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Solar energy is one of the most promising renewable energy resources. Forecasting photovoltaic power generation is an important way to increase photovoltaic penetration. However, the difficulty in qualifying the uncertainty of PV power generation, especially during hazy weather, makes forecasting challenging. This paper proposes a novel model to address the issue. We introduce a modified entropy to qualify uncertainty during hazy weather while clustering and attention mechanisms are employed to reduce computational costs and enhance forecasting accuracy, respectively. Hyperparameters were adjusted using an optimization algorithm. Experiments on two datasets related to hazy weather demonstrate that our model significantly improves forecasting accuracy compared to existing models., Comment: The manuscript was submitted to Applied Energy on August 29, 2024

Tuning thermal transport in nanostructures is essential for many applications, such as thermal management and thermoelectrics. Nanophononic metamaterials (NPM) have shown great potential for reducing thermal conductivity by introducing local resonant hybridization. In this work, the thermal conductivity of NPM with crystalline Si (c-Si) pillar, crystalline Ge (c-Ge) pillar and amorphous Si (a-Si) pillar are systematically investigated by molecular dynamics method. The analyses of phonon dispersion and spectral energy density show that phonon dispersions of Si membrane are flattened due to local resonant hybridization induced by both crystalline and amorphous pillar. In addition, a-Si pillar can cause larger reduction of thermal conductivity compared with c-Si pillar. Specifically, when increasing the atomic mass of atoms in pillars, the thermal conductivity of NPMs with crystalline pillar is increased because of the weakened phonon hybridization, however, the thermal conductivity of NPMs with amorphous pillar is almost unchanged, which indicates that the phonon transports are mainly affected by the scatterings at the interface between amorphous pillar and Si membrane. The results of this work can provide meaningful insights on controlling thermal transport in NPMs by choosing the materials and atomic mass of pillars for specific applications.

Atomic motion in nanopillars standing on the surface of a silicon membrane generates vibrons, which are wavenumber-independent phonons that act as local resonances. These vibrons couple with heat-carrying phonons traveling along the base membrane causing a reduction in the in-plane lattice thermal conductivity. In this work, we examine isolated silicon and gallium nitride nanopillars and for each compare the vibrons density of states (DOS) to those of phonons in an isolated uniform silicon membrane. We show that while the phonon-vibron DOS conformity across the full spectrum is a key factor in reducing the thermal conductivity of the assembled nanostructure, the presence of an intense vibron population at more dominant low frequencies plays a competing role. We report predictions from molecular dynamics simulations showing lower thermal conductivities for a silicon membrane with gallium-nitride nanopillars compared to a silicon membrane with silicon nanopillars.

Remote Sensing Target Fine-grained Classification (TFGC) is of great significance in both military and civilian fields. Due to location differences, growth in data size, and centralized server storage constraints, these data are usually stored under different databases across regions/countries. However, privacy laws and national security concerns constrain researchers from accessing these sensitive remote sensing images for further analysis. Additionally, low-resource remote sensing devices encounter challenges in terms of communication overhead and efficiency when dealing with the ever-increasing data and model scales. To solve the above challenges, this paper proposes a novel Privacy-Reserving TFGC Framework based on Federated Learning, dubbed PRFL. The proposed framework allows each client to learn global and local knowledge to enhance the local representation of private data in environments with extreme statistical heterogeneity (non. Independent and Identically Distributed, IID). Thus, it provides highly customized models to clients with differentiated data distributions. Moreover, the framework minimizes communication overhead and improves efficiency while ensuring satisfactory performance, thereby enhancing robustness and practical applicability under resource-scarce conditions. We demonstrate the effectiveness of the proposed PRFL on the classical TFGC task by leveraging four public datasets., Comment: Under Review, 23 pages, 3 figures, 12 tables

Interfacial thermal resistance arises challenges for the thermal management as the modern semiconductors are miniatured to nanoscale. Previous studies found that graded mass distribution in interface can maximumly enhance the interfacial thermal conductance of crystalline interface, however, whether this strategy is effective for amorphous interface is less explored. In this work, graded mass distribution in the amorphous interface between crystalline Si and crystalline Ge is optimized to increase the interfacial thermal conductance by the extended atomistic Greens function method.

Realization of practical superconducting quantum computing requires many qubits of long coherence time. Compared to the commonly used Ta deposited on c-plane sapphire, which occasionally form {\alpha}-Ta (111) grains and \b{eta}-tantalum grains, high quality Ta (110) film can grow epitaxial on a-plane sapphire because of the atomic relationships at the interface. Well-ordered {\alpha}-Ta (110) film on wafer-scale a-plane sapphire has been prepared. The film exhibits high residual resistance ratio. Transmon qubits fabricated using these film shows relaxation times exceeding 150 {\mu}s. The results suggest Ta film on a-plane sapphire is a promising choice for long coherence time qubit on wafer scale.

Polycrystalline {\alpha}-tantalum (110) films deposited on c-plane sapphire substrate by sputtering are used in superconducting qubits nowadays. However, these films always occasionally form other structures, such as {\alpha}-tantalum (111) grains and \b{eta}-tantalum grains. To improve the film quality, we investigate the growth of {\alpha}-tantalum (110) films on a-plane sapphire substrate under varying conditions by molecular beam epitaxy technology. The optimized {\alpha}-tantalum (110) film is single crystal, with a smooth surface and atomically flat metal-substrate interface. The film with thickness of 30 nm shows a Tc of 4.12K and a high residual resistance ratio of 9.53. The quarter wavelength coplanar waveguide resonators fabricated with the 150 nm optimized {\alpha}-tantalum (110) film, exhibits intrinsic quality factor of over one million under single photon excitation at millikelvin temperature.

The presence of amorphous oxide layers can significantly affect the coherent time of superconducting qubits due to their high dielectric loss. Typically, the surface oxides of superconductor films exhibit lossy and unstable behavior when exposed to air. To increase the coherence time, it is essential for qubits to have stable and low dielectric loss oxides, either as barrier or passivation layers. In this study, we highlight the robust and stable nature of an amorphous tantalum oxide layer formed on {\alpha}-Ta (110) film by employing chemical and structural analyses. Such kind of oxide layer forms in a self-limiting process on the surface of {\alpha}-Ta (110) film in piranha solution, yielding stable thickness and steady chemical composition. Quarter wavelength coplanar waveguide resonators are made to study the loss of this oxide. One resonator has a Qi of 3.0x10^6 in the single photon region. The Qi of most devices are higher than 2.0x10^6. Moreover, most of them are still over 1x10^6 even after exposed to air for months. Based on these findings, we propose an all-tantalum superconducting qubit utilizing such oxide as passivation layers, which possess low dielectric loss and improved stability.

Thermoelectric materials convert heat into electricity through thermally driven charge transport in solids, or vice versa for cooling. To be competitive with conventional energy-generation technologies, a thermoelectric material must possess the properties of both an electrical conductor and a thermal insulator. However, these properties are normally mutually exclusive because of the interconnection of the scattering mechanisms for charge carriers and phonons. Recent theoretical investigations on sub-device scales have revealed that silicon membranes covered by nanopillars exhibit a multitude of local phonon resonances, spanning the full spectrum, that couple with the heat-carrying phonons in the membrane and collectively cause a reduction in the in-plane thermal conductivity$-$while, in principle, not affecting the electrical properties because the nanopillars are external to the pathway of voltage generation and charge transport. Here this effect is demonstrated experimentally for the first time by investigating device-scale suspended silicon membranes with GaN nanopillars grown on the surface. The nanopillars cause up to 21 % reduction in the thermal conductivity while the electrical conductivity and the Seebeck coefficient remain unaffected, thus demonstrating an unprecedented decoupling in the semiconductor's thermoelectric properties. The measured thermal conductivity behavior for coalesced nanopillars and corresponding lattice-dynamics calculations provide further evidence that the reductions are mechanistically tied to the phonon resonances. This finding breaks a longstanding trade-off between competing properties in thermoelectricity and paves the way for engineered high-efficiency solid-state energy recovery and cooling.

The thermal management have been widely focused due to broad applications. Generally, the deformation can largely tune the thermal transport. The main challenge of flexible electronics/ materials is to maintain thermal conductance under large deformation. This work investigates the thermal conductance of a nano-designed Si metamaterial constructed with curved nanobeams by molecular dynamics simulation. Interestingly, it shows that the thermal conductance of the nano-designed Si metamaterial is insensitive under a large deformation (strain~-41%). The new feature comes from the designed curved nanobeams which makes a quasi-zero stiffness. Further calculations show that, when under a large deformation, the average stress in nanobeam is ultra-small (<151 MPa) and its phonon density of states are little changed. This work provides valuable insights on multifunction, such as both stable thermal and mechanical properties, of nano-designed metamaterials.

Interfacial thermal transport is critical for many thermal-related applications such as heat dissipation in electronics. While the total interfacial thermal conductance (ITC) can be easily measured or calculated, the ITC spectral mapping has been investigated only recently and is not fully understood. By combining nonequilibrium molecular dynamics simulations and atomistic Green's function method, we systematically investigate the ITC spectrum across an ideal interface, i.e., the argon-heavy argon interface. Our results show that the ITC spectrum increases gradually with temperature as more phonons and anharmonic scattering channels are activated, e.g., the vibrations with frequencies larger than 1 THz can contribute 5% (15%) to the total ITC at 2 K (40 K) through anharmonic phonon scatterings channels. We further find that the ITC spectrum from the left interfacial Hamiltonian is quite different from that of the right interfacial Hamiltonian, which stems from the asymmetry of anharmonic phonon scatterings caused by the dissimilar vibrational property of the two interfacial contacts. While all the phonons are involved in the anharmonic scatterings for the heavy argon interfacial Hamiltonian, these phonons involved in the anharmonic phonon scatterings from the argon interfacial Hamiltonians are mainly these vibrations with frequency smaller than 1 THz (i.e., the cut-off frequency of heavy argon). Finally, we find the quantum effect is important for the ITC spectrum at low temperatures, e.g., below 30 K in our systems. Our results here systematically investigate the influence of anharmonicity, non-homogeneity, and quantum effects on the ITC spectrum, which is critical for designing and optimizing the interfaces with better performance.

With the tremendous expansion of graphs data, node classification shows its great importance in many real-world applications. Existing graph neural network based methods mainly focus on classifying unlabeled nodes within fixed classes with abundant labeling. However, in many practical scenarios, graph evolves with emergence of new nodes and edges. Novel classes appear incrementally along with few labeling due to its newly emergence or lack of exploration. In this paper, we focus on this challenging but practical graph few-shot class-incremental learning (GFSCIL) problem and propose a novel method called Geometer. Instead of replacing and retraining the fully connected neural network classifer, Geometer predicts the label of a node by finding the nearest class prototype. Prototype is a vector representing a class in the metric space. With the pop-up of novel classes, Geometer learns and adjusts the attention-based prototypes by observing the geometric proximity, uniformity and separability. Teacher-student knowledge distillation and biased sampling are further introduced to mitigate catastrophic forgetting and unbalanced labeling problem respectively. Experimental results on four public datasets demonstrate that Geometer achieves a substantial improvement of 9.46% to 27.60% over state-of-the-art methods., Comment: Accepted to KDD2022

Thermal transport of nanocrystalline Si is of great importance for the application of thermoelectrics. A better understanding of the modal thermal conductivity of nanocrystalline Si will be expected to benefit the efficiency of thermoelectrics. In this work, the variance reduced Monte Carlo simulation with full band of phonon dispersion is applied to study the modal thermal conductivity of nanocrystalline Si. Importantly, the phonon modal transmissions across the grain boundaries which are modeled by the amorphous Si interface are calculated by the mode-resolved atomistic Greens function method. The predicted ratios of thermal conductivity of nanocrystalline Si to that of bulk Si agree well with that of the experimental measurements in a wide range of grain size. The thermal conductivity of nanocrystalline Si is decreased from 54 percent to 3 percent and the contribution of phonons with mean free path larger than the grain size increases from 30 percent to 96 percnet as the grain size decreases from 550 nm to 10 nm. This work demonstrates that the full band Monte Carlo simulation using phonon modal transmission by the mode-resolved atomistic Greens function method can capture the phonon transport picture in complex nanostructures, and therefore can provide guidance for designing high performance Si based thermoelectrics.

As a critical way to modulate thermal transport in nanostructures, phonon resonance hybridization has become an issue of great concern in the field of phonon engineering. In this work, we optimized phonon transport across graphene nanoribbon and obtained minimized thermal conductance by means of designing pillared nanostructures based on resonance hybridization. Specifically, the optimization of thermal conductance was performed by the combination of atomic Green` s function and Bayesian optimization. Interestingly, it is found that thermal conductance decreases non-monotonically with the increasing of number for pillared structure, which is severed as resonator and blocks phonon transport. Further mode-analysis and atomic Green` s function calculations revealed that the anomalous tendency originates from decreased phonon transmission in a wide frequency range. Additionally, nonequilibrium molecular dynamics simulations are performed to verify the results with the consideration of high-order phonon scattering. This finding provides novel insights into the control of phonon transport in nanostructures.

Tuning interfacial thermal conductance has been a key task for the thermal management of nanoelectronic devices. Here, it is studied how the interfacial thermal conductance is great influenced by modulating the mass distribution of the interlayer of one-dimensional atomic chain. By nonequilibrium Green's function and machine learning algorithm, the maximum/minimum value of thermal conductance and its corresponding mass distribution are calculated. Interestingly, the mass distribution corresponding to the maximum thermal conductance is not a simple function, such as linear and exponential distribution predicted in previous works, it is similar to a sinusoidal curve around linear distribution for larger thickness interlayer. Further, the mechanism of the abnormal results is explained by analyzing the phonon transmission spectra and density of states. The work provides deep insight into optimizing and designing interfacial thermal conductance by modulating mass distribution of interlayer atoms.

Self-affine morphology of random interfaces governs their functionalities across tribological, geological, (opto-)electrical and biological applications. However, the knowledge of how energy carriers or generally classical/quantum waves interact with structural irregularity is still incomplete. In this work, we study vibrational energy transport through random interfaces exhibiting different correlation functions on the two-dimensional hexagonal lattice. We show that random interfaces at the atomic scale are Cantor composites populated on geometrical fractals, thus multifractals, and calculate their quantized conductance using atomistic approaches. We obtain a universal scaling law, which contains self-similarity for mass perturbation, and exponential scaling of structural irregularity quantified by fractal dimension. The multifractal nature and Cantor-composite picture may also be extendable to charge and photon transport across random interfaces.

Point cloud classification plays an important role in a wide range of airborne light detection and ranging (LiDAR) applications, such as topographic mapping, forest monitoring, power line detection, and road detection. However, due to the sensor noise, high redundancy, incompleteness, and complexity of airborne LiDAR systems, point cloud classification is challenging. In this paper, we proposed a directionally constrained fully convolutional neural network (D-FCN) that can take the original 3D coordinates and LiDAR intensity as input; thus, it can directly apply to unstructured 3D point clouds for semantic labeling. Specifically, we first introduce a novel directionally constrained point convolution (D-Conv) module to extract locally representative features of 3D point sets from the projected 2D receptive fields. To make full use of the orientation information of neighborhood points, the proposed D-Conv module performs convolution in an orientation-aware manner by using a directionally constrained nearest neighborhood search. Then, we designed a multiscale fully convolutional neural network with downsampling and upsampling blocks to enable multiscale point feature learning. The proposed D-FCN model can therefore process input point cloud with arbitrary sizes and directly predict the semantic labels for all the input points in an end-to-end manner. Without involving additional geometry features as input, the proposed method has demonstrated superior performance on the International Society for Photogrammetry and Remote Sensing (ISPRS) 3D labeling benchmark dataset. The results show that our model has achieved a new state-of-the-art level of performance with an average F1 score of 70.7%, and it has improved the performance by a large margin on categories with a small number of points (such as powerline, car, and facade).

Porous materials provide a large surface to volume ratio, thereby providing a knob to alter fundamental properties in unprecedented ways. In thermal transport, porous nanomaterials can reduce thermal conductivity by not only enhancing phonon scattering from the boundaries of the pores and therefore decreasing the phonon mean free path, but also by reducing the phonon group velocity. Here we establish a structure-property relationship by measuring the porosity and thermal conductivity of individual electrolessly etched single crystalline silicon nanowires using a novel electron beam heating technique. Such porous silicon nanowires exhibit extremely low diffusive thermal conductivity (as low as 0.33 Wm-1K-1 at 300K for 43% porosity), even lower than that of amorphous silicon. The origin of such ultralow thermal conductivity is understood as a reduction in the phonon group velocity, experimentally verified by measuring the Young modulus, as well as the smallest structural size ever reported in crystalline Silicon (less than 5nm). Molecular dynamics simulations support the observation of a drastic reduction in thermal conductivity of silicon nanowires as a function of porosity. Such porous materials provide an intriguing platform to tune phonon transport, which can be useful in the design of functional materials towards electronics and nano-electromechanical systems.

Silicon membranes patterned by nanometer-scale pillars standing on the surface provide a practical platform for thermal conductivity reduction by resonance hybridization. Using molecular simulations, we investigate the effect of nanopillar size, unit-cell size, and finite-structure size on the net capacity of the local resonators in reducing the thermal conductivity of the base membrane. The results indicate that the thermal conductivity reduction increases as the ratio of the volumetric size of a unit nanopillar to that of the base membrane is increased, and the intensity of this reduction varies with unit-cell size at a rate dependent on the volumetric ratio. Considering sample size, the resonance-induced thermal conductivity drop is shown to increase slightly with the number of unit cells until it would eventually level off., Comment: Manuscript is submitted for journal publication

The thermoelectric properties of n type nanoscale three dimensional (3D) Si phononic crystals (PnCs) with spherical pores are studied. Density functional theory and Boltzmann transport equation under the relaxation time approximation are applied to study the electronic transport coefficients, electrical conductivity, Seebeck coefficient and electronic thermal conductivity. We found that the electronic transport coefficients in 3D Si PnC at room temperature (300 K) change very little compared with that of Si, for example, electrical conductivity and electronic thermal conductivity is decreased by 0.26 to 0.41 and 0.39 to 0.55 depending on carrier concentration, respectively, and the Seebeck coefficient is similar to that of bulk Si. However, the lattice thermal conductivity of 3D Si PnCs with spherical pores is decreased by a factor of 500 calculated by molecular dynamics methods, leading to the ZT of 0.76, which is about 30 times of that of porous Si. This work indicates that 3D Si PnC is a promising candidate for high efficiency thermoelectric materials., Comment: 16 pages, 3 figures 1 table

We studied the thermal conductivity of graphene phononic crystal (GPnC), also named as graphene nanomesh, by molecular dynamics simulations. The dependences of thermal conductivity of GPnCs on both length and temperature are investigated. It is found that the thermal conductivity of GPnCs is significantly lower than that of graphene and can be efficiently tuned by changing the porosity and period length. For example, the ratio of thermal conductivity of GPnC to thermal conductivity of graphene can be changed from 0.1 to 0.01 when the porosity is changed from about 21% to 65%. The phonon participation ratio spectra reveal that more phonon modes are localized in GPnCs with larger porosity. Our results suggest that creating GPnCs is a valuable method to efficiently manipulate the thermal conductivity of graphene.

Thermoelectric material provides a high hope for converting harmful and useless heat into useful energy, electricity. It can also be used as solid state Peltier coolers in integrated circuits, an outstanding challenge for electronic engineers. The desire for a high efficient thermoelectric material has never been so keen. Although many works have been done, we are still far from having a recipe for thermoelectric materials. Nano-structuring provides an effective way to increase figure of merit (ZT) by reducing the thermal conductivity without affecting electronic property.1 Here, we propose a novel nanoscale three-dimensional (3D) Si phononic crystal (PnC) with spherical pores, which can reduce the thermal conductivity of bulk Si by a factor up to 10,000 times at room temperature. The extreme-low thermal conductivity could lead to a larger value of ZT than unity. The thermal conductivity changes little when temperature increases from room temperature to 1100 K. The phonon participation ratio spectra show there are more phonon localizations as the porosity of PnC increases., Comment: 3 figures

The thermal conductivity of nanostructures needs to be as small as possible so that it will have a greater efficiency for solid-state electricity generation/refrigeration by thermoelectrics. We studied how the period length and the mass ratio affect the thermal conductivity of isotopic nanoscale 3D phononic crystal of Si. Simulation results by equilibrium molecular dynamics show isotopic nanoscale 3D phononic crystal can make a significance reduction on the thermal conductivity of bulk Si at high temperature,1000 K. Size and mass effects are obvious in manipulating thermal conductivity. The thermal conductivity decreases as the period length and mass ratio increases. The phonon dispersion curves show the decrease of group velocities in 3D phononic crystals. The phonon's localization and band gap is clearly shown in spectra of normalized inverse participation ratio in nanoscale 3D phononic crystal structure., Comment: accepted by Scientific Reports