arXiv subjects

Shi Liu

Publications and source records attributed to Shi Liu.

At least 19 recordsLinked to original sources

arXiv2026

Ultrafast Polar Topology Reversal via THz-Driven Inertial Switching

Topological polar structures in ferroelectric heterostructures offer functional states beyond ordinary domains. As polarization reversal underpins ferroelectric devices, switching between degenerate topological variants, which we refer to as topology reversal, would greatly expand the ways to control polar textures. However, polar topology reversal is challenging because it requires coordinated flipping of oppositely oriented local polarizations, a process not naturally driven by a uniform electric field. Using a multiscale modeling approach, we show that polar topology can be stabilized and reversed in a simple Pt/PbTiO$_3$/Pt nanocapacitor. Electrode-controlled boundary conditions drive tetragonal PbTiO$_3$ into competing orthorhombic- and rhombohedral-like polarization states, which form building blocks for diverse topological structures, including vortex arrays, skyrmion lattices, dipole spirals, and dipole waves. We further show that the same out-of-plane THz pulse can reversibly switch a double-$\mathbf q$ dipole wave between degenerate topological variants within a few picoseconds, while preserving its spatial modulation. This process is enabled by damping-assisted inertial switching, in which the THz pulse injects energy to drive large-angle polarization rotation across shallow barriers, while dissipation removes excess energy and traps the system in the reversed state. These results establish ferroelectric nanocapacitors as a simple platform for programmable polar topology and low-power, THz-speed control through dissipative dynamics.

cond-mat.mtrl-sci↗

arXiv2026

Interlayer Registry Controls Polar Topology in Twisted Bilayer h-BN

Twisting van der Waals bilayers creates a moir'e stacking field with periodically varying local interlayer registry, enabling spatially patterned polarization. However, a systematic understanding of how this registry-controlled polar topology evolves with twist angle, temperature, and mechanical strain is still lacking. Here, using first-principles-trained deep-potential molecular dynamics simulations, we show that spatially varying interlayer registry governs the polarization texture and can be directly tuned by twist and relative layer deformation. As the twist angle decreases, the moir'e polarization texture evolves from compact, nearly circular vortex-like patterns to extended triangular AB/BA domains. This crossover arises from enhanced lattice relaxation at small twist angles, which expands low-energy, oppositely polarized AB/BA regions and confines in-plane polarization mainly to domain boundaries. Despite this reconstruction, the local in-plane winding remains tied to interlayer registry and persists up to 1000 K, indicating robust topology against thermal fluctuations. We further show that the mechanical response is dictated by whether deformation changes the relative registry: homogeneous deformation of both layers produces only weak distortions, whereas layer-selective deformation strongly reconstructs the moiré polarization texture. A mismatch-matrix registry-mapping model is developed to capture this response, explaining deformation-induced changes in vortex radii at larger twist angles and triangular-domain geometry at smaller twist angles. These results identify interlayer registry as the central control parameter for forming and tuning moir'e polar topology in twisted bilayer h-BN.

cond-mat.mtrl-sci↗

arXiv2026

Identification and Structural Decomposition of Hidden Defect Configurations: A Case Study of Charged Oxygen Divacancies in HfO$_2$

Extensive configurational searches have revealed hidden defect structures in crystalline compounds, yet how their local bonding arrangements modify the surrounding atomic environment remains less well understood. We examine this question in hafnium oxide (HfO$_2$), which hosts energetically competing monoclinic (M), tetragonal (T), and polar orthorhombic (PO) polymorphs. For the $c$-axis oxygen-divacancy complex ($T_c$), a local-distortion search with density-functional theory (DFT) relaxation under tetragonal-lattice confinement identifies a hidden family of low-energy configurations ($T_c^{*}$). For the fully charged complex ($2\mathrm{V}_{\mathrm{O}}^{2+}$), structural decomposition using Smooth Overlap of Atomic Positions (SOAP) descriptors reveals mixed M-, T-, and PO-like character, supported by independent analyses of Hf coordination and bond geometry. The idealized $T_c$ reference connects barrierlessly to the lowest-energy mixed-motif $T_c^{*}$ configuration, lowering the energy by $18.4$ meV/f.u. within the confined landscape. After releasing the lattice constraint, we compare transformation pathways to M and PO using solid-state nudged elastic band calculations with cell-relaxed endpoints. The respective barriers increase from $6.9$ and $10.1$ meV/f.u. for the idealized reference to $26.9$ and $15.3$ meV/f.u. for the $T_c^{*}$-derived structure. The larger increase for M makes PO, rather than M, the lower-barrier branch. This case study shows how structural decomposition connects hidden defect configurations with changes in the surrounding atomic environment and kinetic branching between competing polymorphs.

cond-mat.mtrl-sci↗

arXiv2026

Skyrmion nucleus resolves the Landauer paradox

Ferroelectric switching is among the most fundamental and widely studied symmetry-breaking processes in physics. However, the Landauer paradox asserts that an ideal single-domain ferroelectric should be kinetically unswitchable, because a three-dimensional reversed nucleus would carry a prohibitive depolarization penalty, leading to unphysically large coercive field (the electric field required to switch the polarization). Here we show that this long-standing paradox is resolved by topology. In defect-free PbTiO3, large-scale molecular dynamics simulations reveal that the intrinsic critical nucleus is a three-dimensional polar skyrmion. Its continuous polarization rotation and Néel-type boundary walls self-compensate bound charge, suppressing the depolarization energy by orders of magnitude and bringing the predicted coercive field into agreement with experiment. This topological nucleus further overturns the 60-year-old Janovec-Kay-Dunn law. We derive an analytical thickness-dependent coercive-field law governed by field-induced softening of the parent ferroelectric state and controlled by a single material descriptor. This framework captures diverse experimental trends across ferroelectric families and recasts the Kay-Dunn exponent of -2/3 as an effective finite-window behavior. These results establish polar topology as an organizing principle for intrinsic switching and suggest that hidden topological transition states may broadly shape nonequilibrium phase transformations traditionally understood as Landau-type symmetry breaking.

cond-mat.mtrl-sci↗

arXiv2026

Notes2Skills: From Lab Notebooks to Certainty-Aware Scientific Agent Skills

Scientific discovery workflows rely heavily on lab notes, where researchers record observations, interpret uncertain results, and plan follow-up experiments. Unlike polished publications, lab notes preserve evolving scientific reasoning, tacit scientific knowledge, and author uncertainty, giving AI agents access to the process of science. However, most prior work on scientific text focuses on papers, protocols, or structured databases, leaving informal laboratory notes underexplored as inputs to AI agents for science. This gap matters because tacit knowledge in lab notes is not written as a clean set of instructions: validated observations, tentative judgments, and possible experimental next steps often appear in the same passage. If these signals are conflated, an AI agent may mistake uncertain scientific judgments for confirmed conclusions or executable actions. To this end, we present Notes2Skills, a two-stage framework for turning lab notebooks into verifiable skills for scientific AI agents while preserving the author's certainty. Across three wet-lab sessions and seven original downstream configurations, Notes2Skills is the only tested configuration that avoids both observed regime-level failures: over-acting on uncertain notes and missing firm directives. We show that certainty preservation is a key missing piece between lab notebooks and reliable agent skills, opening a path toward safer AI co-scientist systems.

cs.CL↗

arXiv2026

StatefulDiscovery: Evidence-Calibrated Claim Formation in Open-Ended Scientific Discovery

Open-ended scientific discovery asks agents to move beyond executing analyses for predefined questions. Across multiple rounds of exploration, a discovery agent must decide which phenomena warrant investigation while avoiding overinterpretation, where emerging claims exceed the evidential scope of the analyses. This creates an evidence-calibration problem: the exploration trajectory must be coupled with claim status so that evidence can guide both what to investigate next and what can be claimed. We introduce \textsc{StatefulDiscovery}, a discovery framework that externalizes investigation state and uses it to coordinate frontier selection, evidence acquisition, and claim adjudication. We evaluate \textsc{StatefulDiscovery} across 40 real-data discovery tasks. Compared with several baselines, \textsc{StatefulDiscovery} produces more claims overall judged to be both well-supported and high-value. Ablations indicate distinct roles for structured hypotheses, local adjudication, frontier control and persistent states. Together, these results suggest that explicit discovery state can couple exploration with evidence-calibrated claim formation. Our code is released at \href{https://github.com/SUSTech-GenAI/StatefulDiscovery.git}{https://github.com/SUSTech-GenAI/StatefulDiscovery.git}.

cs.AI↗

arXiv2026

UT-ACA: Uncertainty-Triggered Adaptive Context Allocation for Long-Context Inference

Long-context inference remains challenging for large language models due to attention dilution and out-of-distribution degradation. Context selection mitigates this limitation by attending to a subset of key-value cache entries, yet most methods allocate a fixed context budget throughout decoding despite highly non-uniform token-level contextual demands. To address this issue, we propose Uncertainty-Triggered Adaptive Context Allocation (UT-ACA), an inference-time framework that dynamically adjusts the context window based on token-wise uncertainty. UT-ACA learns an uncertainty detector that combines semantic embeddings with logit-based confidence while accounting for uncertainty accumulation across decoding steps. When insufficient evidence is indicated, UT-ACA selectively rolls back, expands the context window, and regenerates the token with additional support. Experiments show that UT-ACA substantially reduces average context usage while preserving generation quality in long-context settings. Code and dataset are available at https://github.com/Tommy307/UT-ACA.

cs.CL↗

arXiv2026

Percolating Multifractal Domains at a Polymorphic Phase Boundary

Giant piezoelectricity in ferroelectrics is commonly associated with phase-boundary instabilities, among which the polymorphic phase boundary (PPB) is a prominent example conventionally attributed to the coexistence of ferroelectric phases. Here, using large-scale molecular dynamics simulations of the lead-free (K,Na)NbO3-(Bi,Na)ZrO3 solid solutions, we show that the PPB hosts a percolating multifractal polar domain which governs the dielectric and piezoelectric responses. By quantifying the global fractal dimension and multifractal spectrum width of this polar network, we identify fractal connectivity and multiscale heterogeneity as microstructural order parameters for the PPB. The maximum reversible piezoelectric response occurs when the fractal-domain volume fraction approaches the three-dimensional percolation threshold, suggesting that near-critical polar connectivity enables giant reversible electromechanical coupling. In this mechanism, the fractal backbone preserves polar memory and provides the restoring force required for reversibility, while the surrounding nonfractal regions supply the polar compliance needed for large polarization rotation and strain. These results establish percolating multifractal polar domains as a microscopic mechanism for PPB-enhanced piezoelectricity and suggest fractal connectivity as a design parameter for high-performance piezoelectrics.

cond-mat.mtrl-sci↗

arXiv2026

Emergent Interfacial Magnetism in Epitaxial RuO$_2$

The magnetic ground state of the altermagnet candidate RuO$_2$ remains controversial, with magnetic signatures observed mainly in epitaxial films. Here we show, using first-principles calculations, that magnetism in epitaxial RuO$_2$ can emerge as an interfacial boundary phase at TiO$_2$/RuO$_2$ interfaces. While TiO$_2$-induced epitaxial strain alone does not make (001)-oriented RuO$_2$ magnetic, explicit TiO$_2$/RuO$_2$ interfaces stabilize sizable Ru moments confined to the first few Ru layers. Charge-density and orbital-resolved analyses reveal interfacial electronic reconstruction, and substrate doping provides a route to tune the induced moments. In symmetric TiO$_2$/RuO$_2$/TiO$_2$ heterostructures, the two magnetic interfaces couple through the metallic RuO$_2$ spacer, producing a thickness-dependent alternation between weak-ferromagnetic and compensated altermagnetic states. Our results identify interface engineering as a practical route to stabilize and control fragile magnetism in RuO$_2$.

cond-mat.mtrl-sci↗

arXiv2026

From Foundation to Application: Improving VLA Models in Practice

Despite recent progress of VLA foundation models, the disparity between laboratory conditions and real-world applications continues to impede their practical implementation. To bridge this gap, we present LingBot-VLA 2.0, which advances LingBot-VLA through improvements in three functional domains. (1) Generalization across tasks and embodiments. Compared to the previous version, we revamp the data processing pipeline and curate around 60,000 hours of data for pretraining, including 50,000 hours of robot trajectories spanning 20 robot configurations and 10,000 hours of egocentric human videos. (2) Expanded action space in addition to dual-arm hardware platforms. In particular, our system accommodates degrees of freedom for the heads, waists, mobile bases, and dexterous hands, thereby empowering the robots to tackle more complex tasks in practical scenarios. (3) Predictive dynamics modeling for improved temporal reasoning. Specifically, we formulate future prediction as a proxy task, facilitated by a video representation model for semantic priors and a depth estimation model for geometric cues. Evaluations on the GM-100 benchmark, conducted in a generalist setting, validate the beneficial impact of these proposed modifications. Furthermore, benefiting from the expanded pretraining data that covers whole-body degrees of freedom, LingBot-VLA-2.0 demonstrates strong cross-embodiment long-horizon mobile manipulation capability across the two robotic platforms.

cs.RO↗

arXiv2026

Unlocking Cryogenic Energy Storage by Constructing Dipole Glass with Unit-cell-level Polar Disorder

Cryogenic energy storage is vital for frontier technologies including deep-space exploration and quantum computing, yet conventional electrochemical energy systems fail below ~230 K due to frozen ion migration. While relaxor-based dielectric capacitors provide high efficiency at room temperature, the intrinsic freezing/growth of polar nanodomains at extended cryogenic regime limits their applications with deteriorated hysteresis losses. Here, we realize superior cryogenic energy-storage performance by designing unit-cell-level disordered dipole-glass state in Pb0.6Sr0.4ZrO3 thin films with composition near antiferroelectric-paraelectric phase boundary. The antiferroelectric-derived dipole-glass introduces enhanced unit-cell-level complexity of dipole interaction that suppresses long-range ferroelectric order. This enables ultralow-hysteresis operation (efficiency > 88%) down to 4 K, delivering record-high energy density (211 J/cm^3) at 9 MV/cm, stability over 10^8 charge/discharge cycles and microsecond-scale charge/discharge capability. This work establishes a dipole-glass paradigm for cryogenic dielectric capacitors, opening a new avenue to highly-efficient energy-storage systems with broad applications in frontier nanoelectronics.

cond-mat.mtrl-sci↗

arXiv2026

Multiferroic VHfO$_4$ with Strain-Tunable Magnetism

The coexistence of ferroelectricity and magnetism in a single-phase oxide is rare because the electronic requirements for these two orders are often incompatible. Here, using first-principles calculations and parallel-tempering Monte Carlo simulations, we propose stoichiometric VHfO$_4$ as a hafnia-derived multiferroic that overcomes this constraint through ordered cation design rather than dilute magnetic doping. We found that VHfO$_4$ could stabilize in a polar $Pca2_1$-like structure with layered V/Hf ordering, remains dynamically stable, and preserves switchable ferroelectricity with a large spontaneous polarization. The ordered V sublattice introduces competing exchange interactions that favor an antiferromagnetic ground state at zero strain. Epitaxial strain further drives transitions into additional phases, including a noncollinear spiral-like state and a predominantly in-plane antiferromagnetic state. We also find that out-of-plane lattice distortions along the polar axis strongly modify the exchange interactions and magnetic phase stability, indicating a strain-mediated pathway for electric-field control of magnetism. These results establish VHfO$_4$ as a promising platform for exploring multiferroicity and magnetoelectric coupling in hafnia-based oxides.

cond-mat.mtrl-sci↗

arXiv2026

A Pragmatic VLA Foundation Model

Offering great potential in robotic manipulation, a capable Vision-Language-Action (VLA) foundation model is expected to faithfully generalize across tasks and platforms while ensuring cost efficiency (e.g., data and GPU hours required for adaptation). To this end, we develop LingBot-VLA with around 20,000 hours of real-world data from 9 popular dual-arm robot configurations. Through a systematic assessment on 4 robotic platforms, each completing 100 tasks with 130 post-training episodes per task, our model achieves clear superiority over competitors, showcasing its strong performance and broad generalizability. We have also built an efficient codebase, which delivers a throughput of 261 samples per second with an 8-GPU training setup, representing a 1.5~2.8$\times$ (depending on the relied VLM base model) speedup over existing VLA-oriented codebases. The above features ensure that our model is well-suited for real-world deployment. To advance the field of robot learning, we provide open access to the code, base model, and benchmark data, with a focus on enabling more challenging tasks and promoting sound evaluation standards.

cs.RO↗

arXiv2026

Disentangling the Discrepancy Between Theoretical and Experimental Curie Temperatures in Ferroelectric PbTiO$_3$

Accurately predicting the Curie temperature ($T_c$) of ferroelectrics from first principles remains a major challenge, as theoretical estimates often fall significantly below experimental values. In this work, we investigate the origin of these discrepancies in the prototypical ferroelectric PbTiO$_3$ by performing extensive constant-pressure ab initio molecular dynamics (AIMD) simulations and benchmarking them against classical molecular dynamics (MD) using machine learning force fields (MLFFs) derived from first-principles data. Our results show that the underestimation of $T_c$ primarily stems from the limitations of the exchange-correlation functional, rather than inaccuracies in the MLFF fitting. We uncover a critical interplay between finite-size effects and the range of interatomic interactions: although short-range MLFFs appear to yield better agreement with experimental $T_c$, this improvement results from a fortuitous cancellation of errors. Incorporating explicit long-range interactions improves accuracy for larger supercells but ultimately leads to lower predicted $T_c$ values. These findings highlight that accurate finite-temperature predictions require not only high-quality training data and sufficiently large simulation cells, but also the explicit treatment of long-range interactions and improved exchange-correlation functionals.

cond-mat.mtrl-sci↗

arXiv2026

Origin of Anomalous Size Effects in Ferroelectric Hafnia Thin Films

The persistence of ferroelectricity in ultrathin HfO$_2$ films challenges conventional theories, particularly given the paradoxical observation that the out-of-plane lattice spacing increases as the film thickness decreases, an anomalous size effect absent in perovskite ferroelectrics. Here, we resolve this puzzle by revealing that this lattice expansion is counterintuitively coupled to a suppressed out-of-plane polarization. First-principles calculations combined with analytical modeling identify two mechanisms behind this expansion: a negative longitudinal piezoelectric response to the residual depolarization field and a positive surface stress that becomes significant at reduced thickness. Their interplay quantitatively reproduces the experimentally observed lattice expansion. Furthermore, (111)-oriented HfO$_2$ films can support out-of-plane polarization even under open-circuit conditions, in contrast to (001) films that stabilize a nonpolar ground state. This behavior points to the emergence of orientation-induced hyperferroelectricity, an unrecognized mechanism that enables polarization persistence through orientation engineering without electrode screening. We further demonstrate that this principle generalizes to conventional perovskites such as PbTiO$_3$, offering a strategy to eliminate the critical thickness limit by choosing the appropriate film orientation. As a practical pathway to device integration, we also identify the two-dimensional electride Ca$_2$N as a near-ideal electrode that fully restores the ferroelectric properties of HfO$_2$ in ultrathin capacitors.

cond-mat.mtrl-sci↗

arXiv2026

Polaron Transport in TiO$_{2}$ from Machine Learning Molecular Dynamics

Transition metal oxides have attracted much attention as photo(electrochemical)-catalysts but practical applications are typically hampered by their low and anisotropic charge mobility. A deep understanding of excess charge carrier transport in these materials requires a dynamical treatment of nuclear motion that goes well beyond standard approaches. Here we introduce DeepPolaron, a machine learning framework boosting the accessible time scale of first principles molecular dynamics of adiabatic polaron transport by three orders of magnitude at a virtually negligible loss in accuracy. We apply our method to excess electron and hole transport in titanium dioxide rutile and anatase. We find that the excess electron in rutile relaxes to a polaron predominantly localized on a single Ti atom with hopping occurring only along the [001] direction, associated with an activation energy of 39 meV and a room temperature mobility of $4.4 \times 10^{-2}$ cm$^2$/Vs in good agreement with experiment. In contrast the hole polaron in anatase is localized on a single O atom, and due to poor O 2p orbital overlap with first nearest neighbors charge transport occurs primarily to second nearest neighbors, with a large activation energy of 139 meV resulting in a small room temperature mobility of $1.4 \times 10^{-3}$ cm$^2$/Vs. This work provides a finite temperature first-principles characterization of small polaron transport in rutile and anatase, with a methodology that is directly transferable to other small polaron forming materials and interfacial charge-transfer processes.

cond-mat.mtrl-sci↗

arXiv2026

Giant Nonlinear Photon-Drag Currents in Moiré Bilayers

The bulk photovoltaic effect provides a fundamental pathway for direct light-to-current conversion in quantum materials. However, these nonlinear currents are often strictly constrained or forbidden by crystal symmetries, hindering their exploration in a broader range of materials. While the nonlinear photon-drag effect leverages finite photon momentum to circumvent these constraints, its investigation has been largely confined to toy models, lacking a robust numerical framework for realistic materials. Here, we develop a unified microscopic theory of nonlinear photon-drag currents formulated within a geometric-loop framework, providing both a transparent quantum-geometric interpretation and numerical tractability. Applying this formalism to twisted bilayer graphene (TBG), we demonstrate that a finite, in-plane photon momentum can trigger massive nonlinear responses, rivaling the giant photovoltaic currents reported in typical 2D materials. These currents exhibit high tunability via photon wavevector, twist angle, and light polarization. Our work not only provides a generalized framework for momentum-dependent light-matter interactions but also establishes the nonlinear photon-drag effect as a potent mechanism for unlocking unprecedented optoelectronic functionalities beyond the limitations of the conventional bulk photovoltaic effect.

cond-mat.mes-hall↗

arXiv2026

When the Manual Lies: A Realistic Benchmark to Evaluate MCP Poisoning Attacks for LLM Agents

The rise of tool-using Large Language Model (LLM) agents, standardized by protocols like the Model Context Protocol (MCP), has unlocked unprecedented autonomous execution capabilities for LLM Agents by integrating external open-domain knowledge and tools. However, this interoperability introduces a covert attack surface targeting the agent's cognitive planning layer. This paper systematically investigates Tool Description Poisoning (TDP), a novel semantic attack. In TDP, malicious instructions are not embedded in a tool's executable code, but rather covertly injected into its descriptive metadata, the very "manual" an agent relies on for secure planning and decision-making. To rigorously and systematically evaluate this emerging threat, we introduce the MCP-TDP Security Benchmark. This high-fidelity sandbox environment comprises 32 realistic, real-world test cases spanning 6 distinct risk categories. Our evaluation of 8 mainstream LLMs reveals severe vulnerabilities, with leading models like GPT-4o exhibiting a nearly 100% Attack Success Rate (ASR) in six high-risk scenarios. Furthermore, our findings demonstrate that common prompt-guardrail defenses are largely ineffective and can, counterintuitively, even be counterproductive (a phenomenon which we term the "Firewall Fallacy"). Crucially, we also propose a defense mechanism: "Reactive Self-Correction," where an agent autonomously detects and reverts its own malicious actions post-execution. This work provides the first specialized security benchmark tailored for TDP, offering essential insights for securing the cognitive and planning layers of advanced agentic systems.

cs.CR↗