular Prior-data Fitted Network (TabPFN)

A hybrid approach combining the Tabular Prior-data Fitted Network (TabPFN) with the Coulomb and Proximity Potential Model (CPPM) is developed to investigate $α$-particle preformation factors $P_α$ and their impact on $α$-decay half-lives. The TabPFN model, trained on 498 nuclei, accurately learns the relationship between nuclear structure properties and $P_α$, achieving a root mean square deviation of $σ_{\mathrm{rms}} = 0.211$. The predicted factors reveal clear odd-even staggering and shell closure effects, and exhibit linear correlations with both $Q_α^{-1/2}$ and the fragmentation potential $V_{\mathrm{frag}}$. When incorporated into CPPM calculations, the machine-learning-based $P_α$ values significantly improve half-life predictions. Similar improvements are also obtained when deformation effects are included in the potential barrier description. The capability of the model is further demonstrated through predictions for superheavy nuclei ($Z = 117$--120), suggesting $N = 184$ as a potential neutron magic number.

nucl-th↗

arXiv2026

DM0: An Embodied-Native Vision-Language-Action Model towards Physical AI

Moving beyond the traditional paradigm of adapting internet-pretrained models to physical tasks, we present DM0, an Embodied-Native Vision-Language-Action (VLA) framework designed for Physical AI. Unlike approaches that treat physical grounding as a fine-tuning afterthought, DM0 unifies embodied manipulation and navigation by learning from heterogeneous data sources from the onset. Our methodology follows a comprehensive three-stage pipeline: Pretraining, Mid-Training, and Post-Training. First, we conduct large-scale unified pretraining on the Vision-Language Model (VLM) using diverse corpora--seamlessly integrating web text, autonomous driving scenarios, and embodied interaction logs-to jointly acquire semantic knowledge and physical priors. Subsequently, we build a flow-matching action expert atop the VLM. To reconcile high-level reasoning with low-level control, DM0 employs a hybrid training strategy: for embodied data, gradients from the action expert are not backpropagated to the VLM to preserve generalized representations, while the VLM remains trainable on non-embodied data. Furthermore, we introduce an Embodied Spatial Scaffolding strategy to construct spatial Chain-of-Thought (CoT) reasoning, effectively constraining the action solution space. Experiments on the RoboChallenge benchmark demonstrate that DM0 achieves state-of-the-art performance in both Specialist and Generalist settings on Table30.

cs.RO↗

arXiv2026

Realization of a Wigner-Mott insulator in 6R-TaS$_2$ superconductor

Wigner-Mott insulating states represent a paradigmatic manifestation of strong electronic correlations, in which long-range Coulomb interactions drive spontaneous charge ordering and enable Mott localization at fractional electronic fillings. Such states have been theoretically proposed to arise from the cooperative interplay between onsite and inter-site Coulomb interactions. However, experimental realizations of the simultaneous microscopic observation of interaction-driven charge order and genuine Mott localization, which are the defining hallmarks of a Wigner-Mott insulator, have remained elusive. Here we report the observation of a Wigner-Mott insulating state in 6R-TaS$_2$ using scanning tunneling microscopy. By locally injecting electrons into the depleted 1T layer, we induce distinct Star-of-David charge-ordered superstructures and realize a cascade of insulating phases. In particular, a $\sqrt{3}\times \sqrt{3}$ charge-ordered superstructure at one-third filling hosts a robust Mott gap despite fractional filling. The spontaneous relaxation from excited states back to the ground state demonstrates that this Wigner-Mott phase is stabilized by the cooperative effects of onsite and inter-site Coulomb interactions. Our results provide direct microscopic evidence for a Wigner-Mott mechanism and establish 6R-TaS$_2$ as a platform for the controlled realization and investigation of Wigner-Mott insulating states.

cond-mat.str-el↗

arXiv2026

Electronic procrystalline state in moire structures

Solid state materials can display varieties of atomic structural orders ranging from crystalline to amorphous, underlying their properties and diverse functionalities. Procrystal has emerged as a new category of solids, featuring a long-range ordered lattice framework tiled with disordered atomic or molecular structures on the lattice sites, arousing great interest due to its novel structural and physical properties. However, the electronic analogue of a procrystal, dubbed as an electronic procrystalline (EPC) state, has never been experimentally observed. Here, we report the observation of an EPC state in a moire superstructure formed between a monolayer metallic NiTe2 and a superconductor NbSe2 with incommensurate lattice wavevectors. The observed EPC state exhibits a long-range periodic charge modulation at the moire scale inlaid with short-range irregular orders within each moire cell. Strikingly, the short-range charge orders inside the moire unit cells have proximately root3*root3 quasi-period, which is absent in pristine NiTe2. Intriguingly, the EPC order is also observed in the superconducting state of the moire superstructure. Furthermore, the emergent EPC state and short-range charge order, coexisting with the proximity induced superconductivity, can be precisely modulated with the thickness of NiTe2. Our findings uncover the potential of moire platform for understanding and tuning novel correlated quantum phases with this exotic procrystalline order.

cond-mat.mtrl-sci↗

arXiv2025

Atomically-precise synthesis and simultaneous integration of 2D transition metal dichalcogenides enabled by nano-confinement

Two-dimensional (2D) materials, such as graphene, transition metal dichalcogenides (TMDs), and hBN, exhibit intriguing properties that are sensitive to their atomic-scale structures and can be further enriched through van der Waals (vdW) integration. However, the precise synthesis and clean integration of 2D materials remain challenging. Here, using graphene or hBN as a vdW capping layer, we create a nano-confined environment that directs the growth kinetics of 2D TMDs (e.g., NbSe2 and MoS2), enabling precise formation of TMD monolayers with tailored morphologies, from isolated monolayer domains to large-scale continuous films and intrinsically-patterned rings. Moreover, Janus S-Mo-Se monolayers are synthesized with atomic precision via vdW-protected bottom-plane chalcogen substitution. Importantly, our approach simultaneously produces ultraclean vdW interfaces. This in situ encapsulation reliably preserves air-sensitive materials, as evidenced by the enhanced superconductivity of nano-confined NbSe2 monolayers. Altogether, our study establishes a versatile platform for the controlled synthesis and integration of 2D TMDs for advanced applications.

cond-mat.mtrl-sci↗

arXiv2025

The parent state in kagome metals and superconductors: Chiral-nematic Fermi liquid state

The kagome metals and superconductors hosting rich correlated and topological electronic states have captivated quantum materials research. These states are triggered by an unconventional chiral charge density wave (CDW) wherein a chiral superconductivity emerges at low temperatures, yet the origin of this chiral CDW order, the parent state, is unresolved. Here, we report the discovery of a parent chiral-nematic Fermi liquid state in kagome metals and superconductors. We use spectroscopic-imaging scanning tunneling microscopy to study Ti-doped CsV3Sb5 where the CDW is suppressed, and find that multiorbital Fermi surfaces break all mirror reflections and exhibit handedness. We observe the chiral low-energy quasiparticle dispersions, providing direct evidence for a chiral-nematic electronic structure. We further observe the direct transition from the parent state to a chiral-nematic superconducting state. Moreover, in the samples with chiral CDW, we also observe the residual chiral-nematic QPI features, demonstrating that the CDW-triggered exotic states descend from the chiral-nematic Fermi liquid. Our findings not only provide a plausible chiral-nematic parent state, but also establish a new conceptual framework for exploring the emergence and consequences of such exotic quantum phases.

cond-mat.supr-con↗

arXiv2025

Tuning Bound States of Symmetry-Breaking Vortices via Unidirectional Charge Density Wave in a Transition-Metal Dichalcogenide Superconductor

The interplay between charge density wave (CDW) and superconducting vortex bound states are crucial for fundamental physics of superconductivity and advancing quantum nanotechnologies. However, the CDW-mediated modulation of vortex bound states, which opens up a new platform for vortex engineering, remains unexplored. Here, we report spatially anisotropic vortex states modulated by the unidirectional CDWs in a transition-metal dichalcogenide superconductor 1T''-NbTe2 using ultra-low-temperature scanning tunneling microscopy/spectroscopy. The stripe-like 3x1x3 CDW order exhibits a robust three-dimensional character across step edges and coexists with superconductivity below a critical temperature of 0.4 K. Under out-of-plane magnetic fields, we observe elliptical vortices whose elongation aligns with the CDW stripes, indicating strong coupling between vortex morphology and underlying electronic order. Remarkably, CDW domain boundaries induce abrupt changes in vortex orientation and vortex bound states, enabling controllable vortex states across CDW nanodomains. These findings establish a new pathway for manipulating superconducting vortex bound states via CDW coupling.

cond-mat.supr-con↗

arXiv2025

Prevailing orbital excitations in paramagnetic kagome superconductor Cs(V$_{0.95}$Ti$_{0.05}$)$_3$Sb$_5$

Using the muon as a sensitive local magnetic probe, we investigated the layered kagome superconductor Cs(V$_{0.95}$Ti$_{0.05}$)$_3$Sb$_5$, a material notably devoid of both static magnetic moments and long-range charge order. Our transverse-field $μ$SR measurements reveal that the local magnetic susceptibility, obtained via the muon Knight shift, is dominated by orbital excitations with a split energy levels around 20 meV. Meanwhile, the persistence of itinerant electron paramagnetism down to 5 K and 7 T confirms the absence of static magnetism within this regime. In addition, zero-field (ZF) $μ$SR experiments detect a significant increase in the inhomogeneous nuclear dipolar field distribution below a featured temperature at 70 K. We attribute this ZF-$μ$SR feature to the emergence of local lattice distortions at low temperatures, potentially arising from orbital ordering. Significantly, our study establishes that orbital excitations constitute an intrinsic property of the layered V-Sb kagome lattice. Despite its small magnitude, spin-orbit coupling plays a crucial role in governing the lattice dynamics, potentially driving the emergence of novel phenomena such as phonon carrying angular momentum in crystals with non-chiral point groups.

cond-mat.str-el↗

arXiv2025

RoboChallenge: Large-scale Real-robot Evaluation of Embodied Policies

Testing on real machines is indispensable for robotic control algorithms. In the context of learning-based algorithms, especially VLA models, demand for large-scale evaluation, i.e. testing a large number of models on a large number of tasks, is becoming increasingly urgent. However, doing this right is highly non-trivial, especially when scalability and reproducibility is taken into account. In this report, we describe our methodology for constructing RoboChallenge, an online evaluation system to test robotic control algorithms, and our survey of recent state-of-the-art VLA models using our initial benchmark Table30.

cs.RO↗

arXiv2025

Bayesian optimization and nonlocal effects method for $α$ decay of superheavy nuclei based on CPPM

We combine nonlocal effects with Bayesian Neural Network (BNN) methods to enhance the prediction accuracy of $α$ decay half-lives. The results indicate that accounting for nonlocal effects significantly impacts the half-life calculations, while the BNN method markedly improves prediction accuracy and demonstrates strong extrapolation capabilities. Furthermore, we discuss the impact of nuclear deformation (the quadrupole deformation factor $β_2$) on machine learning predictions. Through Shapley Additive Explanations (SHAP), we conducted a quantitative comparison of six input features within the BNN, revealing that the $α$ decay energy $Q_α$ is the primary driving factor affecting the half-life $T_{1/2}$. Leveraging the remarkable extrapolation ability of the BNN, we successfully predicted the $α$ decay half-lives of the isotope chain ($Z=118, 120$), uncovering a significant shell effect at neutron number $N=184$. For the isotopic chains ($Z=118, 120$), the predicted $α$ decay half-lives and $Q_α$ values satisfy the Geiger-Nuttall (G-N) linear relationship. This result further confirms the predictive reliability of the proposed model. Keywords: $α$ decay, half-lives, nonlocal effects, Bayesian Neural Network, Coulomb and proximity potential model

nucl-th↗

arXiv2025

Intrinsic Quantum Clusters in Kagome Weyl Semimetal Co3Sn2S2

Impurities and intrinsic point defects, which profoundly influence spin, charge, and topological degrees of freedom, are crucial parameters for tuning quantum states in quantum materials. The magnetic Weyl semimetal Co3Sn2S2 with its strong spin-orbit coupling, intrinsic ferromagnetism, and kagome lattice of correlated electrons, provides a compelling platform for studying impurity excited states. Yet, the role of intrinsic impurities in shaping its quantum states remains elusive. Here, we uncover intrinsic quantum clusters-localized intrinsic point defects that act as tunable quantum perturbations capable of reshaping electronic states and order parameters, on the surface of Co3Sn2S2 via scanning tunneling microscopy/spectroscopy and non contact atomic force microscopy, combined with scanning transmission electron microscopy/electron energy loss spectroscopy. These clusters are identified as native oxygen defects that dominate the intrinsic defect landscape on both cleaved surface terminations. On the Sn-terminated surface, oxygen impurities occupy hollow sites between three Sn atoms, and tune the flat band near the Fermi level, which exhibits orbital magnetism induced unconventional Zeeman effect under an applied magnetic field. On the S-terminated surface, oxygen interstitials reside slightly off center relative to the S lattice and generate occupied impurity states that retain sixfold symmetry at higher energies but reduce to C2 symmetry at lower energies. In contrast, these impurity states show no measurable magnetic response. Our findings establish that intrinsic oxygen-related quantum clusters act as tunable local perturbations in a topological kagome magnet, offering a versatile platform to probe and engineer impurity-driven phenomena in correlated and topological systems.

cond-mat.mtrl-sci↗

arXiv2025

GenAnalysis: Joint Shape Analysis by Learning Man-Made Shape Generators with Deformation Regularizations

We present GenAnalysis, an implicit shape generation framework that allows joint analysis of man-made shapes, including shape matching and joint shape segmentation. The key idea is to enforce an as-affine-as-possible (AAAP) deformation between synthetic shapes of the implicit generator that are close to each other in the latent space, which we achieve by designing a regularization loss. It allows us to understand the shape variation of each shape in the context of neighboring shapes and also offers structure-preserving interpolations between the input shapes. We show how to extract these shape variations by recovering piecewise affine vector fields in the tangent space of each shape. These vector fields provide single-shape segmentation cues. We then derive shape correspondences by iteratively propagating AAAP deformations across a sequence of intermediate shapes. These correspondences are then used to aggregate single-shape segmentation cues into consistent segmentations. We conduct experiments on the ShapeNet dataset to show superior performance in shape matching and joint shape segmentation over previous methods.

cs.GR↗

arXiv2025

Tailoring composite skyrmionic spin textures in an above-room-temperature ferromagnet Fe3-xGaTe2

Realizing room-temperature tunable skyrmionic objects in van der Waals ferromagnet offers unparalleled prospects for future spintronics. Here, we report an experimental investigation on the emergence and evolution of skyrmionic spin textures in the non-stoichiometric Fe3-xGaTe2 using magnetic force microscopy. The iron-deficiency-specific magnetic states of stripe, striped skyrmionium and striped skyrmion sack are observed. Through zero-field-cooling and field-cooling measurements, we observed distinct topological transitions and trivial transitions (distinguished by changes in topological charge) emerging during the stepwise evolution of topological spin textures, which enabled us to develop an evolution pathway model. Leveraging this model, the room-temperature stable composite topological spin textures of skyrmionium, skyrmion bag and sack states are further controllably realized via the exclusive topological-transition path (regulated by magnetic field and DMI intensity). Our work provides valuable insights into the room-temperature realization of topological spin textures in Fe3-xGaTe2, and inspires further exploration of their potential applications in heterostructure spintronics.

cond-mat.mtrl-sci↗