arXiv subjects

Chen Si

Publications and source records attributed to Chen Si.

13 recordsLinked to original sources

arXiv2026

Realization and manipulation of spiral charge density waves in a two-dimensional metal

Nearly degenerate charge-density-wave (CDW) states play a central role in the competition among collective phenomena. In real materials, however, these states are often intertwined by disorder, hindering their disentanglement and control. Here we show that strain can lift this near-degeneracy and spatially separate distinct CDW states in NbSe2. Using van der Waals (vdW) interactions, we stabilize a micron-scale strain network that produces spatially inhomogeneous strain fields. Within this landscape, the intrinsic 3 * 3 CDW superlattice of pristine NbSe2 transforms into an isolated unidirectional 4 * 1 order under 1D-confined compression, and into a 2 * 2 order under biaxial tension. The 4 * 1 CDW has a multiband origin and exhibits markedly enhanced thermal stability, persisting up to 70 K. At strain-network nodes, it further develops into chiral spiral textures, which can be melted by voltage pulses. These results establish strain as a powerful approach to disentangle, stabilize and manipulate competing electronic orders.

cond-mat.mes-hall↗

arXiv2026

AI-accelerated metallized $\sigma$-bonding screening for superconductor discovery

The computational discovery of phonon-mediated superconductors is hindered by the prohibitive cost of density functional perturbation theory (DFPT). Here, guided by the metallized $\sigma$-bonding picture, we introduce the $\sigma$-bonding density of states ($\sigma$DOS) as an efficient physical descriptor to identify high-transition-temperature ($T_{\mathrm{c}}$) superconductors from density functional theory (DFT)-level electronic structure without explicit DFPT calculations. The evaluation of $\sigma$DOS can be further accelerated by a deep-learning DFT Hamiltonian method, enabling efficient large-scale screening for superconductors. Screening 2 million materials, we identify B$_{13}$Se as an ambient-pressure superconductor candidate with predicted $T_{\mathrm{c}} > 40$~K, together with a family of high-$T_{\mathrm{c}}$ B$_{13}X$ candidates, supporting the effectiveness of this discovery strategy. By bridging physics priors with AI acceleration, this study delivers an efficient and generalizable route for computational materials discovery in the AI era.

physics.comp-ph↗

arXiv2026

AnyHand: A Large-Scale Synthetic Dataset for RGB(-D) Hand Pose Estimation

We present AnyHand, a large-scale synthetic dataset designed to advance the state of the art in 3D hand pose estimation. While recent works with foundation approaches have shown that scaling training data markedly improves hand pose estimation, existing real-world datasets are limited in coverage, and prior synthetic datasets rarely provide occlusions, arm details, and aligned depth together at scale. To address this bottleneck, our proposed AnyHand contains 2.5M single-hand and 4.1M hand-object interaction RGB-D images, with rich geometric annotations. We show that extending the original training data recipes of existing RGB baselines with AnyHand yields significant gains on multiple benchmarks (FreiHAND and HO-3D), even when keeping the architectures and training schemes fixed. Together with extensive ablations on the scale and composition of the training data setups, these results suggest that training data diversity and quality are as critical as scale for advancing hand pose estimation. We further examine the utility of AnyHand's aligned depth maps in the appendix, showing that scaling RGB-D supervision with AnyHand allows a lightweight depth-fusion variant of existing RGB baselines to outperform prior RGB-D methods.

cs.CV↗

arXiv2025

Explicit Context-Driven Neural Acoustic Modeling for High-Fidelity RIR Generation

Realistic sound simulation plays a critical role in many applications. A key element in sound simulation is the room impulse response (RIR), which characterizes how sound propagates within a given space. Recent studies have applied neural implicit methods to learn RIR using context information collected from the environment, such as scene images. However, these approaches do not effectively leverage explicit geometric information from the environment. To further exploit neural implicit models with direct geometric features, we present MiNAF, which queries a rough room mesh at given locations and extracts distance distributions as an explicit representation of local context. Our approach demonstrates that incorporating explicit local geometric features can better guide the model in generating more accurate RIR predictions. Through comparisons with conventional and state-of-the-art methods, we show that MiNAF performs competitively across various evaluation metrics.

cs.SD↗

arXiv2025

ArcMemo: Abstract Reasoning Composition with Lifelong LLM Memory

While inference-time scaling enables LLMs to carry out increasingly long and capable reasoning traces, the patterns and insights uncovered during these traces are immediately discarded once the context window is reset for a new query. External memory is a natural way to persist these discoveries, and recent work has shown clear benefits for reasoning-intensive tasks. We see an opportunity to make such memories more broadly reusable and scalable by moving beyond instance-based memory entries (e.g. exact query/response pairs, or summaries tightly coupled with the original problem context) toward concept-level memory: reusable, modular abstractions distilled from solution traces and stored in natural language. For future queries, relevant concepts are selectively retrieved and integrated into the prompt, enabling test-time continual learning without weight updates. Our design introduces new strategies for abstracting takeaways from rollouts and retrieving entries for new queries, promoting reuse and allowing memory to expand with additional experiences. We evaluate on ARC-AGI, a benchmark that stresses compositional generalization and abstract reasoning, making it a natural fit for concept memory. Our method yields a 7.5% relative gain over a strong no-memory baseline with performance continuing to scale with inference compute. We find abstract concepts to be the most consistent memory design, outscoring the baseline at all tested inference compute scales. Moreover, dynamically updating memory during test-time outperforms fixed settings, supporting the hypothesis that accumulating and abstracting patterns enables further solutions in a form of self-improvement. Code is available at https://github.com/matt-seb-ho/arc_memo.

cs.AI↗

arXiv2024

Universal materials model of deep-learning density functional theory Hamiltonian

Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence, but how to achieve this fantastic and challenging objective remains elusive. Here, we propose a feasible pathway to address this paramount pursuit by developing universal materials models of deep-learning density functional theory Hamiltonian (DeepH), enabling computational modeling of the complicated structure-property relationship of materials in general. By constructing a large materials database and substantially improving the DeepH method, we obtain a universal materials model of DeepH capable of handling diverse elemental compositions and material structures, achieving remarkable accuracy in predicting material properties. We further showcase a promising application of fine-tuning universal materials models for enhancing specific materials models. This work not only demonstrates the concept of DeepH's universal materials model but also lays the groundwork for developing large materials models, opening up significant opportunities for advancing artificial intelligence-driven materials discovery.

physics.comp-ph↗

arXiv2024

Enhancement of Ising superconductivity in monolayer NbSe$_2$ via surface fluorination

Recently discovered Ising superconductors have garnered considerable interest due to their anomalously large in-plane upper critical fields ($B_{c2}$). However, the requisite strong spin-orbital coupling in the Ising pairing mechanism generally renders these superconductors heavy-element dominant with notably low superconducting transition temperatures ($T_c$). Here, based on the Migdal-Eliashberg theory and the mean-field Bogoliubov-de Gennes Hamiltonian, we demonstrate a significant enhancement of Ising superconductivity in monolayer NbSe$_2$ through surface fluorination, as evidenced by concomitant improvements in $T_c$ and $B_{c2}$. This enhancement arises from three predominant factors. Firstly, fluorine atoms symmetrically and stably adhere to both sides of the monolayer NbSe$_2$, thereby maintaining the out-of-plane mirror symmetry and locking carrier spins out-of-plane. Secondly, fluorination suppresses the charge density wave in monolayer NbSe$_2$ and induces a van Hove singularity in the vicinity of the Fermi level, leading to a marked increase in the number of carriers and, consequently, strengthening the electron-phonon coupling (EPC). Lastly, the appearance of fluorine-related, low-frequency phonon modes further augments the EPC. Our findings suggest a promising avenue to elevate $T_c$ in two-dimensional Ising superconductors without compromising their Ising pairing.

cond-mat.supr-con↗

arXiv2023

Quantifying proximity-induced superconductivity from first-principles calculations

Proximity induced superconductivity with a clean interface has attracted much attention in recent years. We discuss how the commonly-employed electron tunneling approximation can be hybridized with first-principles calculation to achieve a quantitative characterization starting from the microscopic atomic structure. By using the graphene-Zn heterostructure as an example, we compare this approximated treatment to the full \textit{ab inito} anisotropic Eliashberg formalism. Based on the calculation results, we discuss how superconductivity is affected by the interfacial environment.

cond-mat.supr-con↗

arXiv2021

Stacking effect and Coulomb correlation in layered charge density wave phase of 1T-NbS2

Based on first-principles calculations, we explored the interplay between stacking effect and electron-electron correlation in the layered vdW material of bulk 1T-NbS2 with a 2D charge density wave (CDW) order. Without considering the Coulomb correlation, two energetically favorable out-of-plane stacking configurations are identified: one is a metallic phase with a single-layer stacking pattern, another is a band insulator with a paired-bilayer stacking configuration. Even though the Coulomb correlation is taken into account, the two energetic favorable stacking orders are still far more stable than other stacking orders. Furthermore, increasing the Coulomb interaction, the paired-bilayer stacking configuration transforms from nonmagnetic band insulator to antiferromagnetic insulator, while the single-layer stacking undergoes a Slater-Mott metal-insulator transition, which indicates the non-negligible role of electron-electron correlation interactions. In addition, the electronic structure and magnetic ground state change drastically among different stacking configurations, providing a platform to tune the electronic structures and interlayer magnetic interactions by altering the stacking order. In contrast to the widely accepted scenario of Mott localization as the driving force behind the gap formation in the CDW phase of layered transition metal dichalcogenides, our results not only highlight the crucial role of stacking order in the electronic structures of 1T-NbS2, but also shed fresh light on the distinct effects of Coulomb interaction in different stacking arrangements.

cond-mat.str-el↗

arXiv2020

Phase Transition in a Memristive Suspended MoS2 Monolayer Probed by Opto- and Electro-Mechanics

Semiconducting monolayer of 2D material are able to concatenate multiple interesting properties into a single component. Here, by combining opto-mechanical and electronic measurements, we demonstrate the presence of a partial 2H-1T phase transition in a suspended 2D monolayer membrane of MoS2. Electronic transport shows unexpected memristive properties in the MoS2 membrane, in the absence of any external dopants. A strong mechanical softening of the membrane is measured concurrently and may only be related to the phase 2H-1T phase transition which imposes a 3percent directional elongation of the topological 1T phase with respect to the semiconducting 2H. We note that only a few percent 2H- 1T phase switching is sufficient to observe measurable memristive effects. Our experimental results combined with First-principles total energy calculations indicate that sulfur vacancy diffusion plays a key role in the initial nucleation of the phase transition. Our study clearly shows that nanomechanics represents an ultrasensitive technique to probe the crystal phase transition in 2D materials or thin membranes. Finally, a better control of the microscopic mechanisms responsible for the observed memristive effect in MoS2 is important for the implementation of future devices.

cond-mat.mes-hall↗

arXiv2014

Functionalized Germanene as a Prototype of Large-Gap Two-Dimensional Topological Insulators

We propose new two-dimensional (2D) topological insulators (TIs) in functionalized germanenes (GeX, X=H, F, Cl, Br or I) using first-principles calculations. We find GeI is a 2D TI with a bulk gap of about 0.3 eV, while GeH, GeF, GeCl and GeBr can be transformed into TIs with sizeable gaps under achievable tensile strains. A unique mechanism is revealed to be responsible for large topologically-nontrivial gap obtained: owing to the functionalization, the $σ$ orbitals with stronger spin-orbit coupling (SOC) dominate the states around the Fermi level, instead of original $π$ orbitals with weaker SOC; thereinto, the coupling of the $p_{xy}$ orbitals of Ge and heavy halogens in forming the $σ$ orbitals also plays a key role in the further enlargement of the gaps in halogenated germanenes. Our results suggest a realistic possibility for the utilization of topological effects at room temperature.

cond-mat.mtrl-sci↗

arXiv2013

Superconducting Graphene: the conspiracy of doping and strain

Graphene has exhibited a wealth of fascinating properties, but is also known not to be a superconductor. Remarkably, we show that graphene can be made a conventional Bardeen-Cooper-Schrieffer superconductor by the combined effect of charge doping and tensile strain. While the effect of doping is obvious to enlarge Fermi surface, the effect of strain is profound to greatly increase the electron-phonon coupling. At the experimental accessible doping (4E+14cm-2) and strain (~16%) levels, the superconducting critical temperature Tc reaches as high as ~30 K, the highest for a single-element material above the liquid hydrogen temperature. This significantly makes graphene a commercially viable superconductor.

cond-mat.supr-con↗

arXiv2012

Electronic Strengthening of Graphene by Charge Doping

Graphene is known as the strongest 2D material in nature, yet we show that moderate charge doping of either electrons or holes can further enhance its ideal strength by up to ~17%, based on first principles calculations. This unusual electronic enhancement, versus conventional structural enhancement, of material's strength is achieved by an intriguing physical mechanism of charge doping counteracting on strain induced enhancement of Kohn anomaly, which leads to an overall stiffening of zone boundary K1 phonon mode whose softening under strain is responsible for graphene failure. Electrons and holes work in the same way due to the high electron-hole symmetry around the Dirac point of graphene, while over doping may weaken the graphene by softening other phonon modes. Our findings uncover another fascinating property of graphene with broad implications in graphene-based electromechanical devices.

cond-mat.mtrl-sci↗