arXiv subjects

Peng Yu

Publications and source records attributed to Peng Yu.

At least 73 records · Page 4Linked to original sources

arXiv2024

Strain-driven phonon topological phase transition impedes thermal transport in titanium monoxide

Topological phonon states in crystalline materials have attracted significant research interests due to their importance for fundamental physical phenomena, yet their implication on phonon thermal transport remains largely unexplored. Here, we use density functional theory calculations and symmetry analyses to explore topological phonon phase transitions under uniaxial strains and their tuning effects on thermal transport in titanium monoxide (TiO). Our calculation shows that application of 10% tension significantly diminishes lattice thermal conductivity of TiO by 77% and 66% along the a and c axes, respectively, at room temperature. This suppression is found to result largely from the breaking of symmetry protected degeneracy of acoustic branches, which induces a substantial enhancement of phonon scattering phase space due to the easier fulfillment of scattering selection rules. Our study provides evidence for the importance of phononic band topology in modulating thermal conductivity and offers a promising route towards controlling solid-state heat transport.

physics.comp-ph↗

arXiv2024

Anomalous thermal transport and high thermoelectric performance of Cu-based vanadate CuVO3

Thermoelectric (TE) conversion technology, capable of transforming heat into electricity, is critical for sustainable energy solutions. Many promising TE materials contain rare or toxic elements, so the development of cost-effective and eco-friendly high-performance TE materials is highly urgent. Herein, we explore the thermal transport and TE properties of transition metal vanadate CuVO3 by using first-principles calculation. On the basis of unified theory of heat conduction, we uncover the hierarchical thermal transport feature in CuVO3, where wave-like tunneling makes a significant contribution to the lattice thermal conductivity (\k{appa}l) and result in the anomalously weak temperature dependence of \k{appa}l. This is primarily attributable to the complex phononic band structure caused by the heterogeneity of Cu-O and V-O bonds. Simultaneously, we report a high power factor of 5.45 mW K-2 m-1 realized in hole-doped CuVO3, which arises from a high electrical conductivity and a large Seebeck coefficient enabled by the multiple valleys and large electronic density of states near the valence band edge. Impressively, the low \k{appa}l and the high power factor make p-typed CuVO3 have ZT of up to 1.39, with the excellent average ZT above 1.0 from 300 to 600 K, which is superior to most reported Cu-based TE materials. Our findings suggest that CuVO3 compound is promising candidate for energy conversion applications in innovative TE devices.

physics.comp-ph↗

arXiv2024

Characterizing losses in InAs two-dimensional electron gas-based gatemon qubits

The tunnelling of cooper pairs across a Josephson junction (JJ) allow for the nonlinear inductance necessary to construct superconducting qubits, amplifiers, and various other quantum circuits. An alternative approach using hybrid superconductor-semiconductor JJs can enable superconducting qubit architectures with all electric control. Here we present continuous-wave and time-domain characterization of gatemon qubits and coplanar waveguide resonators based on an InAs two-dimensional electron gas. We show that the qubit undergoes a vacuum Rabi splitting with a readout cavity and we drive coherent Rabi oscillations between the qubit ground and first excited states. We measure qubit relaxation times to be $T_1 =$ 100 ns over a 1.5 GHz tunable band. We detail the loss mechanisms present in these materials through a systematic study of the quality factors of coplanar waveguide resonators. While various loss mechanisms are present in III-V gatemon circuits we detail future directions in enhancing the relaxation times of qubit devices on this platform.

quant-ph↗

arXiv2024

Ubiquitous non-Majorana Zero-Bias Conductance Peaks in Nanowire Devices

We perform tunneling measurements on indium antimonide nanowire/superconductor hybrid devices fabricated for the studies of Majorana bound states. At finite magnetic field, resonances that strongly resemble Majorana bound states, including zero-bias pinning, become common to the point of ubiquity. Since Majorana bound states are predicted in only a limited parameter range in nanowire devices, we seek an alternative explanation for the observed zero-bias peaks. With the help of a self-consistent Poission-Schrödinger multiband model developed in parallel, we identify several families of trivial subgap states which overlap and interact, giving rise to a crowded spectrum near zero energy and zero-bias conductance peaks in experiments. These findings advance the search for Majorana bound states through improved understanding of broader phenomena found in superconductor-semiconductor systems.

cond-mat.mes-hall↗

arXiv2024

Hot electrons and electromagnetic effects in the broadband Au, Ag, and Ag-Au nanocrystals: The UV, visible, and NIR plasmons

Energetic and optical properties of plasmonic nanocrystals strongly depend on their sizes, shapes, and composition. Whereas using plasmonic nanoparticles in biotesting has become routine, applications of plasmonics in energy are still early in development. Here, we investigate hot electron (HE) generation and related electromagnetic effects in both mono- and bi-metallic nanorods (NRs) and focus on one promising type of bi-metallic nanocrystals - core-shell Au-Ag nanorods. The spectra of the NRs are broadband, highly tunable with their geometry, and have few plasmon resonances. In this work, we provide a new quantum formalism describing the HE generation in bi-metallic nanostructures. Interestingly, we observe that the HE generation rate at the UV plasmon resonance of Au-Ag NRs appears to be very high. These HEs are highly energetic and suitable for carbon-fuel reactions. Simultaneously, the HE generation at the longitudinal plasmon (L-plasmon) peaks, which can be tuned from the yellow to near-IR, depends on the near-field and electromagnetic Mie effects, limiting the HE efficiencies for the long and large NRs. These properties of the L-plasmon relate to all kinds of NRs (Au, Ag, and Au-Ag). We also consider the generation of the interband d-holes in Au and Ag, since the involvement of the d-band is crucial for the energetic properties of UV plasmons. The proposed formalism is an important development for the description of bi-metallic (or tri-metallic, or more complex) nanostructures, and it paves the way to the efficient application of the plasmonic HEs and hot holes in sensing, nanotechnology, photocatalysis, and electrophotochemistry.

cond-mat.mes-hall↗

arXiv2023

Leveraging Large Language Model for Automatic Evolving of Industrial Data-Centric R&D Cycle

In the wake of relentless digital transformation, data-driven solutions are emerging as powerful tools to address multifarious industrial tasks such as forecasting, anomaly detection, planning, and even complex decision-making. Although data-centric R&D has been pivotal in harnessing these solutions, it often comes with significant costs in terms of human, computational, and time resources. This paper delves into the potential of large language models (LLMs) to expedite the evolution cycle of data-centric R&D. Assessing the foundational elements of data-centric R&D, including heterogeneous task-related data, multi-facet domain knowledge, and diverse computing-functional tools, we explore how well LLMs can understand domain-specific requirements, generate professional ideas, utilize domain-specific tools to conduct experiments, interpret results, and incorporate knowledge from past endeavors to tackle new challenges. We take quantitative investment research as a typical example of industrial data-centric R&D scenario and verified our proposed framework upon our full-stack open-sourced quantitative research platform Qlib and obtained promising results which shed light on our vision of automatic evolving of industrial data-centric R&D cycle.

cs.AI↗

arXiv2023

Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems

In this paper, we introduce an innovative approach for addressing Bayesian inverse problems through the utilization of physics-informed invertible neural networks (PI-INN). The PI-INN framework encompasses two sub-networks: an invertible neural network (INN) and a neural basis network (NB-Net). The primary role of the NB-Net lies in modeling the spatial basis functions characterizing the solution to the forward problem dictated by the underlying partial differential equation. Simultaneously, the INN is designed to partition the parameter vector linked to the input physical field into two distinct components: the expansion coefficients representing the forward problem solution and the Gaussian latent noise. If the forward mapping is precisely estimated, and the statistical independence between expansion coefficients and latent noise is well-maintained, the PI-INN offers a precise and efficient generative model for Bayesian inverse problems, yielding tractable posterior density estimates. As a particular physics-informed deep learning model, the primary training challenge for PI-INN centers on enforcing the independence constraint, which we tackle by introducing a novel independence loss based on estimated density. We support the efficacy and precision of the proposed PI-INN through a series of numerical experiments, including inverse kinematics, 1-dimensional and 2-dimensional diffusion equations, and seismic traveltime tomography. Specifically, our experimental results showcase the superior performance of the proposed independence loss in comparison to the commonly used but computationally demanding kernel-based maximum mean discrepancy loss.

math.NA↗

arXiv2023

A Generalized Density Dissipation for Weakly-compressible SPH

The weakly compressible Smoothed Particle Hydrodynamics (SPH) is known to suffer from the pressure oscillation, which would undermine the simulation stability and accuracy. To address this issue, we propose a generalized density dissipation scheme suitable for both single-phase and multiphase flow simulations. Our approach consists of two components. Firstly, we replace the basic density dissipation with the density increment dissipation to enable numerical dissipation crossing the interfaces of different fluids in multiphase flow. Secondly, based on the dissipation volume conservation, we utilize dissipation volume correction factor (VCF) to stabilize the simulations for multiphase flows with large density ratio. We demonstrate the accuracy, stability, and robustness of our method through four three-dimensional benchmarks, i.e., the sloshing under external excitations, the single and double bubbles rising, Rayleigh-Taylor instability, and Kelvin Helmholtz instability. Additionally, our study reveals the relationship between SPH with the density dissipation and the approximate Riemann solver.

physics.flu-dyn↗

arXiv2023

Linear TreeShap

Decision trees are well-known due to their ease of interpretability. To improve accuracy, we need to grow deep trees or ensembles of trees. These are hard to interpret, offsetting their original benefits. Shapley values have recently become a popular way to explain the predictions of tree-based machine learning models. It provides a linear weighting to features independent of the tree structure. The rise in popularity is mainly due to TreeShap, which solves a general exponential complexity problem in polynomial time. Following extensive adoption in the industry, more efficient algorithms are required. This paper presents a more efficient and straightforward algorithm: Linear TreeShap. Like TreeShap, Linear TreeShap is exact and requires the same amount of memory.

cs.LG↗

arXiv2022

Superconducting resonators with voltage-controlled frequency and nonlinearity

Voltage-tunable superconductor-semiconductor devices offer a unique platform to realize dynamic tunability in superconducting quantum circuits. By galvanically connecting a gated InAs-Al Josephson junction to a coplanar waveguide resonator, we demonstrate the use of a wide-range gate-tunable superconducting element. We show that the resonant frequency is controlled via a gate-tunable Josephson inductance and that the non-linearity of the voltage-controlled InAs-Al junction is non-dissipative as is the case with conventional Al-AlO$_{x}$ junctions. As the gate voltage is decreased, the inductive participation of the junction increases up to $44\%$, resulting in the resonant frequency being tuned by over 2 GHz. Utilizing the wide tunability of the device, we demonstrate that two resonant modes can be adjusted such that they strongly hybridize, exhibiting an avoided level crossing with a coupling strength of 51 MHz. Implementing such voltage-tunable resonators is the first step toward realizing wafer-scale continuous voltage control in superconducting circuits for qubit-qubit coupling, quantum-limited amplifiers, and quantum memory platforms.

quant-ph↗

arXiv2022

The study of contact properties in edge-contacted graphene-aluminum Josephson junctions

Transparent contact interfaces in superconductor-graphene hybrid systems are critical for realizing superconducting quantum applications. Here, we examine the effect of the edge-contact fabrication process on the transparency of the superconducting aluminum-graphene junction. We show significant improvement in the transparency of our superconductor-graphene junctions by promoting the chemical component of the edge contact etch process. Our results compare favorably with state-of-the-art graphene Josephson junctions. The findings of our study contribute to advancing the fabrication knowledge of edge-contacted superconductor-graphene junctions.

cond-mat.supr-con↗

arXiv2022

Magneto-anisotropic weak antilocalization in near-surface quantum wells

We investigate the effects of an in-plane magnetic field on the weak antilocalization signature of near-surface quantum wells lacking bulk and inversion symmetry. The measured magnetoconductivity exhibits a strong anisotropy with respect to the direction of the in-plane magnetic field. The two-fold symmetry of the observed magneto-anisotropy originates from the competition between Rashba and Dresselhaus spin-orbit couplings. The high sensitivity of the weak antilocalization to the spin texture produced by the combined Zeeman and spin-orbit fields results in very large anisotropy ratios, reaching 100%. Using a semiclassical universal model in quantitative agreement with the experimental data, we uniquely determine the values of the Dresselhaus and Rashba parameters as well as the effective in-plane g-factor of the electrons. Understanding these parameters provides new prospects for novel applications ranging from spintronics to topological quantum computing.

cond-mat.mes-hall↗

arXiv2022

Numerical Study of Viscoelastic Upstream Instability

In this work, we report numerical results on the flow instability and bifurcation of a viscoelastic fluid in the upstream region of a confined cylinder in a narrow channel. Two-dimensional direct numerical simulations based on the FENE-P model (the finite-extensible nonlinear elastic model with the Peterlin closure) are conducted with numerical stabilization techniques. Our results show that the macroscopic viscoelastic constitutive relation can capture the viscoelastic upstream instability reported in previous experiments for low-Reynolds-number flows. The numerical simulations reveal that the non-dimensional recirculation length ($L_D$) is affected by the cylinder blocking rate ($BR$), the Weissenberg number ($Wi$), the viscosity ratio ($β$), and the maximum polymer extension ($L$). Close to the onset of upstream recirculation, depending on the values of $L$ and $β$, the bifurcation can be supercritical or subcritical. $β$ has a nonlinear influence on the upstream recirculation length. This work contributes to our theoretical understanding of this new instability mechanism in viscoelastic wake flows.

physics.flu-dyn↗

arXiv2022

Nonthermal Atmospheric Plasma Reactors for Hydrogen Production from Low-Density Polyethylene

Hydrogen is largely produced via natural gas reforming or electrochemical water-splitting, leaving organic solid feedstocks under-utilized. Plasma technology powered by renewable electricity can lead to the sustainable upcycling of plastic waste and production of green hydrogen. In this work, low-temperature atmospheric pressure plasma reactors based on transferred arc (transarc) and gliding arc (glidarc) discharges are designed, built, and characterized to produce hydrogen from low-density polyethylene (LDPE) as a model plastic waste. Experimental results show that hydrogen production rate and efficiency increase monotonically with increasing voltage level in both reactors, with the maximum hydrogen production of 0.33 and 0.42 mmol/g LDPE for transarc and glidarc reactors, respectively. For the transarc reactor, smaller electrode-feedstock spacing favors greater hydrogen production, whereas, for the glidarc reactor, greater hydrogen production is obtained at intermediate flow rates. The hydrogen production from LDPE is comparable despite the markedly different modes of operation between the two reactors.

physics.plasm-ph↗

arXiv2022

A machine learning based method to generate random packed isotropic porous media with desired porosity and permeability

Porous materials are used in many fields, including energy industry, agriculture, medical industry, etc. The generation of digital porous media facilitates the fabrication of real porous media and the analysis of their properties. The past random digital porous media generation methods are unable to generate a porous medium with a specific permeability. A new method is proposed in the present study, which can generate the random packed isotropic porous media with specific porosity and permeability. Firstly, the process of generating the random packed isotropic porous media is detailed. Secondly, the permeability of the generated porous media is calculated with the multi-relaxation time (MRT) lattice Boltzmann method (LBM), which is prepared for the training of convolutional neural network (CNN). Thirdly, 3000 samples on the microstructure of porous media and their permeabilities are used to train the CNN model. The trained model is very effective in predicting the permeability of a porous medium. Finally, our method is elaborated and the choice of target permeability in this method is discussed. With the support of a powerful computer, a porous medium that satisfies the error condition of porosity and permeability can be generated in a short time.

physics.flu-dyn↗

arXiv2022

Comparison of three numerical stabilization techniques of viscoelastic flows: vortex shedding behind a confined cylinder

In this study, the OpenFOAM platform, based on the finite volume method, is applied to investigate the two-dimensional viscoelastic flow past a circular cylinder. The FENE-P model, which considers the bounded elongation of polymer molecules, is chosen to describe the elastic constitutive relationship of the polymer solution. The maximum molecular chain lengths of L = 10, 50, 100, and 200 are considered, which describe the molecular conformation characteristics of the polymer solution. To improve the numerical instability of the viscoelastic flow simulation, three different methods, i.e., the traditional method (Td) with the addition of artificial viscosity, the logarithmic reconstruction method (Log), and the square root tensor method (Sqrt), are evaluated. The results show that the artificial viscosity has a little effect on the accuracy for the simulation with a small molecular chain length (L = 10). However, for long molecular chain lengths such as L = 100 and L = 200, the addition of artificial dissipation tends to overestimate the drag, which indicates that special caution is needed to incorporate the artificial dissipation in the simulation. Moreover, the logarithmic reconstruction method shows a strong grid-dependent characteristics, which may produce unphysical results.

physics.flu-dyn↗

arXiv2022

Wake asymmetry weakening in viscoelastic fluids: Numerical discovery and mechanism exploration

Viscoelasticity weakens the asymmetry of laminar shedding flow behind a blunt body in a free domain. In the present study, this finding is confirmed by four unsteady viscoelastic flows with asymmetric flow configuration, i.e., flow over an inclined flat plate with various angles of incidence, flow over a rotating circular cylinder, flow over a circular cylinder with asymmetric slip boundary distribution, and flow over an inclined row of eight equally closely spaced circular cylinders (which can be considered as a single large blunt body) through direct numerical simulation combined with the Peterlin approximation of the finitely extensible nonlinear elastic (FENE-P) model. At high Weissenberg number, an arc shape region with high elastic stress, which is similar to shock wave, forms in the frontal area of the blunt body. This region acts as a stationary shield to separate the flow into different regions. Thus, the free stream resembles to pass this shield instead of the original blunt body. As this shield has symmetric feature, the wake flow restores symmetry.

physics.flu-dyn↗

arXiv2022

Wall-induced translation of a rotating particle in viscoelastic fluid

Shear-thinning and viscoelasticity are two non-Newtonian fluid properties widely existing in biological fluids. In this study, we found that the translation motion of a rotating particle near a wall speed up firstly, and then slows down with enhancement of fluid viscoelasticity, which is different from the behavior reported in shear thinning fluid (Chen et al. J. Fluid Mech. 2021, 927). Our research is carried out by numerical simulation of Navier-Stokes equations combined with Oldroyd-B constitutive model. This work is expected to be helpful to understand the movement of a rotating sphere near a wall in complex fluids comprehensively.

physics.flu-dyn↗