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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., Comment: 8 pages, 5 figures, magazine paper

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.

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., Comment: conference paper with 6 pages and 5 figures. arXiv admin note: text overlap with arXiv:2411.01458

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.

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.

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., Comment: 16 pages, 9 figures, and with extra appendix

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.

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., Comment: 7 pages, 4 figures

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., Comment: 15 pages, 12 figures, regular journal

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., Comment: 9 pages, 4 figures

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.

Combined with first-principles calculations and semiclassical Boltzmann transport theory, Janus {\alpha}-STe2 and {\alpha}-SeTe2 monolayers are investigated systematically. Janus {\alpha}-STe2 and {\alpha}-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 {\alpha}-Te monolayer (2.8). The higher ZT originates from Janus structures reduce lattice thermal conductivities remarkably compared with pristine {\alpha}-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 {\alpha}-STe2 and {\alpha}-SeTe2 monolayers as thermoelectric materials and highlights Janus structure as an effective way to enhance thermoelectric performance.

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.

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.

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., Comment: 10 pages, 8 figures

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.

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.

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., Comment: 13 pages, 6 figures

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., Comment: 14 pages, 8 figures

The electronic and thermal transport properties have been systematically investigated in monolayer C$_4$N$_3$H with first-principles calculations. The intrinsic thermal conductivity of monolayer C$_4$N$_3$H was calculated coupling with phonons Boltzmann transport equation. For monolayer C$_4$N$_3$H, the thermal conductivity (\k{appa}) (175.74 and 157.90 W m-1K-1 with a and b-plane, respectively) is significantly lower than that of graphene (3500 Wm$^{-1}$K$^{-1}$) and C3N(380 Wm$^{-1}$K$^{-1}$). Moreover, it is more than the second time higher than C$_2$N (82.88 Wm$^{-1}$K$^{-1}$) at 300 K. Furthermore, the group velocities, relax time, anharmonicity, as well as the contribution from different phonon branches, were thoroughly discussed in detail. A comparison of the thermal transport characters among 2D structure for monolayer C$_4$N$_3$H, graphene, C$_2$N and C$_3$N has been discussed. This work highlights the essence of phonon transport in new monolayer material., Comment: 25 pages, 8 figures

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., Comment: 8 pages, 5 figures

We systematically calculate the structure, formation enthalpy, formation free energy, elastic constants and electronic structure of Ti$_{0.98}$X$_{0.02}$ system by density functional theory (DFT) simulations to explore the effect of transition metal X (X=Ag, Cd, Co, Cr, Cu, Fe, Mn, Mo, Nb, Ni, Pd, Rh, Ru, Tc, and Zn) on the stability mechanism of $\beta$-titanium. Based on our calculations, the results of formation enthalpy and free energy show that adding trace X is beneficial to the thermodynamic stability of $\beta$-titanium. This behavior is well explained by the density of state (DOS). However, the tetragonal shear moduli of Ti$_{0.98}$X$_{0.02}$ systems are negative, indicating that $\beta$-titanium doping with a low concentration of X is still elastically unstable at 0 K. Therefore, we theoretically explain that $\beta$-titanium doping with trace transition metal X is unstable in the ground state., Comment: 19 pages, 4 figures, 3 tables

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., Comment: 7 pages, 6 figures

With the advantages of Millimeter wave in wireless communication network, the coverage radius and inter-site distance can be further reduced, the ultra dense network (UDN) becomes the mainstream of future networks. The main challenge faced by UDN is the serious inter-site interference, which needs to be carefully addressed by joint user association and resource allocation methods. In this paper, we propose a multi-agent Q-learning based method to jointly optimize the user association and resource allocation in UDN. The deep Q-network is applied to guarantee the convergence of the proposed method. Simulation results reveal the effectiveness of the proposed method and different performances under different simulation parameters are evaluated., Comment: 5 pages, 5 figures

Software-defined internet of vehicles (SDIoV) has emerged as a promising paradigm to realize flexible and comprehensive resource management, for next generation automobile transportation systems. In this paper, a vehicular cloud computing-based SDIoV framework is studied wherein the joint allocation of transmission power and graph job is formulated as a nonlinear integer programming problem. To effectively address the problem, a structure-preservation-based two-stage allocation scheme is proposed that decouples template searching from power allocation. Specifically, a hierarchical tree-based random subgraph isomorphism mechanism is applied in the first stage by identifying potential mappings (templates) between the components of graph jobs and service providers. A structure-preserving simulated annealing-based power allocation algorithm is adopted in the second stage to achieve the trade-off between the job completion time and energy consumption. Extensive simulations are conducted to verify the performance of the proposed algorithms., Comment: 6 pages, 4 figures, INFOCOM WORKSHOP 2020

Charge-density-wave (CDW) instability and pressure-induced superconductivity in bulk 1T-NbS2 are predicted theoretically by first-principles calculations. We reveal a CDW instability towards the formation of a stable commensurate CDW order, resulting in a sqart(13)*sqart(13) structural reconstruction featured with star-of-David clusters. The CDW phase exhibits one-dimensional metallic behavior with in-plane flat-band characteristics, and coexists with an orbital-density-wave order predominantly contributed by 4d_(z^2-r^2 ) orbital from the inner Nb atoms of the star-of-David cluster. By doubling the cell of the CCDW phase along the layer stacking direction, a metal-insulator transition may be realized in the CDW phase in case the interlayer antiferromagnetic ordering and Coulomb correlation effect have been considered simultaneously. Bare electron susceptibility, phonon linewidth and electron-phonon coupling calculations suggest that the CDW instability is driven by softened phonon modes due to the strong electron-phonon coupling interactions. CDW order can be suppressed by pressure, concomitant with appearance of superconductivity. Our theoretical predictions call for experimental investigations to further clarify the transport and magnetic properties of 1T-NbS2. Furthermore, it would also be very interesting to explore the possibility to realize the CDW order coexisting with the superconductivity in bulk 1T-NbS2., Comment: 24 pages,9 figures +supplemental material,submitted to PRB

Vehicular cloud computing has emerged as a promising solution to fulfill users' demands on processing computation-intensive applications in modern driving environments. Such applications are commonly represented by graphs consisting of components and edges. However, encouraging vehicles to share resources poses significant challenges owing to users' selfishness. In this paper, an auction-based graph job allocation problem is studied in vehicular cloud-assisted networks considering resource reutilization. Our goal is to map each buyer (component) to a feasible seller (virtual machine) while maximizing the buyers' utility-of-service, which concerns the execution time and commission cost. First, we formulate the auction-based graph job allocation as an integer programming (IP) problem. Then, a Vickrey-Clarke-Groves based payment rule is proposed which satisfies the desired economical properties, truthfulness and individual rationality. We face two challenges: 1) the above-mentioned IP problem is NP-hard; 2) one constraint associated with the IP problem poses addressing the subgraph isomorphism problem. Thus, obtaining the optimal solution is practically infeasible in large-scale networks. Motivated by which, we develop a structure-preserved matching algorithm by maximizing the utility-of-service-gain, and the corresponding payment rule which offers economical properties and low computation complexity. Extensive simulations demonstrate that the proposed algorithm outperforms the benchmark methods considering various problem sizes., Comment: 14 pages, 8 figures

Silicon is one of the most frequently used chemical elements of the periodic table in nanotechnology. Two-dimensional (2D) silicene, a silicon analog of graphene, has been readily obtained to make field-effect transistors since 2015. Recently, as new members of the silicene family, penta-silicene and its nanoribbon have been experimentally grown on Ag(110) surface with exotic electronic properties. However, the thermoelectric performance of penta-silicene has not been so far studied that would hinder its potential applications of electric generation from waste heat and solid-state Peltier coolers. Based on the Boltzmann transport theory and ab initio calculations, we find that penta-silicene shows a remarkable room temperature figure of merit ZT of 3.4 and 3.0 at the reachable hole and electron concentrations, respectively. We attribute this high ZT to the superior pudding-mold electronic band structure and ultralow lattice thermal conductivity. The discovery provides new insight into the transport property of pentagonal nanostructures and highlights the potential applications of thermoelectric materials at room temperature., Comment: 17 pages, 5 figures, and 3 tables

The successful isolation of cyclo-C18 in the experiment means the ground-breaking epoch of carbon rings. Herein, we studied the thermodynamic stabilities of cyclo-Cn (4 $\leq$ n $\leq$ 34) with density functional theory. When n = 4N + 2 (N is integer), cyclo-Cn (10 $\leq$ n $\leq$ 34) were thermodynamically stable. Especially, cyclo-C10 and cyclo-C14 were thermodynamically, kinetically, dynamically, and optically preferred to cyclo-C18, and were the candidates of zero-dimension carbon rings. The carbon atoms were sp hybridization in cyclo-C10, cyclo-C14 and cyclo-C18. Cyclo-C14 and cyclo-C18 had alternating abnormal single and triple bonds, but cyclo-C10 had equal bonds. Cyclo-C10, cyclo-C14, and cyclo-C18 with large aromaticities had out-plane and in-plane pi systems, which were perpendicular to each other. The number of pi electrons in out-plane and in-plane pi systems followed the Huckel rule. Simulated UV-vis-NIR spectra indicated the similar electronic structures of cyclo-C14 and cyclo-C18., Comment: 23 pages, 6 figures, 1 table

The lattice thermal conductivity and thermal transport properties of 2D $\alpha$-selenene are investigated based on the first-principles calculations. The isotropic in-plane thermal conductivity is as low as 3.04 W m$^{-1}$ K$^{-1}$ at room temperature, even abnormally lower than $\alpha$-tellurene which processes analogous configuration and lower Debye temperature. We find this abnormal phenomenon reasonably stems from the larger anharmonicity of the acoustic phonon branch. Moreover, the phonon spectra, elastic properties, and related thermal properties are also exhibited. Acoustic phonons contribute mainly to the total thermal conductivity. Furthermore, size effect, boundary effect, the total phase space for three-phonon processes, phonon group velocity and relaxation time are further investigated, and the last one is unveiled to be the key ingredient of thermal transport in 2D selenene., Comment: 15 pages, 5 figures, 1 table

Vehicular cloud computing has emerged as a promising paradigm for realizing user requirements in computation-intensive tasks in modern driving environments. In this paper, a novel framework of multi-task offloading over vehicular clouds (VCs) is introduced where tasks and VCs are modeled as undirected weighted graphs. Aiming to achieve a trade-off between minimizing task completion time and data exchange costs, task components are efficiently mapped to available virtual machines in the related VCs. The problem is formulated as a non-linear integer programming problem, mainly under constraints of limited contact between vehicles as well as available resources, and addressed in low-traffic and rush-hour scenarios. In low-traffic cases, we determine optimal solutions; in rush-hour cases, a connection-restricted randommatching-based subgraph isomorphism algorithm is proposed that presents low computational complexity. Evaluations of the proposed algorithms against greedy-based baseline methods are conducted via extensive simulations.

In this paper, three novel metallic sp2/sp3 -hybridized Boron Nitride (BN) polymorphs are proposed by first-principles calculations. One of them, named as tP-BN, is predicted based on the evolutionary particle swarm structural search. tP-BN is constructed by two interlocked rings forming a tube-like 3D network. The stability and band structure calculations show tP-BN is metastable and metallic at zero pressure. Calculations for the density of states and electron orbits confirm that the metallicity originates from the sp2 -hybridized B and N atoms, and forming 1D linear conductive channels in the 3D network. According to the relationship between the atomic structure and electronic properties, another two 3D metastable metallic sp2/sp3 -hybridized BN structures are constructed manually. Electronic properties calculations show that both of these structures have 1D conductive channel along different axes. The polymorphs predicted in this study enrich the structures and provide a different picture of the conductive mechanism of BN compounds., Comment: 21 pages, 7 figures

We report a previously unknown monolayer borophene allotrope and we call it super-B with a flat structure based on the ab initio calculations. It has good thermal, dynamical, and mechanical stability compared with many other typical borophenes. We find that super-B has a fascinating chemical bond environment consisting of standard sp, sp2 hybridizations, and delocalized five-center three-electron $\pi$ bond, called $\pi$(5c-3e). This particular electronic structure plays a pivotal role in stabilizing the super-B chemically. By extra doping, super-B can be transformed into a Dirac material from pristine metal. Like graphene, it can also sustain tensile strain smaller than 24%, indicating superior flexibility. Moreover, due to the small atomic mass and large density of states at the Fermi level, super-B has the highest critical temperature Tc of 25.3 K in single-element superconductors at ambient conditions. We attribute this high Tc of super-B to the giant anharmonicity of two linear acoustic phonon branches and an unusually low optic phonon mode. These predictions provide new insight into the chemical nature of low dimensional boron nanostructures and highlight the potential applications of designing flexible devices and high Tc superconductors., Comment: 16 pages, 7 figures, 3 tables, 10 formula

We study the lattice thermal conductivity of two-dimensional (2D) pentagonal systems, such as penta-silicene and penta-germanene. Penta-silicene has been recently reported, while the stable penta-germanene, belonging to the same group IV element, is revealed firstly by our ab initio calculations. We find that both penta-silicene and penta-germanene at room temperature have ultralow lattice thermal conductivities $\kappa$ of 1.29 W/mK and 0.30 W/mK respectively. To the best of our knowledge, penta-germanene may have the lowest $\kappa$ in 2D crystal materials. We attribute ultralow $\kappa$ to the weak phonon harmonic interaction and strong anharmonic scattering. A small phonon group velocity, a small Debye frequency, a large Gruneisen parameter, and a large number of modes available for phonon-phonon interplay together lead to the ultralow $\kappa$ of penta-silicene and penta-germanene. These discoveries provide new insight into the manipulation of ultralow $\kappa$ in 2D materials and highlight the potential applications of designing silicon and germanium based high thermoelectric materials., Comment: 11 pages, 6 figures, 1 table

In 2016, bulk tellurium was experimentally observed as a remarkable thermoelectric material. Recently, two-dimensional (2D) tellurium, called tellurene, has been synthesized and has exhibited unexpected electronic properties compared with the 2D MoS$_2$. They have also been fabricated into air-stable and high efficient field-effect transistors. There are two stable 2D tellurene phases. One ($\beta$-Te) has been confirmed with an ultralow lattice thermal conductivity ($\kappa_L$). However, the study of the transport properties of the other more stable phase, $\alpha$-Te, is still lacking. Here, we report the thermoelectric performance and phonon properties of $\alpha$-Te using Boltzmann transport theory and first principle calculations. A maximum ZT value of 0.83 is achieved under reasonable hole concentration, suggesting that the monolayer $\alpha$-Te is a potential competitor in the thermoelectric field., Comment: 14 pages, 5 figures

2H phase (trigonal prismatic D3h) of layered two-dimensional (2D) transition metal dichalcogenides (TMDs) have attracted a lot of interests due to the superior electronic and optoelectronic properties. However, flexible electronic devices and thermoelectric performances based on 2H phase have been potentially limited by the strain sensitive electronic band gap and high lattice thermal conductivity (kappa_L). Here, we predict and calculate two 1T (octahedral Oh) phase monolayer telluride materials SnTe2 and SiTe2 with soft mechanics, ultralow kappa_L and electronic properties. The calculated in-plane Young's modulus of monolayer SnTe2 is softer than most of 1T-MX2 compounds. Furthermore, monolayer SiTe2 and SnTe2 also have relatively flexible electronic properties under large biaxial strain, indicating potential flexible electrode materials. Meanwhile, monolayer SiTe2 and SnTe2 both exhibit ultralow \k{appa}_L (2.27 W/mK of SiTe2 and 1.62 W/mK of SnTe2) at room temperature. Considering both acoustic and polar optical phonon scattering of the electronic relaxation time, the figure of merit (ZT) can achieve 0.46 at 600 K and 0.71 at 900 K for monolayer SiTe2 and SnTe2 respectively., Comment: 20 pages, 6 figures, 3 tables

On the basis of the first principles simulation, the structure, formation enthalpy, and mechanical properties (elastic constant, bulk, and shear modulus and hardness) of five Nb-doped Ni systems are systematically studied. The calculated equilibrium volume increases with the Nb concentration increasing. The computational elastic constants and formation enthalpy indicate that all Nb-doped Ni systems are mechanically and thermodynamically stable in our research. The hardness of these systems also be predicted after the bulk modulus and shear modulus have been accurately calculated. The results show that the hardness increases with the Nb concentration increasing when the Nb concentration below 4.9%, beyond which the hardness will decrease within the scope of our study., Comment: 21 pages, 6 figures, 5 tables

Recently, $\beta$-Te (atomically 2D tellurium) with rectangular crystal structure has been synthesized successfully on highly oriented pyrolytic graphite substrates by using molecular beam epitaxy. It has been found possessing remarkable properties such as ultralow lattice thermal conductivity and high thermoelectric efficiency. Based on the first-principles calculations, we study the thermal expansion and thermomechanic properties of the experimental phase monolayer $\beta$-Te, using quasiharmonic approach. It is found $\beta$-Te shows large positive thermal expansion at elevated temperature, while the linear thermal expansion coefficient is negative along a direction at very low temperature. The linear thermal expansion coefficient along b direction is 4.9*10$^{-5}$ K$^{-1}$ at 500 K, which is considerably large in 2D materials. $\beta$-Te exhibits strong in-plane anisotropy, including thermal expansion, 2D elastic moduli and Poisson's ratios. However, the elastic moduli, Poisson's ratios and the in-plane anisotropy are weakened with increasing temperature, and the variations are dominated by the generalized mode Gr\"{u}neisen parameters., Comment: 25 pages, 7 figures, 14 formulas

In search of an improved strategy to form low resistance contacts to MoS2 and related semiconducting transition metal dichalcogenides, we use ab initio density functional electronic structure calculations in order to determine the equilibrium geometry and electronic structure of MoO3/MoS2 and MoO2/MoS2 bilayers. Our results indicate that, besides a rigid band shift associated with charge transfer, the presence of molybdenum oxide modifies the electronic structure of MoS2 very little. We find that the charge transfer in the bilayer provides a sufficient degree of hole doping to MoS2, resulting in a highly transparent contact region., Comment: 14 pages, 9 figures

Based on first-principles calculations, we study systematically the ideal tensile stress-strain relations of three monoatomic group-V monolayer two dimensional (2D) materials with buckled honeycomb lattices: blue phosphorene, arsenene, and antimonene. The ideal strengths and critical strains for these 2D materials are investigated under uniaxial and equibiaxial strains. It is found that the ideal strengths decrease significantly as the atomic number increases, while the critical strains change not so much. In particular, the ideal strength of antimonene along armchair direction is found to exceed Griffith strength limit. The distributions of charge density, buckling heights, bond lengths, and bond angles are also studied under different types of strains. It can be concluded that the critical strain is determined by the stretch and rotation of bonds simultaneously. Furthermore, the phonon dispersions, phonon instabilities, and failure mechanism of these materials under three types of strains are also calculated and explored., Comment: 24 pages, 8 figures

Graph jobs represent a wide variety of computation-intensive tasks in which computations are represented by graphs consisting of components (denoting either data sources or data processing) and edges (corresponding to data flows between the components). Recent years have witnessed dramatic growth in smart vehicles and computation-intensive graph jobs, which pose new challenges to the provision of efficient services related to the Internet of Vehicles. Fortunately, vehicular clouds formed by a collection of vehicles, which allows jobs to be offloaded among vehicles, can substantially alleviate heavy on-board workloads and enable on-demand provisioning of computational resources. In this paper, we present a novel framework for vehicular clouds that maps components of graph jobs to service providers via opportunistic vehicle-to-vehicle communication. Then, graph job allocation over vehicular clouds is formulated as a non-linear integer programming with respect to vehicles' contact duration and available resources, aiming to minimize job completion time and data exchange cost. The problem is addressed for two scenarios: low-traffic and rush-hours. For the former, we determine the optimal solutions for the problem. In the latter case, given the intractable computations for deriving feasible allocations, we propose a novel low complexity randomized graph job allocation mechanism by considering hierarchical tree based subgraph isomorphism. We evaluate the performance of our proposed algorithms through extensive simulations., Comment: 11 pages, 6 Figures