arXiv subjects

Zhibin Gao

Publications and source records attributed to Zhibin Gao.

At least 37 records · Page 2Linked to original sources

arXiv2024

Bonding Hierarchy and Coordination Interaction Leading to High Thermoelectricity in Wide Bandgap TlAgI2

High thermoelectric properties are associated with the phonon-glass electron-crystal paradigm. Conventional wisdom suggests that the optimal bandgap of semiconductor to achieve the largest power factor should be between 6 and 10 kbT. To address challenges related to the bipolar effect and temperature limitations, we present findings on Zintl-type TlAgI2, which demonstrates an exceptionally low lattice thermal conductivity of 0.3 W m-1 K-1 at 300 K. The achieved figure of merit (ZT) for TlAgI2, featuring a 1.55 eV bandgap, reaches a value of 2.20 for p-type semiconductor. This remarkable ZT is attributed to the existence of extended antibonding states Ag-I in the valence band. Furthermore, the bonding hierarchy, influencing phonon anharmonicity, and coordination bonds, facilitating electron transfer between the ligand and the central metal ion, significantly contribute to electronic transport. This finding serves as a promising avenue for the development of high ZT materials with wide bandgaps at elevated temperatures.

cond-mat.mtrl-sci↗

arXiv2024

DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach

The rapid advancement of Artificial Intelligence (AI) has introduced Deep Neural Network (DNN)-based tasks to the ecosystem of vehicular networks. These tasks are often computation-intensive, requiring substantial computation resources, which are beyond the capability of a single vehicle. To address this challenge, Vehicular Edge Computing (VEC) has emerged as a solution, offering computing services for DNN-based tasks through resource pooling via Vehicle-to-Vehicle/Infrastructure (V2V/V2I) communications. In this paper, we formulate the problem of joint DNN partitioning, task offloading, and resource allocation in VEC as a dynamic long-term optimization. Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time. To this end, we first leverage a Lyapunov optimization technique to decouple the original long-term optimization with stability constraints into a per-slot deterministic problem. Afterwards, we propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models to determine the optimal DNN partitioning and task offloading decisions. Furthermore, we integrate convex optimization techniques into MAD2RL as a subroutine to allocate computation resources, enhancing the learning efficiency. Through simulations under real-world movement traces of vehicles, we demonstrate the superior performance of our proposed algorithm compared to existing benchmark solutions.

cs.LG↗

arXiv2024

Machine learning for predicting ultralow thermal conductivity and high ZT in complex thermoelectric materials

Efficient and precise calculations of thermal transport properties and figure of merit, alongside a deep comprehension of thermal transport mechanisms, are essential for the practical utilization of advanced thermoelectric materials. In this study, we explore the microscopic processes governing thermal transport in the distinguished crystalline material Tl$_9$SbTe$_6$ by integrating a unified thermal transport theory with machine learning-assisted self-consistent phonon calculations. Leveraging machine learning potentials, we expedite the analysis of phonon energy shifts, higher-order scattering mechanisms, and thermal conductivity arising from various contributing factors like population and coherence channels. Our finding unveils an exceptionally low thermal conductivity of 0.31 W m$^{-1}$ K$^{-1}$ at room temperature, a result that closely correlates with experimental observations. Notably, we observe that the off-diagonal terms of heat flux operators play a significant role in shaping the overall lattice thermal conductivity of Tl$_9$SbTe$_6$, where the ultralow thermal conductivity resembles that of glass due to limited group velocities. Furthermore, we achieve a maximum $ZT$ value of 3.17 in the $c$-axis orientation for \textit{p}-type Tl$_9$SbTe$_6$ at 600 K, and an optimal $ZT$ value of 2.26 in the $a$-axis and $b$-axis direction for \textit{n}-type Tl$_9$SbTe$_6$ at 500 K. The crystalline Tl$_9$SbTe$_6$ not only showcases remarkable thermal insulation but also demonstrates impressive electrical properties owing to the dual-degeneracy phenomenon within its valence band. These results not only elucidate the underlying reasons for the exceptional thermoelectric performance of Tl$_9$SbTe$_6$ but also suggest potential avenues for further experimental exploration.

cond-mat.mtrl-sci↗

arXiv2024

Anomalous thermal conductivity in 2D silica nanocages of immobilizing noble gas atom

Noble gas atoms such as Kr and Xe are byproducts of nuclear fission in nuclear plants. How to trap and confine these volatile even radioactive gases is particularly challenging. Recent studies have shown that they can be trapped in nanocages of ultrathin silica. Here, we exhibit with self-consistent phonon theory and four-phonon (4ph) scattering where the adsorption of noble gases results in an anomalous increase in lattice thermal conductivity, while the presence of Cu atoms doping leads to a reduction in lattice thermal conductivity. We trace this behavior in host-guest 2D silica to an interplay of tensile strain, rattling phonon modes, and redistribution of electrons. We also find that 4ph scatterings play indispensable roles in the lattice thermal conductivity of 2D silica. Our work illustrates the microscopic heat transfer mechanism in 2D silica nanocages with the immobilization of noble gas atoms and inspires further exploring materials with the kagome and glasslike lattice thermal conductivity.

cond-mat.mtrl-sci↗

arXiv2024

Long-Term or Temporary? Hybrid Worker Recruitment for Mobile Crowd Sensing and Computing

This paper investigates a novel hybrid worker recruitment problem where the mobile crowd sensing and computing (MCSC) platform employs workers to serve MCSC tasks with diverse quality requirements and budget constraints, under uncertainties in workers' participation and their local workloads.We propose a hybrid worker recruitment framework consisting of offline and online trading modes. The former enables the platform to overbook long-term workers (services) to cope with dynamic service supply via signing contracts in advance, which is formulated as 0-1 integer linear programming (ILP) with probabilistic constraints of service quality and budget.Besides, motivated by the existing uncertainties which may render long-term workers fail to meet the service quality requirement of each task, we augment our methodology with an online temporary worker recruitment scheme as a backup Plan B to support seamless service provisioning for MCSC tasks, which also represents a 0-1 ILP problem. To tackle these problems which are proved to be NP-hard, we develop three algorithms, namely, i) exhaustive searching, ii) unique index-based stochastic searching with risk-aware filter constraint, iii) geometric programming-based successive convex algorithm, which achieve the optimal or sub-optimal solutions. Experimental results demonstrate our effectiveness in terms of service quality, time efficiency, etc.

cs.DC↗

arXiv2023

GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling over Dynamic Vehicular Clouds

Vehicular clouds (VCs) are modern platforms for processing of computation-intensive tasks over vehicles. Such tasks are often represented as directed acyclic graphs (DAGs) consisting of interdependent vertices/subtasks and directed edges. In this paper, we propose a graph neural network-augmented deep reinforcement learning scheme (GA-DRL) for scheduling DAG tasks over dynamic VCs. In doing so, we first model the VC-assisted DAG task scheduling as a Markov decision process. We then adopt a multi-head graph attention network (GAT) to extract the features of DAG subtasks. Our developed GAT enables a two-way aggregation of the topological information in a DAG task by simultaneously considering predecessors and successors of each subtask. We further introduce non-uniform DAG neighborhood sampling through codifying the scheduling priority of different subtasks, which makes our developed GAT generalizable to completely unseen DAG task topologies. Finally, we augment GAT into a double deep Q-network learning module to conduct subtask-to-vehicle assignment according to the extracted features of subtasks, while considering the dynamics and heterogeneity of the vehicles in VCs. Through simulating various DAG tasks under real-world movement traces of vehicles, we demonstrate that GA-DRL outperforms existing benchmarks in terms of DAG task completion time.

cs.LG↗

arXiv2023

The role of high-order anharmonicity and off-diagonal terms in thermal conductivity: a case study of multi-phase CsPbBr3

We investigate the influence of three- and four-phonon scattering, perturbative anharmonic phonon renormalization, and off-diagonal terms of coherent phonons on the thermal conductivity of CsPbBr3 phase change perovskite, by using advanced implementations and first-principles simulations. Our study spans a wide temperature range covering the entire structural spectrum. Notably, we demonstrate that the interactions between acoustic and optical phonons result in contrasting trends of phonon frequency shifts for the high-lying optical phonons in orthorhombic and cubic CsPbBr3 as temperature varies. Our findings highlight the significance of wave-like tunneling of coherent phonons in ultralow and glass-like thermal conductivity in halide perovskites.

cond-mat.mtrl-sci↗

arXiv2023

Kirigami-Inspired Thermal Regulator

One of the current challenges in nanoscience is tailoring phononic devices, such as thermal regulators and thermal computing. This has long been a rather elusive task because the thermal-switching ratio is not as high as electronic analogs. Mapping from a topological kirigami assembly, nitrogen-doped porous graphene metamaterials on the nanoscale are inversely designed with a thermal-switching ratio of 27.79, which is more than double the value of previous work. We trace this behavior to the chiral folding-unfolding deformation, resulting in a metal-insulator transition. This study provides a nanomaterial design paradigm to bridge the gap between kinematics and functional metamaterials that motivates the development of high-performance thermal regulators.

cond-mat.mtrl-sci↗

arXiv2022

Ultrahigh thermoelectric performance of Janus α-STe2 and α-SeTe2 monolayers

Combined with first-principles calculations and semiclassical Boltzmann transport theory, Janus α-STe2 and α-SeTe2 monolayers are investigated systematically. Janus α-STe2 and α-SeTe2 monolayers are indirect semiconductors with band gaps of 1.20 and 0.96 eV. It is found they possess ultrahigh figure of merit (ZT) values of 3.9 and 4.4 at 500 K, much higher than that of the pristine α-Te monolayer (2.8). The higher ZT originates from Janus structures reduce lattice thermal conductivities remarkably compared with pristine α-Te monolayer. The much higher phonon anharmonicity in Janus monolayers leads to significant lower lattice thermal conductivity. It is also found electronic thermal conductivity can play an important role in thermoelectric efficiency for the materials with quite low lattice thermal conductivity. This work suggests the potential applications of Janus α-STe2 and α-SeTe2 monolayers as thermoelectric materials and highlights Janus structure as an effective way to enhance thermoelectric performance.

cond-mat.mtrl-sci↗

arXiv2022

RFID: Towards Low Latency and Reliable DAG Task Scheduling over Dynamic Vehicular Clouds

Vehicular cloud (VC) platforms integrate heterogeneous and distributed resources of moving vehicles to offer timely and cost-effective computing services. However, the dynamic nature of VCs (i.e., limited contact duration among vehicles), caused by vehicles' mobility, poses unique challenges to the execution of computation-intensive applications/tasks with directed acyclic graph (DAG) structure, where each task consists of multiple interdependent components (subtasks). In this paper, we study scheduling of DAG tasks over dynamic VCs, where multiple subtasks of a DAG task are dispersed across vehicles and then processed by cooperatively utilizing vehicles' resources. We formulate DAG task scheduling as a 0-1 integer programming, aiming to minimize the overall task completion time, while ensuring a high execution success rate, which turns out to be NP-hard. To tackle the problem, we develop a ranking and foresight-integrated dynamic scheduling scheme (RFID). RFID consists of (i) a dynamic downward ranking mechanism that sorts the scheduling priority of different subtasks, while explicitly taking into account for the sequential execution nature of DAG; (ii) a resource scarcity-based priority changing mechanism that overcomes possible performance degradations caused by the volatility of VC resources; and (iii) a degree-based weighted earliest finish time mechanism that assigns the subtask with the highest scheduling priority to the vehicle which offers rapid task execution along with reliable transmission links. Our simulation results reveal the effectiveness of our proposed scheme in comparison to benchmark methods.

cs.DC↗

arXiv2022

Energy-Aware Graph Task Scheduling in Software-Defined Air-Ground Integrated Vehicular Networks

The Software-Defined Air-Ground integrated Vehicular (SD-AGV) networks have emerged as a promising paradigm, which realize the flexible on-ground resource sharing to support innovative applications for UAVs with heavy computational overhead. In this paper, we investigate a vehicular cloud-assisted task scheduling problem in SD-AGV networks, where the computation-intensive tasks carried by UAVs, and the vehicular cloud are modeled via graph-based representation. To map each component of the graph tasks to a feasible vehicle, while achieving the trade-off among minimizing UAVs' task completion time, energy consumption, and the data exchange cost among moving vehicles, we formulate the problem as a mixed-integer non-linear programming problem, which is Np-hard. Moreover, the constraint associated with preserving task structures poses addressing the subgraph isomorphism problem over dynamic vehicular topology, that further complicates the algorithm design. Motivated by which, we propose an efficient decoupled approach by separating the template (feasible mappings between components and vehicles) searching from the transmission power allocation. For the former, we present an efficient algorithm of searching for all the isomorphic subgraphs with low computation complexity. For the latter, we introduce a power allocation algorithm by applying $p$-norm and convex optimization techniques. Extensive simulations demonstrate that the proposed approach outperforms the benchmark methods considering various problem sizes.

eess.SP↗

arXiv2021

Large Contribution of Quasi-Acoustic Shear Phonon Modes to Thermal Conductivity in Novel Monolayer Ga2O3

Bulk gallium oxide (Ga2O3) has been widely used in lasers, dielectric coatings for solar cells, deep-ultraviolet transistor applications due to the large band gap over 4.5 eV. With the miniaturization of electronic devices, atomically thin Ga2O3 monolayer has been unveiled recently, which features an asymmetric configuration with a quintuple-layer atomic structure. The superior stability, the strain-tunable electronic properties, high carrier mobility and optical absorption indicate the promising applications in the electronic and photoelectronic devices. However, the strict investigation of lattice thermal conductivity (kappa_L) of 2D Ga2O3 is still lacking, which has impeded the widespread use in practical applications. Here, we report the computational discovery of low kappa_L with a value of 10.28 W m-1 K-1 at 300 K in atomically thin Ga2O3. Unexpectedly, two quasi-acoustic shear phonon modes contribute as high as 27% to the kappa_L at 300 K, leading to 37% contribution of optical phonon modes, much larger than many other 2D materials. We also find that the quasi-acoustic shear mode can emerge in the system without van der Waals interactions. This work provides new insight into the nature of thermal transport in non-van der Waals monolayer materials and predicts a new low kappa_L material of potential interest for thermal insulation in transistor applications.

cond-mat.mtrl-sci↗

arXiv2021

Phonon Hall effect with first-principles calculations

Phonon Hall effect (PHE) has attracted a lot of attention in recent years with many theoretical and experimental explorations published. While experiments work on complicated materials, theoretical studies are still hovering around the phenomenon-based models. Moreover, previous microscopic theory was found unable to explain large thermal Hall conductivity obtained by experiments in strontium titanate (STO). Therefore, as a first attempt to bridge this gap, we implement first-principles calculations to explore the PHE in real materials. Our work provides a new benchmark of the PHE in sodium chloride (NaCl) under a large external magnetic field. Moreover, we demonstrate our results in barium titanate (BTO), and discuss the results in STO in detail about their deviation from experiments. As a possible future direction, we further propose that the inner electronic Berry curvature plays an important role in the PHE in STO.

cond-mat.mtrl-sci↗

arXiv2021

Directional Design of Materials Based on the Multi-Objective Optimization: A Case Study of Two-Dimensional Thermoelectric SnSe

Directional design of functional materials with multi-objective constraints is a big challenge, whose performance and stability are determined by different physics factors entangled with each other complicatedly. In this work, we apply the multi-objective optimization based on the Pareto Efficiency and Particle-Swarm Optimization methods to design new functional materials directionally. As a demonstration, we achieve the thermoelectric design of 2D SnSe materials through the methods. We identify several novel metastable 2D SnSe structures with simultaneously lower free energy and better thermoelectric performance over the experimentally-reported monolayer structures. We hope our results about the multi-objective Pareto Optimization method can make a step towards the integrative design of multi-objective and multi-functional materials in the future.

cond-mat.mtrl-sci↗

arXiv2021

First-principles method to study near-field radiative heat transfer

We present a general and convenient first principle method to study near-field radiative heat transfer. We show that the Landauer-like expression of heat flux can be expressed in terms of a frequency and wave-vector dependent macroscopic dielectric function which can be obtained from the linear response density functional theory. A random phase approximation is used to calculate the response function. We computed the heat transfer in three systems -- graphene, molybdenum disulfide (MoS$_2$), and hexagonal boron nitride (h-BN). Our results show that the near-field heat flux exceeds the blackbody limit up to four orders of magnitude. With the increase of the distances between two parallel sheets, a $1/d^2$ dependence of heat flux is shown, consistent with Coulomb's law. The heat transfer capacity is sensitive to the dielectric properties of materials. Influences from chemical potential and temperature are also discussed. Our method can be applied to a wide range of materials including systems with inhomogeneities.

physics.comp-ph↗

arXiv2021

Let' s Trade in The Future! A Futures-Enabled Fast Resource Trading Mechanism in Edge Computing-Assisted UAV Networks

Mobile edge computing (MEC) has emerged as one of the key technical aspects of the fifth-generation (5G) networks. The integration of MEC with resource-constrained unmanned aerial vehicles (UAVs) could enable flexible resource provisioning for supporting dynamic and computation-intensive UAV applications. Existing resource trading could facilitate this paradigm with proper incentives, which, however, may often incur unexpected negotiation latency and energy consumption, trading failures and unfair pricing, due to the unpredictable nature of the resource trading process. Motivated by these challenges, an efficient futures-based resource trading mechanism for edge computing-assisted UAV network is proposed, where a mutually beneficial and risk-tolerable forward contract is devised to promote resource trading between an MEC server (seller) and a UAV (buyer). Two key problems i.e. futures contract design before trading and power optimization during trading are studied. By analyzing historical statistics associated with future resource supply, demand, and air-to-ground communication quality, the contract design is formulated as a multi-objective optimization problem, aiming to maximize both the seller's and the buyer's expected utilities, while estimating their acceptable risk tolerance. Accordingly, we propose an efficient bilateral negotiation scheme to help players reach a trading consensus on the amount of resources and the relevant price. For the power optimization problem, we develop a practical algorithm that enables the buyer to determine its optimal transmission power via convex optimization techniques. Comprehensive simulations demonstrate that the proposed mechanism offers both players considerable utilities, while outperforming the onsite trading mechanism on trading failures and fairness, negotiation latency, and cost.

cs.DC↗

arXiv2020

Current-induced phonon Hall effect

Since the first experimental observation of the phonon Hall effect (PHE) in 2005, its physical origin and theoretical explanation have been extensively investigated. While spin-orbit interactions are believed to play important roles under external magnetic fields, nonmagnetic effects are also possible. Here, we propose a mechanism of PHE which is induced by electric current in a nonequilibrium system through electron-phonon interactions. The influence of the drift electrons to the phonon degrees of freedom, as a correction to the Born-Oppenheimer approximation, is represented by an antisymmetric matrix which has the same form as in a typical phonon Hall model. We demonstrate the idea with a graphene-like hexagonal lattice having a finite phonon Hall conductivity under a driven electric current.

cond-mat.mtrl-sci↗

arXiv2020

Highly Anisotropic Electronic and Mechanical Properties of Monolayer and Bilayer As2S3

Anisotropic materials, with orientation-dependent properties, have attracted more and more attention due to their compelling tunable and flexible performance in electronic and optomechanical devices. So far, two-dimensional (2D) black phosphorus shows the largest known anisotropic behavior, which is highly desired for synaptic and neuromorphic devices, multifunctional directional memories, and even polarization-sensitive photodetector, whereas it is unstable at ambient conditions. Recently, 2D few-layered As2S3 with superior chemical stability was successfully exfoliated in experiments. However, the electronic and mechanical properties of monolayer and bilayer As2S3 is still lacking. Here, we report the large anisotropic electronic and mechanical properties of As2S3 systems through first-principles calculations and general angle-dependent Hooke's law. Monolayer and bilayer As2S3 exhibit anisotropic factors of Young's modulus of 3.15 and 3.32, respectively, which are larger than the black phosphorous with experimentally confirmed and an anisotropic factor of 2. This study provides an effective route to flexible orientation-dependent nanoelectronics, nanomechanics, and offers implications in promoting related experimental investigations.

cond-mat.mtrl-sci↗