arXiv subjects

Shi Liu

Publications and source records attributed to Shi Liu.

At least 37 records · Page 2Linked to original sources

arXiv2026

Unlocking Static Polarization and Strain Density Waves in Perovskites by Softening a Hidden Antiferrodistortive Tilt Gradient Mode

Spin density waves (SDWs) represent a fundamental paradigm of spatially modulated order in condensed matter systems, yet their electrical and mechanical analogues polarization and strain density waves (PDWs and StDWs) have remained elusive as equilibrium phases. Here, we introduce a general, symmetry-driven strategy to unlock static PDWs and StDWs in perovskites SrTiO3 and SrMnO3. Using first-principles calculations, we uncover a previously overlooked soft antiferrodistortive tilt gradient mode at small-q wavevector in the phonon dispersion of their presumed Ima2 ground state under moderate tensile strain. Group-theory analysis reveals that a hard polaracoustic phonon, which intrinsically carries PDWs and StDWs, is improperly destabilized by a trilinear coupling with this modulated tilt mode and an inherently uniform tilt mode. This interaction drives a structural transition from the Ima2 phase to a novel lower-energy Pmn21 phase that hosts long-range-ordered PDWs and StDWs. Strikingly, the engineered StDWs in SrMnO3 activate an electrically tunable SDW via the flexomagnetic effect. These discoveries fundamentally revise the strain-phase diagrams of prototypical perovskites and establish a unified phonon-engineering framework that links modulated phonon instabilities to targeted density-wave order, offering new pathways for designing advanced electromechanical and magnetoelectric functionalities.

cond-mat.mtrl-sci↗

arXiv2026

GPUMDkit: A User-Friendly Toolkit for GPUMD and NEP

Machine-learned interatomic potentials have revolutionized molecular dynamics simulations by providing quantum-mechanical accuracy at empirical-potential speeds. The graphics processing unit molecular dynamics (GPUMD) package, featuring the highly efficient neuroevolution potential (NEP) framework, has emerged as a powerful tool in this domain. However, the complexity of force field development, active learning, and trajectory post-processing often requires extensive manual scripting, imposing a steep learning curve on new users. To address this, we present GPUMDkit, a comprehensive and user-friendly toolkit that streamlines the entire simulation workflow for GPUMD and NEP. GPUMDkit integrates a suite of essential functionalities, including format conversion, structure sampling, property calculation, and data visualization, accessible through both interactive and command-line interfaces. Its modular, extensible architecture ensures accessibility for users of all experience levels while allowing seamless integration of new features. By automating complex tasks and enhancing productivity, GPUMDkit substantially lowers the barrier to using GPUMD and NEP programs. This article describes the program architecture and demonstrates its capabilities through practical applications.

cond-mat.mtrl-sci↗

arXiv2026

CoEmpaTeam: Enhancing Cognitive Empathy using LLM-based Avatars and Dynamic Role Play in Virtual Reality

Cognitive empathy, the ability to understand others' perspectives, is essential for effective communication, reducing biases, and constructive negotiation. However, this skill is declining in a performance-driven society, which prioritizes efficiency over perspective-taking. Here, the training of cognitive empathy is challenging because it is a subtle, hard-to-perceive soft skill. To address this, we developed CoEmpaTeam, a VR-based system that enables users to train their cognitive empathy by using LLM-driven avatars with different personalities. Through dynamic role play, users actively engage in perspective-taking, experiencing situations through another person's eyes. CoEmpaTeam deploys three avatars who significantly differ in their personality, validated by a technical evaluation and an online experiment (n=90). Next, we evaluated the system through a lab experiment with 32 participants who performed three sessions across two weeks, followed by a one-week diary study. Our results showed a significant increase in cognitive empathy, which, according to participants, transferred into their real lives.

cs.HC↗

arXiv2026

AttentiveLearn: Personalized Post-Lecture Support for Gaze-Aware Immersive Learning

Immersive learning environments such as virtual classrooms in Virtual Reality (VR) offer learners unique learning experiences, yet providing effective learner support remains a challenge. While prior HCI research has explored in-lecture support for immersive learning, little research has been conducted to provide post-lecture support, despite being critical for sustained motivation, engagement, and learning outcomes. To address this, we present AttentiveLearn, a learning ecosystem that generates personalized quizzes on a mobile learning assistant based on learners' attention distribution inferred using eye-tracking in VR lectures. We evaluated the system in a four-week field study with 36 university students attending lectures on Bayesian data analysis. AttentiveLearn improved learners' reported motivation and engagement, without conclusive evidence of learning gains. Meanwhile, anecdotal evidence suggested improvements in attention for certain participants over time. Based on our findings of the field study, we provide empirical insights and design implications for personalized post-lecture support for immersive learning systems.

cs.HC↗

arXiv2026

Decoupling effects of the resistive-switching behavior on the polarization reversal in ultrathin ferroelectric Hf0.5Zr0.5O2 films

HfO2-based ferroelectric films have attracted considerable attention as their nanoscale ferroelectricity and compatibility with cmos technology, fulfilling demands of emerging memory technologies. However, as films scale down, resistive-switching behavior becomes increasingly pronounced, intricately intertwining with the polarization-switching process and affecting ferroelectric switching factors often overlooked yet crucial for device performance optimization. By characterizing resistive-switching behavior and oxygen vacancy motion using tailored electric pulse schemes, we decouple the resistive-switching behavior from the overall switching process in ultrathin ferroelectric HZO films, which would otherwise erroneously inflate polarization values and increase coercive fields. Building on this, we elucidate endurance degradation mechanisms from dual perspectives of resistive switching and defect migration. Furthermore, we demonstrate the mitigated resistive switching activity by designing HfO2-based devices with symmetric oxide electrodes, achieving reduced coercive fields and improved cycling performances. This work provides crucial insights into the origins of inflated polarizations and reliability challenges in HfO2-based devices while offering a viable strategy to enhance ferroelectric properties for advanced memory applications.

cond-mat.mtrl-sci↗

arXiv2026

Tunable spin-phonon polarons in a chiral molecular qubit framework

Chiral structures that produce asymmetric spin-phonon coupling can theoretically generate spin-phonon polarons -- quasiparticles exhibiting non-degenerate spin states with phonon displacements. These quasiparticles are speculated to be the origin of chirality-induced spin selectivity and presumably can display exotic dynamic behaviors. However, direct experimental evidence of spin-phonon polarons has been lacking. Using a chiral molecular qubit framework embedding stable semiquinone-like radicals, we report spin dynamic signatures that indicate the formation of spin-phonon polarons for the first time. Our non-adiabatic model reveals that these quasiparticles introduce an active spin relaxation channel when polaron reorganization energy approaches Zeeman splitting. This new channel manifests itself as anomalous, temperature-independent spin relaxation, which can be suppressed by high magnetic fields or pore-filling solvents (e.g. CH2Cl2, CS2). Such field- and guest-tunable relaxation is unattainable in conventional spin systems. Harnessing this mechanism could boost repetition rates in spin-based quantum information technologies without compromising coherence or quantum sensing performance.

cond-mat.mes-hall↗

arXiv2026

Electrical Regulation of Transverse Spin Currents in Unconventional Magnetic Ferroeletrics

We identify hexagonal YMnO$_3$ as a material realization of the elusive $β$-phase of unconventional magnetism, a noncollinear, noncoplanar antiferromagnetic state defined by intrinsic spin-momentum locking and a topological spin texture. First-principle calculations reveal that this unique electronic structure enables a perpendicular electric field to generate a transverse pure spin current, a response that occurs without requiring relativistic spin-orbit coupling. Symmetry analysis demonstrates that this spin current is intimately related to the material's ferroelectric polarization that breaks the inversion symmetry and is rigorously forbidden at domain walls where electrical polarization vanishes. This provides a blueprint for a non-volatile transistor where a gate voltage switches the spin current conductivity by controlling domain wall density, enabling all-electrical control for energy-efficient antiferromagnetic spintronics.

cond-mat.mtrl-sci↗

arXiv2025

Are Foundational Atomistic Models Reliable for Finite-Temperature Molecular Dynamics?

Machine learning force fields have emerged as promising tools for molecular dynamics (MD) simulations, potentially offering quantum-mechanical accuracy with the efficiency of classical MD. Inspired by foundational large language models, recent years have seen considerable progress in developing foundational atomistic models, sometimes referred to as universal force fields, designed to cover most elements in the periodic table. This Perspective adopts a practitioner's viewpoint to ask a critical question: Are these foundational atomistic models reliable for one of their most compelling applications, in particular simulating finite-temperature dynamics? Instead of a broad benchmark, we use the canonical ferroelectric-paraelectric phase transition in PbTiO$_3$ as a focused case study to evaluate prominent foundational atomistic models. Our findings suggest a potential disconnect between static accuracy and dynamic reliability. While 0 K properties are often well-reproduced, we observed that the models can struggle to consistently capture the correct phase transition, sometimes exhibiting simulation instabilities. We believe these challenges may stem from inherent biases in training data and a limited description of anharmonicity. These observed shortcomings, though demonstrated on a single system, appear to point to broader, systemic challenges that can be addressed with targeted fine-tuning. This Perspective serves not to rank models, but to initiate a crucial discussion on the practical readiness of foundational atomistic models and to explore future directions for their improvement.

physics.comp-ph↗

arXiv2025

Topological Control of Transition Metal Networks for Reversible High-Capacity Li-rich Cathodes

Developing high-energy-density batteries is essential for advancing sustainable energy technologies. However, leading cathode materials such as Li-rich oxides, including Li$_2$MnO$_3$, suffer from capacity loss due to irreversible oxygen release and structural degradation, both consequences of the oxygen redox activity that also enables their high capacity. The atomic-scale mechanisms behind this degradation, and whether it can be made reversible, remain open questions. Here, using submicrosecond-scale molecular dynamics simulations with first-principles accuracy, we directly visualize the entire charge-discharge cycle of Li$_2$MnO$_3$, uncovering the full lifecycle of the O$_2$-filled nanovoids responsible for degradation and identifying the critical size limit for voids to remain fully repairable upon discharge. Our results reveal that the topology of the Mn cation network is the key factor governing void growth, coalescence, and reparability. Based on a structural topology-informed design principle, we computationally develop a novel Li$_2$MnO$_3$ structure featuring a Mn lattice with a Kagome-like pattern, demonstrating full electrochemical reversibility even under extreme 80% delithiation. Our work establishes a new paradigm for designing high-energy cathodes, shifting the focus from mitigating damage to engineering inherent stability through atomic-level topological control of transition metal network.

cond-mat.mtrl-sci↗

arXiv2025

ABACUS: An Electronic Structure Analysis Package for the AI Era

ABACUS (Atomic-orbital Based Ab-initio Computation at USTC) is an open-source software for first-principles electronic structure calculations and molecular dynamics simulations. It mainly features density functional theory (DFT) and molecular dynamics functions and is compatible with both plane-wave basis sets and numerical atomic orbital basis sets. ABACUS serves as a platform that facilitates the integration of various electronic structure methods, such as Kohn-Sham DFT, stochastic DFT, orbital-free DFT, and real-time time-dependent DFT, etc. In addition, with the aid of high-performance computing, ABACUS is designed to perform efficiently and provide massive amounts of first-principles data for generating general-purpose machine learning potentials, such as DPA models. Furthermore, ABACUS serves as an electronic structure platform that interfaces with several AI-assisted algorithms and packages, such as DeePKS-kit, DeePMD, DP-GEN, DeepH, DeePTB, HamGNN, etc.

cond-mat.mtrl-sci↗

arXiv2025

Rippled Moire Superlattices for Decoupled Ferroelectric Bits

Symmetry considerations suggest that moire superlattices formed by twisted two-dimensional materials should preserve overall inversion symmetry. However, experiments consistently report robust ferroelectricity in systems such as twisted bilayer h-BN, posing a fundamental discrepancy between theory and experiment regarding its microscopic origin. Here, using large-scale finite-field molecular dynamics simulations, we challenge the prevailing defect-pinning hypothesis and instead identify an out-of-plane bending field, induced by in-plane compressive strain, as the key symmetry-breaking mechanism. This strain-induced rippling drives spatially heterogeneous interlayer sliding and distorts the moire domain wall network, resulting in a four-state ferroelectric system. Remarkably, we show this mechanism can be harnessed at the nanoscale, where localized nanobubbles designate the moire lattice's fundamental hexagonal domain clusters as the smallest individually addressable ferroelectric bits, thereby imposing local control on an otherwise globally defined structure. Our findings establish a geometry-driven framework for understanding and engineering moire ferroelectrics, offering not only a route toward ultra-high-density, rewritable memory, but also a strategy for locally tuning the moire potential itself, a critical step for manipulating emergent correlated and topological quantum phases.

physics.comp-ph↗

arXiv2025

ScaleCUA: Scaling Open-Source Computer Use Agents with Cross-Platform Data

Vision-Language Models (VLMs) have enabled computer use agents (CUAs) that operate GUIs autonomously, showing great potential, yet progress is limited by the lack of large-scale, open-source computer use data and foundation models. In this work, we introduce ScaleCUA, a step toward scaling open-source CUAs. It offers a large-scale dataset spanning 6 operating systems and 3 task domains, built via a closed-loop pipeline uniting automated agents with human experts. Trained on this scaled-up data, ScaleCUA can operate seamlessly across platforms. Specifically, it delivers strong gains over baselines (+26.6 on WebArena-Lite-v2, +10.7 on ScreenSpot-Pro) and sets new state-of-the-art results (94.4% on MMBench-GUI L1-Hard, 60.6% on OSWorld-G, 47.4% on WebArena-Lite-v2). These findings underscore the power of data-driven scaling for general-purpose computer use agents. We will release data, models, and code to advance future research: https://github.com/OpenGVLab/ScaleCUA.

cs.CV↗

arXiv2025

Dipolar Nematic State in Relaxor Ferroelectrics

Relaxor ferroelectrics exhibit exceptional dielectric and electromechanical properties, yet their microscopic origins remain elusive due to the interplay of hierarchical polar structures and chemical complexity. While models based on polar nanoregions or nanodomains offer valuable phenomenological insights, they often lack the first-principles predictive capability necessary for quantitatively describing functional properties such as piezoelectric coefficients. Here, we use large-scale molecular dynamics simulations, enabled by a universal first-principles-based machine-learning interatomic potential, to investigate atomic-scale polar dynamics in canonical Pb-, Bi-, and Ba-based relaxors. Across all systems, we uncover a universal dipolar nematic state, characterized by long-range orientational order of local polarizations without local alignment, challenging conventional polar cluster-based paradigms. We introduce a universal order parameter, derived from the skewness of the distributions of the local polarization autocorrelation functions, that captures the thermal evolution of both lead-based and lead-free systems within a single master curve. This nematic order, and its robust structural memory under electric field cycling, underpins key relaxor phenomena, including diffuse phase transition, frequency-dependent dielectric dispersion, and reversible giant piezoelectricity. Our findings establish a unified microscopic framework for relaxors and present a broadly applicable statistical approach to understanding complex disordered materials.

cond-mat.mtrl-sci↗

arXiv2025

Atomistic Structure of Transient Switching States in Ferroelectric AlScN

We resolve the microscopic mechanism of polarization switching in wurtzite ferroelectric AlScN by integrating advanced thin-film fabrication, ferroelectric switching dynamics characterizations, high-resolution scanning transmission electron microscopy (STEM), and large-scale molecular dynamics simulations enabled by a deep neural network-based interatomic potential. Contrary to earlier interpretations proposing a transient nonpolar intermediate phase, we demonstrate that the broad transitional regions previously observed in STEM images are projection artifacts resulting from the intrinsic three-dimensional zigzag morphology of 180$^\circ$ domain walls, which are a characteristic form of inversion domain boundary. This is further confirmed by STEM imaging of strategically prepared, partially switched Al$_{0.75}$Sc$_{0.25}$N thin films. Our simulations reveal that switching proceeds through collective, column-by-column atomic displacements, directly explaining the emergence of zigzag-shaped domain walls, and is consistent with the nucleation-limited switching behavior observed in experimental switching dynamic measurements. Furthermore, we show that increasing Sc content systematically lowers domain wall energy and associated nucleation barrier, thereby reducing the switching field in agreement with experimental trends. These findings establish a direct connection between local domain wall structure, switching kinetics, and macroscopic ferroelectric behavior.

cond-mat.mtrl-sci↗

arXiv2025

Unraveling Shear Strain Induced Ferroelectric-to-Antiferroelectric Phase Transition and Accessing Intrinsic Antiferroelectricity in Two-dimensional NbOCl2

Compared to the well studied two-dimensional (2D) ferroelectricity, much rare is the appearance of 2D antiferroelectricity, where local dipoles from the nonequivalent sublattices within 2D monolayers are oppositely orientated. Using NbOCl2 monolayer with competing ferroelectric (FE) and antiferroelectric (AFE) phases as a 2D material platform, we demonstrate the emerging of intrinsic antiferroelectricity in NbOCl2 monolayer under the experimentally accessible shear strain, and new functionality associated with electric field induced AFE-to-FE phase transition. Specifically, the complex configuration space accommodating FE and AFE phases, polarization switching kinetics and finite temperature thermodynamic properties of 2D NbOCl2, are all accurately predicted by large-scale molecular dynamic (MD) simulations based on deep learning interatomic potential (DP) model. Moreover, room temperature stable antiferroelectricity with low polarization switching barrier and one-dimensional (1D) collinear polarization arrangement is predicted in shear deformed NbOCl2 monolayer. Transition from AFE to FE phase in 2D NbOCl2 can be triggered by the low critical electric field, leading to the double polarization-electric (P-E) loop with small hysteresis. A new type optoelectronic device composed of AFE-NbOCl2, enabling electric "writing" and nonlinear optical "reading" logical operation with fast operation speed and low power consumption is also proposed.

cond-mat.mtrl-sci↗

arXiv2025

Double Helix of atomic displacements in Ferroelectric PbTiO$_3$

Recent theoretical work has predicted the existence of a "dipole spiral" structure in strained freestanding membranes of PbTiO$_3$, suggesting a potential route to enhanced electromechanical responses [\href{https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.133.046802}{PRL \textbf{133}, 046802 (2024)}]. However, its microscopic nature, energetic landscape, and electronic properties remain largely unexplored from a first-principles perspective. Here, using density function theory on PbTiO$_3$ under biaxial tensile strain, we identify a novel form of polar order: a chiral, non-collinear ferroelectric double helix. We find that the Pb- and Ti-cation sublattices form two distinct, intertwined helices, reminiscent of DNA. This topology is stabilized by a collective helical twisting of the oxygen octahedral framework, which gives rise to an electric Dzyaloshinskii-Moriya-like interaction. The resulting structure, which can be canceptualized as a "self-Moiré" crystal, exhibits two coupled functionalities. First, it possesses a rotational pseudo-zero-energy mode that underpins a giant piezoelectric response ($e_{33}\approx$16 C/m$^2$). Second, the long-period potential reconstructs the electronic band structure, leading to a multi-valley electronic topology at the valence band edge. Our work establishes a physical route to designing complex chiral order that supports both giant electromechanical coupling and multi-valley electronics.

cond-mat.mtrl-sci↗

arXiv2025

Giant Reversible Piezoelectricity from Symmetry-Governed Stochastic Dipole Hopping

Organic--inorganic hybrid perovskites with giant piezoelectric responses, exemplified by TMCM-CdCl$_3$, represent a promising platform for flexible and environmentally friendly electromechanical materials. However, the microscopic origin of such exceptional performance in this weakly polar system has remained elusive. Here, using deep-learning-assisted large-scale molecular dynamics simulations, we resolve this paradox by reproducing the experimentally measured piezoelectric coefficient $d_{33} \approx 220$~pC/N, and demonstrating that the giant response arises from the collective contribution of multiple intrinsic components, particularly the shear component $d_{15}$. This effect does not stem from conventional polarization rotation or phase switching, but instead originates from stochastic 120$^\circ$ in-plane rotational hopping of a small fraction of organic cations. This discrete hopping mechanism is governed by the local C$_3$-symmetric halogen-bonding network between the host framework and the guest cation. The Arrhenius-type temperature dependence of $d_{15}$ further confirms the role of thermally activated dipole hopping. This work provides a clear pathway to enhance piezoelectric performance of hybrid materials through rational engineering of host--guest interactions.

cond-mat.mtrl-sci↗

arXiv2025

Paper Summary Attack: Jailbreaking LLMs through LLM Safety Papers

The safety of large language models (LLMs) has garnered significant research attention. In this paper, we argue that previous empirical studies demonstrate LLMs exhibit a propensity to trust information from authoritative sources, such as academic papers, implying new possible vulnerabilities. To verify this possibility, a preliminary analysis is designed to illustrate our two findings. Based on this insight, a novel jailbreaking method, Paper Summary Attack (\llmname{PSA}), is proposed. It systematically synthesizes content from either attack-focused or defense-focused LLM safety paper to construct an adversarial prompt template, while strategically infilling harmful query as adversarial payloads within predefined subsections. Extensive experiments show significant vulnerabilities not only in base LLMs, but also in state-of-the-art reasoning model like Deepseek-R1. PSA achieves a 97\% attack success rate (ASR) on well-aligned models like Claude3.5-Sonnet and an even higher 98\% ASR on Deepseek-R1. More intriguingly, our work has further revealed diametrically opposed vulnerability bias across different base models, and even between different versions of the same model, when exposed to either attack-focused or defense-focused papers. This phenomenon potentially indicates future research clues for both adversarial methodologies and safety alignment.Code is available at https://github.com/233liang/Paper-Summary-Attack

cs.CL↗