arXiv subjects

Zhenzhen Qin

Publications and source records attributed to Zhenzhen Qin.

At least 19 recordsLinked to original sources

arXiv2026

Making AI Drafts Count: A Quality Threshold in Audio Description Workflows

Audio description (AD) narrates visual elements in video for blind and low-vision audiences. Recent work has shown that giving novice describers an AI-generated draft to start from helps produce higher-quality AD and lowers the barrier to entry. What remains an open question is how draft quality shapes the editing process. We investigate this through GenAD, an AD generation pipeline that incorporates accessibility guidelines and contextual video information, and RefineAD, an editing interface for human revisions. Human-AI contributions are measured across text, timing, and delivery. In a within-subjects study, we compared authoring from scratch against editing AI drafts of varying quality. GenAD drafts cut completion time by more than half and significantly reduced cognitive load. In contrast, baseline drafts generated from simple, unguided prompts offered only modest benefits, pointing to a minimum quality threshold for effectiveness. Qualitative findings suggest this threshold is content-dependent; as visual complexity increases, so does the quality needed from AI drafts. We propose this as a design principle: effective AI assistance should clear a quality threshold suited to the target content, rather than simply be present.

cs.HC↗

arXiv2026

C-Mining: Unsupervised Discovery of Seeds for Cultural Data Synthesis via Geometric Misalignment

Achieving cultural alignment in Large Language Models (LLMs) increasingly depends on synthetic data generation. For such synthesis, the most vital initial step is seed curation; however, current methods lack quantifiable standards for selecting these seeds. Existing approaches rely on unscalable manual curation or bias-prone LLM extraction, treating cultural specificity as an abstract concept rather than a measurable signal. In this paper, we address this "quantification gap" by proposing C-Mining, an unsupervised framework that transforms the discovery of cultural seeds from a subjective selection process into a computable data mining formulation. Our approach exploits a novel geometric insight, leveraging the cross-lingual misalignment of cultural concepts within pre-trained embedding spaces as a quantifiable discovery signal. By systematically identifying these regions characterized by pronounced linguistic exclusivity and geometric isolation, while actively filtering out noise, C-Mining automatically extracts high-fidelity Culture Points (CPs) from raw multilingual corpora without reliance on human or LLM supervision, reducing preparation costs by more than 150-fold. We further leverage the mined knowledge to steer the synthesis of diverse instruction-tuning datasets. Extensive experiments demonstrate that this seed-centric approach significantly enhances cultural understanding and reasoning capabilities, achieving a +6.03 point improvement on CulturalBench-Hard and surpassing state-of-the-art baselines, providing a scalable, quantifiable solution for high-quality cultural data synthesis.

cs.CL↗

arXiv2026

Toward Scalable Patient Safety Training: A Prototype for Root Cause Analysis Simulation With AI Virtual Avatars

Patient safety training is essential for preparing healthcare professionals to identify, investigate, and prevent adverse events. However, conventional simulation-based approaches often require substantial faculty time, physical resources, and standardized facilitation. This paper presents a prototype AI-powered simulation platform designed to support more scalable patient safety training through root cause analysis (RCA). The system provides a Unity-based 3D simulation environment, which allows trainees to investigate an ICU adverse event by interviewing five virtual team members represented as AI-powered avatars. Each avatar is driven by a large language model (LLM) agent with role-specific knowledge and variable states of mind. Moreover, emotional text-to-speech and AI-supported facial and body animation enable more realistic and immersive interactions. After completing the simulation, trainees submit a written RCA report and receive rubric-guided formative and summative feedback automatically generated by an LLM-based assessment component. The prototype is built to support patient safety training for healthcare professionals, focusing on skills in communication, investigation, thinking, and analysis, with low recurring instructional burden. We describe the design of the platform, its core technical components, and an RCA case based on a published ICU scenario. This work demonstrates the feasibility of integrating generative AI into immersive simulation for scalable patient safety education.

cs.HC↗

arXiv2025

Two-dimensional Rashba semiconductors and inversion-asymmetric topological insulators in monolayer Janus MAA'ZxZ'(4-x) family

The Rashba effect in Janus structures, accompanied by nontrivial topology, plays an important role in spintronics and even photovoltaic applications. Herein, through first-principles calculations, we systematically investigate the geometric stability and electronic structures of 135 kinds of Janus MAA'ZxZ'(4-x) family derived from two-dimensional MA2Z4 (M=Mg, Ga, Sr; A=Al, Ga; Z=S, Se, Te) monolayers, and design numerous Rashba semiconductors and inversion-asymmetric topological insulators. Specifically, there are a total of 26 Rashba semiconductors with isolated spin splitting bands contributed by Se/Te-pz orbitals at conduction band minimum, and the magnitude of the Rashba constant correlates strongly with both the intrinsic electric field and the strength of spin-orbit coupling (SOC). As the atomic number increases, the bandgap of Janus MAA'ZxZ'(4-x) continually decreases until it shrinks to a point where, when SOC is considered, band inversion occurs, leading to a reopening of the bandgap with nontrivial topological phases. In conjunction with band inversion, pz orbitals near the Fermi level can introduce double Rashba splitting featuring a distinctive hybrid spin texture, which can be further effectively adjusted through small biaxial strains and show a continuous evolution of topological to non-topological accompanied by different spin textures. This work provides significant insights into Rashba and topology physics and further presents indispensable inversion asymmetry materials for the development of nonlinear optoelectronics.

physics.app-ph↗

arXiv2025

Rashba effect modulation in two-dimensional A2B2Te6 (A = Sb, Bi; B = Si, Ge) materials via charge transfer

Designing two-dimensional (2D) Rashba semiconductors, exploring the underlying mechanism of Rashba effect, and further proposing efficient and controllable approaches are crucial for the development of spintronics. On the basis of first-principles calculations, we here theoretically design all possible types (common, inverse, and composite) of Janus structures and successfully achieve numerous ideal 2D Rashba semiconductors from a series of five atomic-layer A2B2Te6 (A = Sb, Bi; B = Si, Ge) materials. Considering the different Rashba constant αR and its modulation trend under external electric field, we comprehensively analyze the intrinsic electric field Ein in terms of work function, electrostatic potential, dipole moment, and inner charge transfer. Inspired by the quantitative relationship between charge transfer and the strength of Ein and even the αR, we propose a straightforward strategy of introducing a single adatom onto the surface of 2D monolayer to introduce and modulate the Rashba effect. Lastly, we also examine the growth feasibility and electronic structures of the Janus Sb2Ge2Se3Te3 system and Janus-adsorbed systems on a 2D BN substrate. Our work not only conducts a detailed analysis of A2B2Te6-based Rashba systems, but also proposes a new strategy for efficiently and controllably modulating the αR through the reconfiguration of charge transfer.

cond-mat.mes-hall↗

arXiv2024

Asymmetric electron distribution induced intrinsically strong anisotropy of thermal transport in bulk CrOCl

Anisotropic heat transfer offers promising solutions to the efficient heat dissipation in the realm of electronic device thermal management. However, the fundamental origin of the anisotropy of thermal transport remains mysterious. In this paper, by combining frequency domain thermoreflectance (FDTR) technique and first-principles-based multiscale simulations, we report the intrinsic anisotropy of thermal transport in bulk CrOCl, and further trace the origin of the anisotropy back to the fundamental electronic structures. The in-plane and cross-plane thermal conductivities ($κ$) at 300 K are found to be 21.6 and 2.18 Wm$^{-1}$K$^{-1}$, respectively, showcasing a strong $κ_\mathrm{in-plane}/κ_\mathrm{cross-plane}$ ratio of $\sim$10. Deep analysis of orbital-resolved electronic structures reveals that electrons are mainly distributed along the in-plane direction with limited interlayer distribution along the cross-plane direction, fundamentally leading to the intrinsic anisotropy of thermal transport in bulk CrOCl. The insight gained in this work sheds light on the design of advanced thermal functional materials.

physics.app-ph↗

arXiv2024

Neural network study on nuclear ground-state spin distribution within random interaction ensemble

The distribution of nuclear ground-state spin in the two-body random ensemble (TBRE) is studied by using a general classification neural network (NN) model with the two-body interaction matrix elements as input features and corresponding ground-state spins as labels or output predictions. It seems that quantum many-body system problem exceeds the capability of our optimized neural networks when it comes to accurately predicting the ground-state spin of each sample within the TBRE. However, our neural network model effectively captures the statistical properties of the ground-state spin. This may be attributed to the fact that the neural network (NN) model has learned the empirical regularity of the ground-state spin distribution in TBRE, as discovered by human physicists.

nucl-th↗

arXiv2024

Superfolded configuration induced low thermal conductivity in two-dimensional carbon allotropes revealed via machine learning force constant potential

Understanding the fundamental link between structure and functionalization is crucial for the design and optimization of functional materials, since different structural configurations could trigger materials to demonstrate diverse physical, chemical, and electronic properties. However, the correlation between crystal structure and thermal conductivity (\k{appa}) remains enigmatic. In this study, taking two-dimensional (2D) carbon allotropes as study cases, we utilize phonon Boltzmann transport equation (BTE) along with machine learning force constant potential to thoroughly explore the complex folding structure of pure sp2 hybridized carbon materials from the perspective of crystal structure, mode-level phonon resolved thermal transport, and atomic interactions, with the goal of identifying the underlying relationship between 2D geometry and \k{appa}. We propose two potential structure evolution mechanisms for targeted thermal transport properties: in-plane and out-of-plane folding evolutions, which are generally applicable to 2D carbon allotropes. It is revealed that the folded structure produces strong symmetry breaking, and simultaneously produces exceptionally strongly suppressed phonon group velocities, strong phonon-phonon scattering, and weak phonon hydrodynamics, which ultimately lead to low \k{appa}. The insight into the folded effect of atomic structures on thermal transport deepens our understanding of the relationship between structure and functionalization, which offers straightforward guidance for designing novel nanomaterials with targeted \k{appa}, as well as propel developments in materials science and engineering.

cond-mat.mtrl-sci↗

arXiv2024

A promising candidate for ising ferromagnetism of two-dimensional kagome V$_2$O$_3$ honeycomb monolayer

Due to the low dimensionality in the quantization of the electronic states and degree of freedom for device modulation, two-dimensional (2D) ferromagnetism plays a critical role in lots of fields. In this study, we perform first-principles calculation to investigate the ising ferromagnetism and half-metallicity of kagome V$_2$O$_3$ monolayer. Based on the calculations using different functional, it is found that GGA-PBE gives a half-metallic band gap while the GGA+U gives a semiconductor narrow band gap (~1.1 meV), which shows quasi-half metallic nature. By studying the magnetic properties with LDA, GGA-PBE, and GGA+U, we get a robust ferromagnetic ground state, where the giant perpendicular magnetic anisotropy energy of ~0.544 meV is achieved by applying the spin-orbit coupling (SOC) with GGA+U. Furthermore, by exploring the orbital contribution to the electronic bands and the magnetic crystalline anisotropy, it is uncovered that the 3d (V) orbitals contribute to the out-of-plane. The electronic band structure shows two flat bands (F1 and F2) and Dirac points (D1 and D2) which further confirm that kagome V$_2$O$_3$ ML can also be used for topological properties. Besides, the Curie temperature of the V$_2$O$_3$ ML is calculated to be 640 K by Metropolis Monte Carlo (MC) simulations.

cond-mat.mtrl-sci↗

arXiv2024

Wave-graphene: a full-auxetic carbon semiconductor with high flexibility and optical UV absorption

The abundant bonding possibilities of Carbon stimulate the design of numerous carbon allotropes, promising the foundation for exploring structure-functionality relationships. Herein, utilizing the space bending strategy, we successfully engineered a two-dimensional carbon allotrope with pure sp2 hybridization, named "Wave-graphene" from the unique wave-like ripple structure. The novel Wave-graphene exhibits full-auxetic behavior due to anisotropic mechanical response, possessing both negative and zero Poisson's ratios. The fundamental mechanism can be attributed to the fact that highly buckled out-of-plane structures lead to anisotropic responses of in-plane nonlinear interactions, which further lead to anisotropy of lattice vibrations. In addition, Wave-graphene is found having quasi-direct wide bandgap of 2.01 eV, the excellent optical transparency and the high flexibility. The successful design of Wave-graphene with excellent outstanding multifunctional properties shows that the utilization of space bending strategies can provide more degrees of freedom for designing novel materials, further enriching the carbon material family and supplementing its versatility.

physics.atom-ph↗

arXiv2023

Janus-graphene: a two-dimensional half-auxetic carbon allotropes with non-chemical Janus configuration

The asymmetric properties of Janus two-dimensional materials commonly depend on chemical effects, such as different atoms, elements, material types, etc. Herein, based on carbon gene recombination strategy, we identify an intrinsic non-chemical Janus configuration in a novel purely sp$^2$ hybridized carbon monolayer, named as Janus-graphene. With the carbon gene of tetragonal, hexagonal, and octagonal rings, the spontaneous unilateral growth of carbon atoms drives the non-chemical Janus configuration in Janus-graphene, which is totally different from the chemical effect in common Janus materials such as MoSSe. A structure-independent half-auxetic behavior is mapped in Janus-graphene that the structure maintains expansion whether stretched or compressed, which lies in the key role of $p_z$ orbital. The unprecedented half-auxeticity in Janus-graphene extends intrinsic auxeticity into pure sp$^2$ hybrid carbon configurations. With the unique half-auxeticity emerged in the non-chemical Janus configuration, Janus-graphene enriches the functional carbon family as a promising candidate for micro/nanoelectronic device applications.

cond-mat.mtrl-sci↗

arXiv2023

Biaxial strain modulated electronic structures of layered two-dimensional MoSiGeN4 Rashba systems

The two-dimensional (2D) MA2Z4 family has received extensive attention in manipulating its electronic structure and achieving intriguing physical properties. However, engineering the electronic properties remains a challenge. Herein, based on first-principles calculations, we systematically investigate the effect of biaxial strains on the electronic structures of 2D Rashba MoSiGeN4 (MSGN), and further explore how the interlayer interactions affect the Rashba spin splitting in such strained layered MSGNs. After applying biaxial strains, the band gap decreases monotonically with increasing tensile strains but increases when the compressive strains are applied. An indirect-direct-indirect band gap transition is induced by applying a moderate compressive strain (< 5%) in the MSGNs. Due to the symmetry breaking and moderate spin-orbit coupling (SOC), the monolayer MSGN possess an isolated Rashba spin splitting (R) near the Fermi level, which could be effectively regulated to the Lifshitz transition (L) by biaxial strain. For instance, a L-R-L transformation of Fermi surface is presented in monolayer and a more complex and changeable L-R-L-R evolution is observed in bilayer and trilayer MSGNs as the biaxial strain vary from -8% to 12%, which actually depend on the appearance, variation, and vanish of the Mexican hat band in the absence of SOC under different strains. The contribution of Mo-dz2 orbital hybridized with N-pz orbital in the highest valence band plays a dominant role on the band evolution under biaxial strains, where the R-L evolution corresponds to the decreased Mo-dz2 orbital contribution. Our study highlights the biaxial strain controllable Rashba spin splitting, in particular the introduction and even the evolution of Lifshitz transition near Fermi surface, which makes the strained MSGNs as promising candidates for future applications in spintronic devices.

cond-mat.mtrl-sci↗

arXiv2022

Softened sp2-sp3 bonding network leads to strong anharmonicity and weak hydrodynamics in graphene+

Graphene+, a novel carbon monolayer with sp2-sp3 hybridization, is recently reported to exhibit graphene-like Dirac properties and unprecedented out-of-plane half-auxetic behavior [Yu et al, Cell Reports Physical Science, 3 100790 (2022)]. Herein, from comprehensively state-of-the-art first-principles studies, we report the exceptional lattice thermal transport properties of graphene+ driven by the unique sp2-sp3 crystal configuration. At room temperature, the thermal conductivity of graphene+ is calculated to be ~170 W/mK, which is much lower than that of graphene (~3170 W/mK) Despite the buckling structure, weak phonon scattering phase space is trapped in graphene+. Thus, the reduction in thermal conductivity magnitude stems from soft bonding due to the unique sp2-sp3 crystal configuration. Soft bonding suppresses the vibrations of acoustic phonons, which leads to strong anharmonicity and weak phonon hydrodynamics. Further, lower group velocity, relaxation time and smaller phonon mean free path emerge in graphene+, and the significantly decreased thermal conductivity is achieved. Our study provides fundamental physical insights into the thermal transport properties of graphene+, and it serves as an ideal model to study atomic bonding versus thermal transport properties due to weak scattering phase space.

cond-mat.mtrl-sci↗

arXiv2022

The record low thermal conductivity of monolayer Cuprous Iodide (CuI) with direct wide bandgap

Two-dimensional materials have attracted lots of research interests due to the fantastic properties that are unique to the bulk counterparts. In this paper, from the state-of-the-art first-principles, we predicted the stable structure of monolayer counterpart of the γ-CuI (Cuprous Iodide), which is a p-type wide bandgap semiconductor. The monolayer CuI presents multifunctional superiority in terms of electronic, optical, and thermal transport properties. Specifically, the ultralow thermal conductivity of 0.116 Wm-1K-1 is predicted for monolayer CuI, which is much lower than γ-CuI (0.997 Wm-1K-1) and other typical semiconductors. Moreover, an ultrawide direct bandgap of 3.57 eV is found in monolayer CuI, which is larger than γ-CuI (2.95-3.1 eV), promoting the applications in nano-/optoelectronics with better optical performance. The ultralow thermal conductivity and direct wide bandgap of monolayer CuI as reported in this study would promise its potential applications in transparent and wearable electronics.

cond-mat.mtrl-sci↗

arXiv2022

Emerging negative Poisson's ratio driven by strong intralayer interaction response in rectangular transition metal chalcogenides

Auxetic behavior quantified by the negative Poisson's ratio (NPR) is commonly attributed to geometry evolution with re-entrant mechanism or other mechanical factors, which is thought to be independent of electronic structures. Thus, searching for electronic effect dominated auxetic behavior is challenging. Herein, from state-of-the-art first-principles calculations, by studying a class of two-dimensional (2D) transition metal chalcogenides (TMCs), namely X2Y2-type (X=Cu, Ag, Au; Y=O, S, Se) rectangular TMCs (R-TMCs), we identify that the monolayer R-Cu2Se2 unconventionally demonstrates a structure-independent anisotropic NPR. In contrast, the NPR is absent in other R-TMCs. The emerging NPR is attributed to the strong strain response of intralayer interaction in R-Cu2Se2, which can be traced to the lone pair electrons and weak electronegativity of Se atoms under multi-orbital hybridization. The emerging NPR would make R-Cu2Se2 a promising candidate in electronics protection, and our study would provide valuable clues and useful guidance for designing advanced auxetic materials.

cond-mat.mtrl-sci↗

arXiv2022

The consistent behavior of negative Poissons ratio with interlayer interactions

Negative Poissons ratio (NPR) is of great interest due to the novel applications in lots of fields. Films are the most commonly used form in practical applications, which involves multiple layers. However, the effect of interlayer interactions on the NPR is still unclear. In this study, based on first principles calculations, we systematically investigate the effect of interlayer interactions on the NPR by comparably studying single-layer graphene, few-layer graphene, h-BN, and graphene-BN heterostructure. It is found that they almost have the same geometry-strain response. Consequently, the NPR in bilayer graphene, triple-layer graphene, and graphene-BN heterostructure are consistent with that in single-layer graphene and h-BN. The fundamental mechanism lies in that the response to strain of the orbital coupling are consistent under the effect of interlayer interactions. The deep understanding of the NPR with the effect of interlayer interactions as achieved in this study is beneficial for the future design and development of micro-/nanoscale electromechanical devices with novel functions based on nanostructures.

physics.chem-ph↗

arXiv2022

Accessing negative Poisson`s ratio of graphene by machine learning interatomic potentials

The negative Poisson`s ratio (NPR) is a novel property of materials, which enhances the mechanical feature and creates a wide range of application prospects in lots of fields, such as aerospace, electronics, medicine, etc. Fundamental understanding on the mechanism underlying NPR plays an important role in designing advanced mechanical functional materials. However, with different methods used, the origin of NPR is found different and conflicting with each other, for instance, in the representative graphene. In this study, based on machine learning technique, we constructed a moment tensor potential (MTP) for molecular dynamics (MD) simulations of graphene. By analyzing the evolution of key geometries, the increase of bond angle is found to be responsible for the NPR of graphene instead of bond length. The results on the origin of NPR are well consistent with the start-of-art first-principles, which amend the results from MD simulations using classic empirical potentials. Our study facilitates the understanding on the origin of NPR of graphene and paves the way to improve the accuracy of MD simulations being comparable to first-principle calculations. Our study would also promote the applications of machine learning interatomic potentials in multiscale simulations of functional materials. *Author

physics.comp-ph↗

arXiv2019

Anomalous thermal transport behavior in graphene-like carbon nitride (C$_3$N)

New classes 2D carbon-based materials beyond graphene have been intensively studied for their promising applications in nano-/opto-/spin-electronics, catalysis, sensors, clean energy, etc. Very recently, the controllable large-scale synthesis of 2D single crystalline carbon nitride (C3N) was reported, which is the first and the only crystalline, hole-free, single-layer carbon nitride with fascinating properties. Herein, we perform a comparative study of thermal transport between monolayer C3N and the parent graphene. The thermal conductivity (k) of C3N shows an anomalous temperature dependence, which is totally different from that for common crystalline materials and deviates largely from the well-known k~1/T relationship. Moreover, the k of C3N is found in surprise to be enlarged by applying bilateral tensile strain, despite its similar planar honeycomb structure as graphene, whose k is reduced upon stretching. The underlying mechanism is revealed by providing direct evidence for the interaction between lone-pair N-s electrons and bonding electrons from C atoms in C3N based on the analysis of orbital-projected electronic structures and electron localization function (ELF). Our study not only make a comprehensive investigation of the thermal transport in graphene-like C3N, but also reveals the physical origins for its anomalous properties, which deepens the understanding of phonon transport in 2D materials.

cond-mat.mtrl-sci↗