arXiv subjects

Zhibin Gao

Publications and source records attributed to Zhibin Gao.

At least 19 recordsLinked to original sources

arXiv2026

Four-phonon scattering and coherent heat transport in ultrawide-bandgap SrSnO3

SrSnO3 is a promising ultrawide-bandgap perovskite oxide whose thermal transport is governed by structural distortions and anharmonic lattice dynamics. Here, we investigate the lattice thermal conductivity (kL) of orthorhombic and cubic SrSnO3 within a unified first-principles framework combining self-consistent phonon renormalization, three- and four-phonon scattering, and coherent heat transport. Bonding analysis reveals a rigid Sn-O octahedral framework embedded in a weakly bonded Sr sublattice, giving rise to low-frequency vibrational modes susceptible to strong anharmonic effects. Four-phonon scattering is identified as a key mechanism limiting particle-like heat conduction, reducing the Peierls thermal conductivity by 19.4% at 300 K in the orthorhombic phase and by 52.1% at 1300 K in the cubic phase, while the coherent contribution provides a finite channel that partially compensates this reduction. We further show that the apparent agreement between three-phonon calculations and experimental thermal conductivity at room temperature is not indicative of a complete physical description. Instead, it arises from a near cancellation between four-phonon suppression of the particle-like channel and the neglected coherent contribution. This cancellation breaks down when the full temperature dependence is considered, where only the combined treatment improves agreement with the experimentally observed scaling behavior. A physically consistent description of kL therefore requires phonon renormalization, four-phonon scattering, and coherent transport to be treated on equal footing rather than inferred from three-phonon agreement at a single temperature. These results provide microscopic insight into thermal transport in ultrawide-bandgap stannate perovskites and establish a benchmark for anharmonic transport in strongly distorted oxides.

cond-mat.mtrl-sci↗

arXiv2026

Beyond Janus Atomic Ordering: High-Throughput First-Principles Search for Hidden MoSO Monolayer Structures

Despite the growing interest in two-dimensional (2D) MoSO systems, existing studies have exclusively focused on conventional Janus structures. In this work, we perform high-throughput first-principles calculations to explore novel stable 2D MoSO monolayers. Combined with random sampling strategy, graph theory and group theory, we successfully screen out three novel non-Janus 2D MoSO monolayers from 1325 candidate structures, namely Reversed 2H-MoSO, Hybrid 2H-MoSO, and Hybrid 1T'-MoSO. Compared with Janus MoSO monolayers, the non-Janus MoSO counterparts possess lower binding energies, varying from -4.38 to -4.51 eV/atom. A systematic combination of dynamic, thermodynamic, and mechanical stability analyses corroborates their excellent structural robustness. Ab initio molecular dynamics (AIMD) simulations confirm their superior thermal resistance, with the structures remaining stable at temperatures beyond 2000 K. Interestingly, unlike the semiconducting Janus MoSO, the Hybrid 1T'-MoSO monolayer exhibits distinct metallic characteristics. Furthermore, we found that strain and curvature can enable controlled phase transitions of MoSO among semiconducting, semimetallic, and metallic phases. More importantly, the Hybrid 1T'-MoSO exhibits favorable HER activity with a Gibbs free energy of -0.002 eV, rendering it a promising candidate for hydrogen evolution catalysis. This work not only expands the family of 2D MoSO materials but also provides a reliable strategy for discovering stable functional 2D materials via high-throughput computation.

cond-mat.mtrl-sci↗

arXiv2026

LLM-driven discovery for carbon allotropes with bond-network entropy

The discovery of novel carbon allotropes with tailored thermal and mechanical properties is critical for advanced thermal management. However, exploring the vast configurational space of carbon using \textit{ab initio} calculations remains computationally prohibitive. Driven by the rich topological landscape of carbon, where the competition between $sp, sp^2,$ and $sp^3$ hybridization states dictates material performance, we establish a closed-loop AI framework to explore this complex configurational space. We introduce a hybridization entropy descriptor to guide the search beyond conventional forms. Here, we establish a closed-loop AI framework that synergizes a Large Language Model (LLM) for structural generation with a Machine Learning Potential (MLP) for accelerated evaluation. Leveraging CrystaLLM to generate candidates and an iteratively refined MLP for high-fidelity validation, we screened thousands of structures to identify several stable allotropes with exotic properties. Specifically, we report ``yne-diamond C$_{12}$'' and ``yne-hex-diamond C$_{8}$'', which exhibit extreme thermal anisotropy and ultralow in-plane shear stiffness arising from their mixed $sp$-$sp^3$ hybridization. Furthermore, we discovered a complex $sp$-$sp^2$-$sp^3$ hybridized C$_{12}$ phase that combines metallic conductivity with an anomalous negative Poisson's ratio. Notably, we identified a superhard phase (C16_3) possessing a calculated Vickers hardness (103.3 GPa) exceeding that of diamond 96 GPa). Microscopic analysis reveals that thermal transport in these materials is governed by the interplay between rigid frameworks and flexible linkers. This work expands the known carbon phase space and demonstrates the efficacy of coupling generative AI with machine learning potentials for the accelerated inverse design of functional materials.

cond-mat.mtrl-sci↗

arXiv2026

Role of octahedral tilting induced acoustic softening on limiting thermal transport in SrSnO3

Octahedral tilting is a fundamental structural distortion in perovskites, governing key phenomena such as lattice stabilizing, soft phonon dynamics, group-theoretical analysis, phase transitions, ferroelectricity, and even for tunable electronic band gap. However, its influence on lattice thermal conductivity (kL) remains poorly understood. In the archetypal perovskite SrTiO3, tilting in the low-temperature tetragonal phase is known to enhance kL by suppressing specific phonon scattering channels around 200 cm-1. Here, we investigate the thermal transport in strontium stannate (SrSnO3), another perovskite oxide that undergoes temperature-driven phase transitions, and reveal a completely opposite effect. Through a systematic study across its orthorhombic, tetragonal, and cubic phases, we demonstrate that octahedral tilting in the tetragonal phase of SrSnO3 anomalously triggers acoustic phonon softening. This softening manifests as reduced frequencies and group velocities in low-frequency (<3 THz) acoustic modes, creating a large decrease for heat transport, particularly along the c-axis. Consequently, kL is significantly suppressed, decreasing from 7.48 W m-1 K-1 to 6.06 W m-1 K-1 as the tilting angle increases by a mere 1 degree. These findings identify tilting-induced acoustic softening as a pivotal mechanism for limiting and controlling anisotropic thermal transport in SrSnO3, presenting a stark contrast to the established behavior in SrTiO3.

cond-mat.mtrl-sci↗

arXiv2026

Materials Informatics: Emergence To Autonomous Discovery In The Age Of AI

This perspective explores the evolution of materials informatics, from its foundational roots in physics and information theory to its maturation through artificial intelligence (AI). We trace the field's trajectory from early milestones to the transformative impact of the Materials Genome Initiative and the recent advent of large language models (LLMs). Rather than a mere toolkit, we present materials informatics as an evolving ecosystem, reviewing key methodologies such as Bayesian Optimization, Reinforcement Learning, and Transformers that drive inverse design and autonomous self-driving laboratories. We specifically address the practical challenges of LLM integration, comparing specialist versus generalist models and discussing solutions for uncertainty quantification. Looking forward, we assess the transition of AI from a predictive tool to a collaborative research partner. By leveraging active learning and retrieval-augmented generation (RAG), the field is moving toward a new era of autonomous materials science, increasingly characterized by "human-out-of-the-loop" discovery processes.

physics.comp-ph↗

arXiv2025

Metavalent Bonding-Induced Phonon Hardening and Giant Anharmonicity in BeO

The search for materials with intrinsically low thermal conductivity ($κ_L$) is critical for energy applications, yet conventional descriptors often fail to capture the complex interplay between bonding and lattice dynamics. Here, first-principles calculations are used to contrast the thermal transport in covalent zincblende (zb) and metavalent rocksalt (rs) BeO. We find that the metavalent bonding in rs-BeO enhances lattice anharmonicity, activating multi-phonon scattering channels and suppressing phonon transport. This results in an ultralow $κ_L$ of 24 W m$^{-1}$ K$^{-1}$ at 300 K, starkly contrasting with the zb phase (357 W m$^{-1}$ K$^{-1}$). Accurately modeling such strongly anharmonic systems requires explicit inclusion of temperature-dependent phonon renormalization and four-phonon scattering. These contributions, negligible in zb-BeO, are essential for high-precision calculations of the severely suppressed $κ_L$ in rs-BeO. Finally, we identify three key indicators to guide the discovery of metavalently bonded, incipient-metallic materials: (i) an NaCl-type crystal structure, (ii) large Grüneisen parameters ($\textgreater$2), and (iii) a breakdown of the Lyddane-Sachs-Teller relation. These findings provide microscopic insight into thermal transport suppression by metavalent bonding and offer a predictive framework for identifying promising thermoelectrics and phase-change materials.

cond-mat.mtrl-sci↗

arXiv2025

Digital Twin-Assisted Task Offloading and Resource Allocation in ISAC-Enabled Internet of Vehicles

The convergence of the Internet of vehicles (IoV) and 6G networks is driving the evolution of next-generation intelligent transportation systems. However, IoV networks face persistent challenges, including low spectral efficiency in vehicular communications, difficulty in achieving dynamic and adaptive resource optimization, and the need for long-term stability under highly dynamic environments. In this paper, we study the problem of digital twin (DT)-assisted task offloading and resource allocation in integrated sensing and communication (ISAC)-enabled IoV networks. The objective is to minimize the long-term average system cost, defined as a weighted combination of delay and energy consumption, while ensuring queue stability over time. To address this, we employ an ISAC-enabled design and introduce two transmission modes (i.e., raw data transmission (DataT) and instruction transmission (InstrT)). The InstrT mode enables instruction-level transmission, thereby reducing data volume and improving spectral efficiency. We then employ Lyapunov optimization to decompose the long-term stochastic problem into per-slot deterministic problems, ensuring long-term queue stability. Building upon this, we propose a Lyapunov-driven DT-enhanced multi-agent proximal policy optimization (Ly-DTMPPO) algorithm, which leverages DT for global state awareness and intelligent decision-making within a centralized training and decentralized execution (CTDE) architecture. Extensive simulations verify that Ly-DTMPPO achieves superior performance compared with existing benchmarks.

cs.NI↗

arXiv2025

Machine learning-driven elasticity prediction in advanced inorganic materials via convolutional neural networks

Inorganic crystal materials have broad application potential due to excellent physical and chemical properties, with elastic properties (shear modulus, bulk modulus) crucial for predicting materials' electrical conductivity, thermal conductivity and mechanical properties. Traditional experimental measurement suffers from high cost and low efficiency, while theoretical simulation and graph neural network-based machine learning methods--especially crystal graph convolutional neural networks (CGCNNs)--have become effective alternatives, achieving remarkable results in predicting material elastic properties. This study trained two CGCNN models using shear modulus and bulk modulus data of 10987 materials from the Matbench v0.1 dataset, which exhibit high accuracy (mean absolute error <13, coefficient of determination R-squared close to 1) and good generalization ability. Materials were screened to retain those with band gaps between 0.1-3.0 eV and exclude radioactive element-containing compounds. The final predicted dataset comprises two parts: 54359 crystal structures from the Materials Project database and 26305 crystal structures discovered by Merchant et al. (2023 Nature 624 80). Ultimately, this study completed the prediction of shear modulus and bulk modulus for 80664 inorganic crystals. This work enriches existing material elastic data resources and provides robust support for material design, with all data openly available at https://doi.org/10.57760/sciencedb.j00213.00104.

cond-mat.mtrl-sci↗

arXiv2025

Carrier-phonon decoupling via annealing enhances thermoelectric performance of Bi2(Te,Se)3

Thermoelectric cooling based on the Peltier effect requires high-performance materials near room temperature. In this work, Bi2Te2.6Se0.4 synthesized by melting-hot pressing followed by 100 h annealing (MT-HP-AN) at 723 K exhibits markedly improved performance. Annealing introduces cation vacancies via Te(Se) volatilization, lowering carrier density and enhancing mobility, while simultaneously increasing phonon scattering. A peak ZT of 1.06 at 373 K and an average ZT of 0.99 at 300-423 K are achieved. This MT-HP-AN approach offers a simple yet effective strategy to decouple carrier and phonon transport, advancing the potential of n-type BTS for thermo-electric cooling applications.

cond-mat.mtrl-sci↗

arXiv2025

Ambient-pressure superconductivity above 22 K in hole-doped YB2

Recent studies of hydrogen-dominant (superhydride) materials, such as LaH10 have led to putative discoveries of near-room temperature superconductivity at high pressures, with a superconducting transition temperature (Tc) of 250 K observed at 170 GPa. While these findings are promising, achieving such high superconductivity requires challenging experimental conditions typically exceeding 100 GPa. In this study, we utilize first-principles calculations and Migdal-Eliashberg theory to examine the superconducting properties of the stable boron-based compound YB2 at atmospheric pressure, where yttrium and boron atoms form a layered structure. Our results indicate that YB2 exhibits a Tc of 2.14 K at 0 GPa. We find that doping with additional electrons (0 to 0.3) leads to a monotonic decrease in Tc as the electron concentration increases. Conversely, introducing holes significantly enhances Tc, raising it to 22.83 K. Although our findings do not surpass the superconducting temperature of the well-known MgB2, our doping strategy highlights a method for tuning electron-phonon coupling strength in metal borides. This insight could be valuable for future experi-mental applications. Overall, this study not only deepens our understanding of YB2 superconducting properties but also contributes to the ongoing search for high-temperature superconductors.

cond-mat.supr-con↗

arXiv2025

Same-group element replacement enhances superconductivity in clathrate-like YH4

H3S, LaH10, and hydrogen-based compounds have garnered significant interest due to their high-temperature superconducting properties. However, the requirement for extremely high pressures limits their practical applications. In this study, YH4 is adopted as a base material, with partial substitution of Yttrium (Y) by Scandium (Sc), Lanthanum (La), and Zirconium (Zr). Pure YH4, stable at 120 GPa, exhibits a critical temperature (Tc) of 84-95 K. Substituting half of the Y atoms increases Tc to 124.43 K for (Y,Sc)H4 at 100 GPa but reduces it to 101.24 K for (Y,La)H4 at 120 GPa. In contrast, (Y,Zr)H4 at 200 GPa shows a further suppressed Tc of 69.55 K. The remarkable superconductivity in (Y,Sc)H4 might be related to its unique phonon dispersion without optical-acoustic gap, compressed Y-H bonds, and significant electron delocalization under pressure, collectively boosting electron-phonon interactions. Furthermore, the lowest optical phonons play a crucial role in the superconductivity of these materials. This work suggests that substituting Y with same-group metal elements is an effective strategy to enhance Tc in hydride superconductors.

cond-mat.supr-con↗

arXiv2025

A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach for UAV-Assisted Vehicular Networks with Delayed CSI Feedback

Low altitude uncrewed aerial vehicles (UAVs) are expected to facilitate the development of aerial-ground integrated intelligent transportation systems and unlocking the potential of the emerging low-altitude economy. However, several critical challenges persist, including the dynamic optimization of network resources and UAV trajectories, limited UAV endurance, and imperfect channel state information (CSI). In this paper, we offer new insights into low-altitude economy networking by exploring intelligent UAV-assisted vehicle-to-everything communication strategies aligned with UAV energy efficiency. Particularly, we formulate an optimization problem of joint channel allocation, power control, and flight altitude adjustment in UAV-assisted vehicular networks. Taking CSI feedback delay into account, our objective is to maximize the vehicle-to-UAV communication sum rate while satisfying the UAV's long-term energy constraint. To this end, we first leverage Lyapunov optimization to decompose the original long-term problem into a series of per-slot deterministic subproblems. We then propose a diffusion-based deep deterministic policy gradient (D3PG) algorithm, which innovatively integrates diffusion models to determine optimal channel allocation, power control, and flight altitude adjustment decisions. Through extensive simulations using real-world vehicle mobility traces, we demonstrate the superior performance of the proposed D3PG algorithm compared to existing benchmark solutions.

cs.NI↗

arXiv2025

Copper delocalization leads to ultralow thermal conductivity in chalcohalide CuBiSeCl2

Mixed anion halide-chalcogenide materials have attracted considerable attention due to their exceptional optoelectronic properties, making them promising candidates for various applications. Among these, CuBiSeCl_2 has recently been experimentally identified with remarkably low lattice thermal conductivity (k_L). In this study, we employ Wigner transport theory combined with neuroevolution machine learning potential (NEP)-assisted self-consistent phonon calculations to unravel the microscopic origins of this low k_L. Our findings reveal that the delocalization and weak bonding of copper atoms are key contributors to the strong phonon anharmonicity and wavelike tunneling (random walk diffusons). These insights deepen our understanding of the relationship between bonding characteristics, anharmonicity, delocalization, and vibrational dynamics, paving the way for the design and optimization of CuBiSeCl_2 and analogous materials for advanced phonon engineering applications.

cond-mat.mtrl-sci↗

arXiv2025

Discovery of a Robust Non-Janus Hybrid MoSH Monolayer as a Two-Gap Superconductor via High-Throughput Computational Screening

The atomic-scale determination of hydrogen positions in MoSH monolayers remains experimentally challenging, and existing studies are confined to Janus-type configurations. Here, we combine high-throughput structural screening with first-principles calculations to predict a novel non-Janus Hybrid 1T$^{'}$-MoSH monolayer, which energetically surpasses all previously reported MoSH phases with a binding energy of -3.02 eV. This structure emerges as a hybrid of MoS$_2$ and MoH$_2$, featuring alternating S and H atoms on both sides of the Mo layer. Comprehensive stability analyses confirm its robustness in energy, mechanics, dynamics, and thermodynamics (stable up to 1600 K). Remarkably, anisotropic Migdal-Eliashberg theory predicts Hybrid 1T$^{'}$-MoSH as a two-gap superconductor with a critical temperature T$_c$ of 16.34 K, driven by strong electron-phonon coupling ($λ$$=$1.39). Substituting Mo with Hf, Ta, or Ti drastically suppresses T$_c$ $\sim$ (0.53-2.42 K), highlighting Mo$^{'}$s unique role in enhancing superconductivity. Our work not only expands the family of 2D transition metal chalcogenides but also proposes a promising candidate for quantum technologies, bridging theoretical design to functional material discovery.

cond-mat.mtrl-sci↗

arXiv2025

PINK: physical-informed machine learning for lattice thermal conductivity

Lattice thermal conductivity ($κ_L$) is crucial for efficient thermal management in electronics and energy conversion technologies. Traditional methods for predicting \k{appa}L are often computationally expensive, limiting their scalability for large-scale material screening. Empirical models, such as the Slack model, offer faster alternatives but require time-consuming calculations for key parameters such as sound velocity and the Gruneisen parameter. This work presents a high-throughput framework, physical-informed kappa (PINK), which combines the predictive power of crystal graph convolutional neural networks (CGCNNs) with the physical interpretability of the Slack model to predict \k{appa}L directly from crystallographic information files (CIFs). Unlike previous approaches, PINK enables rapid, batch predictions by extracting material properties such as bulk and shear modulus from CIFs using a well-trained CGCNN model. These properties are then used to compute the necessary parameters for $κ_L$ calculation through a simplified physical formula. PINK was applied to a dataset of 377,221 stable materials, enabling the efficient identification of promising candidates with ultralow $κ_L$ values, such as Ag$_3$Te$_4$W and Ag$_3$Te$_4$Ta. The platform, accessible via a user-friendly interface, offers an unprecedented combination of speed, accuracy, and scalability, significantly accelerating material discovery for thermal management and energy conversion applications.

cond-mat.mtrl-sci↗

arXiv2025

Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking

Lyapunov optimization theory has recently emerged as a powerful mathematical framework for solving complex stochastic optimization problems by transforming long-term objectives into a sequence of real-time short-term decisions while ensuring system stability. This theory is particularly valuable in unmanned aerial vehicle (UAV)-based low-altitude economy (LAE) networking scenarios, where it could effectively address inherent challenges of dynamic network conditions, multiple optimization objectives, and stability requirements. Recently, generative artificial intelligence (GenAI) has garnered significant attention for its unprecedented capability to generate diverse digital content. Extending beyond content generation, in this paper, we propose a framework integrating generative diffusion models with reinforcement learning to address Lyapunov optimization problems in UAV-based LAE networking. We begin by introducing the fundamentals of Lyapunov optimization theory and analyzing the limitations of both conventional methods and traditional AI-enabled approaches. We then examine various GenAI models and comprehensively analyze their potential contributions to Lyapunov optimization. Subsequently, we develop a Lyapunov-guided generative diffusion model-based reinforcement learning framework and validate its effectiveness through a UAV-based LAE networking case study. Finally, we outline several directions for future research.

cs.NI↗

arXiv2024

Joint Model Caching and Resource Allocation in Generative AI-Enabled Wireless Edge Networks

With the rapid advancement of artificial intelligence (AI), generative AI (GenAI) has emerged as a transformative tool, enabling customized and personalized AI-generated content (AIGC) services. However, GenAI models with billions of parameters require substantial memory capacity and computational power for deployment and execution, presenting significant challenges to resource-limited edge networks. In this paper, we address the joint model caching and resource allocation problem in GenAI-enabled wireless edge networks. Our objective is to balance the trade-off between delivering high-quality AIGC and minimizing the delay in AIGC service provisioning. To tackle this problem, we employ a deep deterministic policy gradient (DDPG)-based reinforcement learning approach, capable of efficiently determining optimal model caching and resource allocation decisions for AIGC services in response to user mobility and time-varying channel conditions. Numerical results demonstrate that DDPG achieves a higher model hit ratio and provides superior-quality, lower-latency AIGC services compared to other benchmark solutions.

cs.NI↗

arXiv2024

An interpretable formula for lattice thermal conductivity of crystals

Lattice thermal conductivity (kL) is a crucial physical property of crystals with applications in thermal management, such as heat dissipation, insulation, and thermoelectric energy conversion. However, accurately and rapidly determining kL poses a considerable challenge. In this study, we introduce an formula that achieves high precision (mean relative error=8.97%) and provides fast predictions, taking less than one minute, for kL across a wide range of inorganic binary and ternary materials. Our interpretable, dimensionally aligned and physical grounded formula forecasts kL values for 4,601 binary and 6,995 ternary materials in the Materials Project database. Notably, we predict undiscovered high kL values for AlBN2 (kL=101 W/ m/ K) and the undetectedlow kL Cs2Se (kL=0.98 W/ m/ K) at room temperature. This method for determining kL streamlines the traditionally time-consuming process associated with complex phonon physics. It provides insights into microscopic heat transport and facilitates the design and screening of materials with targeted and extreme kL values through the application of phonon engineering. Our findings offer opportunities for controlling and optimizing macroscopic transport properties of materials by engineering their bulk modulus, shear modulus, and Gruneisen parameter.

cond-mat.mtrl-sci↗