arXiv subjects

Bing Dong

Publications and source records attributed to Bing Dong.

At least 19 recordsLinked to original sources

arXiv2026

Singlet-doublet transitions and Josephson currents in a superconducting ring with a quantum dot

We investigate the ground state properties of a superconducting ring embedded with a quantum dot (QD) by using a variational wave-function approach. A theoretical formulation for the treatment of the finite-U Anderson impurity coupled with a superconducting ring are presented. We demonstrate singlet-doublet transitions of the ground state for this system with the QD in the mixed valence regime. It is shown that the supercurrent in the superconductor ring shows oscillations with the external enclosed magnetic flux and exhibits abrupt jumps at the singlet-doublet phase transition points.

cond-mat.mes-hall↗

arXiv2026

Exceptional Points in a Parallel Double-Quantum-Dot Josephson Junction Coupled to a Ferromagnetic Reservoir

We investigate exceptional points (EPs) in a parallel double-quantum-dot Josephson junction coupled to a dissipative reservoir. By integrating out the leads, we obtain a non-Hermitian Bogoliubov-de Gennes description wherein the superconducting phase difference and orbital flux govern the complex Andreev spectrum. For spin-independent dissipation, the second-order EPs identified within the infinite superconducting gap limit are eliminated when the finite superconducting gap is properly incorporated. In contrast, spin-dependent dissipation originating from a ferromagnetic reservoir, in conjunction with magnetic flux, gives rise to second-order EPs that persist in superconducting leads with finite gap. Moreover, flux tuning enables the coalescence of two second-order EPs into a third-order EP, whose eigenvalue splitting exhibits cubic-root scaling behavior. A many-body parity analysis establishes the connection between the contrasting finite-gap behavior and the spectral relationship between the even- and odd-parity sectors. Finally, Josephson currents calculated from both the free-energy derivative and the surrogate-model density matrix demonstrate consistency and remain continuous across the EPs. These findings establish spin-selective dissipation and interferometric flux as effective control parameters for robust non-Hermitian singularities in superconducting nanostructures.

cond-mat.mes-hall↗

arXiv2026

Buildrix: An Open Platform for Sharing and Benchmarking Agentic AI Skills in Building Engineering

Agentic AI offers significant potential to automate complex building-engineering workflows. However, most existing applications remain isolated proof-of-concept demonstrations and lack reusable domain capabilities, human-verified evaluation cases, and standardized benchmarking infrastructure. This study presents Buildrix, an open, community-driven platform for developing, sharing, executing, and evaluating agentic AI skills for building engineering. Buildrix integrates three components: a Python command-line package for developing, validating, publishing, installing, and managing skills and test cases; a web-based Hub for organizing open challenges, reusable skills, test cases, reviews, and benchmark results; and a local agent harness that supports skill discovery, external toolchain provisioning, progressive context loading, and multi-step workflow execution. Buildrix skills are organized as standardized, self-contained packages containing task instructions, executable scripts, dependencies, and supporting resources. Quantitative test cases can be verified by domain experts and promoted to golden test cases for reproducible benchmark evaluation. Buildrix provides an open foundation for reusable capability development, transparent evaluation, and community-driven advancement of agentic AI in building engineering.

eess.SY↗

arXiv2026

The Rise of Quantum Computing -- Take a BITE for Built Environment and Urban Microclimate Research

Quantum computing is a new approach to computation that utilizes superposition, entanglement, interference, and tunneling to solve problems too complex for classical computers. This paper discusses the basic concepts and development of quantum computing, exploring its potential applications in the built environment and urban microclimate research. In buildings, quantum computing may help optimize energy management, control HVAC systems, and plan electric vehicle charging networks more efficiently. For urban microclimates, it could accelerate renewable energy planning and support multi-objective design, making it easier to balance urban building performance with climate conditions. Since current quantum hardware is still in the Noisy Intermediate-Scale Quantum (NISQ) stage, we propose the "BITE" principle to guide researchers in choosing suitable problems for quantum acceleration: B (Big search), I (Input-light), T (Tiny computation), and E (Evaluation polish). Although quantum computing still faces challenges such as noise and hardware limits, it offers great potential for developing more climate-resilient, sustainable, and energy-efficient cities of the future.

physics.pop-ph↗

arXiv2026

Ultrasensitive Terahertz Metasurface Biosensor Based on Quasi-Bound States in the Continuum

The terahertz (THz) spectral regime offers unique opportunities for next-generation biochemical sensing due to its non-destructive, label-free probing capability and strong sensitivity to molecular vibrations. However, conventional THz biosensors remain hampered by intrinsically low-quality factors and limited sensitivity, severely restricting their utility for trace-level biochemical and chemical detection. Here, we report an ultrasensitive THz metasurface biosensor that harnesses quasi-bound states in the continuum (QBICs) with sharp resonances and enhanced light-matter interactions to overcome these limitations. As a proof of concept, the device achieves label-free detection of a sulfur-containing amino acid cysteine, with an ultrahigh sensitivity of 492 GHz/RIU and an ultralow detection limit down to 0.00025 mg/mL. The synergy between QBIC-induced field confinement and meticulous structural optimization of the metasurface underpins this performance, marking a significant advance over conventional THz metasurface biosensing schemes. These results establish QBIC-based metasurfaces as a promising platform for ultrasensitive and high-precision biochemical and chemical sensing, with broad implications for medical diagnostics, food safety, and environmental monitoring.

physics.optics↗

arXiv2026

OptAgent: an Agentic AI framework for Intelligent Building Operations

The urgent need for building decarbonization calls for a paradigm shift in future autonomous building energy operation, from human-intensive engineering workflows toward intelligent agents that interact with physics-grounded digital environments. This study proposes an end-to-end agentic AI-enabled Physics-Informed Machine Learning (PIML) environment for scalable building energy modeling, simulation, control, and automation. The framework consists of (1) a modular and physics-consistent PIML digital environment spanning building thermal dynamics, Heating, Ventilation, and Air Conditioning (HVAC), and distributed energy resources (DER) for grid-interactive energy management; and (2) an agentic AI layer with 11 specialist agents and 72 Model Context Protocol (MCP) tools that enable end-to-end execution of multi-step energy analytics. A representative case study demonstrates multi-domain, multi-agent coordination for assessing how system and control upgrades affect energy use, operating cost, thermal comfort, and flexibility. In addition, a large-scale benchmark (about 4000 runs) systematically evaluates workflow performance in terms of accuracy, token consumption, execution time, and inference cost. The results quantify the impacts of intelligence mode design, model size, task complexity, and orchestrator-specialist coordination, and provide key lessons for building future agentic AI systems in real-world building energy applications. This work establishes a scalable, physics-grounded foundation for deploying agentic AI in decarbonized and grid-interactive building operations.

eess.SY↗

arXiv2026

BESTOpt: A Modular, Physics-Informed Machine Learning based Building Modeling, Control and Optimization Framework

Modern buildings are increasingly interconnected with occupancy, heating, ventilation, and air-conditioning (HVAC) systems, distributed energy resources (DERs), and power grids. Modeling, control, and optimization of such multi-domain systems play a critical role in achieving building-sector decarbonization. However, most existing tools lack scalability and physical consistency for addressing these complex, multi-scale ecosystem problems. To bridge this gap, this study presents BESTOpt, a modular, physics-informed machine learning (PIML) framework that unifies building applications, including benchmarking, evaluation, diagnostics, control, optimization, and performance simulation. The framework adopts a cluster-domain-system/building-component hierarchy and a standardized state-action-disturbance-observation data typology. By embedding physics priors into data-driven modules, BESTOpt improves model accuracy and physical consistency under unseen conditions. Case studies on single-building and cluster scenarios demonstrate its capability for multi-level centralized and decentralized control. Looking ahead, BESTOpt lays the foundation for an open, extensible platform that accelerates interdisciplinary research toward smart, resilient, and decarbonized building ecosystems.

eess.SY↗

arXiv2025

Magnetic flux controlled current phase relationship in double Quantum Dot Josephson junction

In this work, we study a Josephson junction with parallel-connected quantum dots (QDs) threaded by a magnetic flux in the central region. We discretize the superconducting (SC) electrode into three discrete energy levels and modify the tunneling coefficients to construct a finite-dimensional surrogate Hamiltonian. By directly diagonalizing this Hamiltonian, we compute the physical quantities of the system. Additionally, we employ a low-energy effective model to gain deeper physical insight. Our findings reveal that when only one QD exhibits Coulomb interaction, the system undergoes a phase transition between singlet and doublet states. The magnetic flux has a minor influence on the singlet state but significantly affects the doublet state. When both QDs have interactions, the system undergoes two phase transitions as the SC phase difference increases: the ground state evolves from a doublet to a singlet and finally into a triplet state at $\phi = \pi$. Increasing the magnetic flux suppresses the doublet and triplet phases, eventually stabilizing the singlet state. In this regime, enhancing the interaction strength does not induce a singlet-doublet transition but instead drives a transition between upper and lower singlet states, leading to a critical current peak as $U$ increases. Finally, we examine the case where the tunneling coefficient $\Gamma$ exceeds the SC pairing potential $\Delta$. Here, doublet states dominate, and the system only exhibits a phase transition between doublet and triplet states when $\phi_B = 0$. In the presence of a magnetic flux, the three states converge, resulting in a triple point in the ($\phi$, $\phi_B$) parameter space.

cond-mat.mes-hall↗

arXiv2025

Quantum phase transition in a double quantum dot Josephson junction driven by electron-electron interactions

In this work, we employ a surrogate BCS model with discrete energy levels to investigate a hybrid system comprising two quantum dots (QD1 and QD2), where QD1 is tunnel-coupled to two superconducting leads. Through exact diagonalization of this system, we obtain numerically exact solutions that enable rigorous computation of key physical quantities. Our analysis reveals a rich phase diagram featuring multiple controllable phase transitions mediated by quantum dot interactions. Specifically, the system first undergoes an initial phase transition when tuning QD2's interaction strength while maintaining QD1 in the non-interacting regime. Subsequent adjustment of QD1's interaction induces a secondary phase transition, followed by a third transition arising from inter-dot coupling modulation. Furthermore, we demonstrate that parallel magnetic field application can drive reversible ferromagnetic-antiferromagnetic phase transitions under specific parameter conditions. Finally, we report the emergence of non-local magnetization phenomena when subjecting QD1 to weak magnetic fields. And our results demonstrate that the orientation of nonlocal magnetization can be precisely manipulated through systematic adjustment of the on-site interaction strength $U_2$ in QD2.

cond-mat.mes-hall↗

arXiv2025

Physics-informed Modularized Neural Network for Advanced Building Control by Deep Reinforcement Learning

Physics-informed machine learning (PIML) provides a promising solution for building energy modeling and can serve as a virtual environment to enable reinforcement learning (RL) agents to interact and learn. However, challenges remain in efficiently integrating physics priors, evaluating the effectiveness of physics constraints, balancing model accuracy and physics consistency, and enabling real-world implementation. To address these gaps, this study introduces a Physics-Informed Modularized Neural Network (PI-ModNN), which incorporates physics priors through a physics-informed model structure, loss functions, and hard constraints. A new evaluation metric called "temperature response violation" is developed to quantify the physical consistency of data-driven building dynamic models under varying control inputs and training data sizes. Additionally, a physics prior evaluation framework based on rule importance is proposed to assess the contribution of each individual physics prior, offering guidance on selecting appropriate PIML techniques. Results indicate that incorporating physical priors does not always improve model performance; inappropriate priors may decrease model accuracy and consistency. However, hard constraints are effective in enforcing model consistency. Furthermore, we present a general workflow for developing control-oriented PIML models and integrating them with deep reinforcement learning (DRL). Following this framework, a case study implementing DRL in an office space over three months demonstrates potential energy savings of 31.4%. Finally, we provide a general guideline for integrating data-driven models with advanced building control through a four-step evaluation framework, paving the way for reliable and scalable deployment of advanced building controls.

eess.SY↗

arXiv2025

Physics-informed machine learning for building performance simulation-A review of a nascent field

Building performance simulation (BPS) is critical for understanding building dynamics and behavior, analyzing performance of the built environment, optimizing energy efficiency, improving demand flexibility, and enhancing building resilience. However, conducting BPS is not trivial. Traditional BPS relies on an accurate building energy model, mostly physics-based, which depends heavily on detailed building information, expert knowledge, and case-by-case model calibrations, thereby significantly limiting their scalability. With the development of sensing technology and increased data availability, there is a growing attention and interest in data-driven BPS. However, purely data-driven models often suffer from limited generalization ability and a lack of physical consistency, resulting in poor performance in real-world applications. To address these limitations, recent studies have started to incorporate physics priors into data-driven models, a methodology called physics-informed machine learning (PIML). PIML is an emerging field with the definitions, methodologies, evaluation criteria, application scenarios, and future directions that remain open. To bridge those gaps, this study systematically reviews the state-of-art PIML for BPS, offering a comprehensive definition of PIML, and comparing it to traditional BPS approaches regarding data requirements, modeling effort, performance and computation cost. We also summarize the commonly used methodologies, validation approaches, application domains, available data sources, open-source packages and testbeds. In addition, this study provides a general guideline for selecting appropriate PIML models based on BPS applications. Finally, this study identifies key challenges and outlines future research directions, providing a solid foundation and valuable insights to advance R&D of PIML in BPS.

eess.SY↗

arXiv2025

Quantum Resistor-Capacitor Circuit with two Majorana Bound States

In this paper, we derive the equations of motion for a system composed of a spinless quantum dot coupled to two normal leads and two Majorana bound states (MBSs), utilizing the auxiliary-mode expansion method and the nonequilibrium Green function technique. Subsequently, we use these equations to analyze the linear conductance, adiabatic linear capacitance, and adiabatic linear relaxation resistance of the system. We find that when the phase difference between the two MBSs is an integer multiple of $\pi$, the MBSs enter the zero mode, leading to the complete suppression of the linear relaxation resistance. In this case, the relaxation resistance is highly sensitive to the MBSs modes. On the other hand, when the phase difference is not an integer multiple of $\pi$, the linear conductance is fully suppressed. Furthermore, the linear relaxation resistance remains completely suppressed, even when the MBSs are not in the zero mode, and the system loses its sensitivity to the MBSs modes.

cond-mat.mes-hall↗

arXiv2025

PoVF: Empowering Decentralized Blockchain Systems with Verifiable Function Consensus

Consensus mechanism is the core technology for blockchain to ensure that transactions are executed in sequence. It also determines the decentralization, security, and efficiency of blockchain. Existing mechanisms all have certain centralization issues and fail to ensure the decentralization of blockchain networks. A decentralized and efficient mechanism is required to improve blockchain systems. This paper proposes a fair consensus mechanism called Proof of Verifiable Functions (PoVF), based on the verifiability and unpredictability of verifiable functions. PoVF provides a sufficiently fair mechanism, ensuring that all nodes in blockchain network have equal opportunity to participate in consensus. In addition, a structure called "Delay buffer" is proposed to ensure transactions are executed sequentially. It delay the selection of blocks to avoid blockchain forks caused by broadcasting and transaction execution confusion. According to our security analysis, PoVF is provably secure and has the ability to resist potential adversaries. According to the experiments, PoVF-based blockchain can process up to 4000 transactions per second with nodes configured with only 4-core CPUs. This paper uses the Gini coefficient to measure the decentralization of blockchains, and the PoVF-based blockchain achieves the lowest Gini coefficient of 0.39 among all sampled blockchains. PoVF has been shown to provide sufficient efficiency while ensuring decentralization and security through experiments.

cs.CR↗

arXiv2024

A class of core inverses associated with Green's relations in semigroups

Let $S$ be a $*$-monoid and let $a,b,c$ be elements of $S$. We say that $a$ is $(b,c)$-core-EP invertible if there exist some $x$ in $S$ and some nonnegative integer $k$ such that $cax(ca)^{k}c=(ca)^{k}c$, $x{\mathcal R}(ca)^{k}b$ and $x{\mathcal L}((ca)^{k}c)^{*}$. This terminology can be seen as an extension of the $w$-core-EP inverse and the $(b,c)$-core inverse. It is explored when $(b,c)$-core-EP invertibility implies $w$-core-EP invertibility. Another accomplishment of our work is to establish the criteria for the $(b,c)$-core-EP inverse of $a$ and to clarify the relations between the $(b,c)$-inverse, the core inverse, the core-EP inverse, the $w$-core inverse, the $(b,c)$-core inverse and the $(b,c)$-core-EP inverse. As an application, we improve a result in the literature focused on $(b,c)$-core inverses. We then establish the criterion for the $(B,C)$-core-EP inverse of $A$ in complex matrices, and give the solution to the system of matrix equations.

math.RA↗

arXiv2024

Modularized Neural Network Incorporating Physical Priors for Smart Building Control, Accuracy or Consistency?

Model predictive control can achieve significant energy savings, offer grid flexibility, and mitigate carbon emissions. However, the challenge of identifying individual control-oriented building dynamic models limits large-scale real-world applications. To address this issue, this study proposed a Modularized Neural Network Incorporating Physical Priors (ModNN), capable of establishing a control-oriented and physical-consistent building dynamic model within minutes without substantial modeling effort. This is also the first study to evaluate the physical consistency of a given data-driven model both qualitatively and quantitively. We compared the physical consistency of a classical Long Short-Term Memory (LSTM) model and our ModNN. The ModNN strictly satisfies physical constraints, whereas the LSTM model learned contradictory system dynamics. Additionally, we compared their control performance on an EnergyPlus virtual testbed. While the LSTM model demonstrated slightly better prediction accuracy in dynamic modeling, it failed in control optimization, resulting in an 89{\deg}C-h temperature violation, whereas the ModNN showed only a 0.57{\deg}C-h violation and achieved up to a 78% peak load reduction. Our findings highlight the importance of incorporating physics priors into data-driven models and provide a promising solution for future smart building control optimization. Furthermore, the proposed evaluation framework defines two physical consistency indicators, providing guidelines for selecting and testing control-oriented, data-driven building dynamic models.

eess.SY↗

arXiv2024

Crater-shaped Enrichment of $\mathrm{V}_\mathrm{Si}$ Color Centers in $4H$-SiC using Single-Pulse Near-Infrared Femtosecond Laser Processing

Currently, Si vacancy ($\mathrm{V}_\mathrm{Si}$) color centers in SiC are of significant interest due to their potential applications in quantum sensing and quantum communication. Meanwhile, the qualities of laser-induced color centers are well guaranteed. Femtosecond laser processing suffices for increasing the yield of $\mathrm{V}_\mathrm{Si}$ color centers in bulk materials and forms crater-shaped enriched regions on the surface. However, there is a notable absence of existing simulation methods to explain the mechanisms behind laser-assisted $\mathrm{V}_\mathrm{Si}$ color center generation. In this work, we design a three-dimensional molecular dynamics (3D-MD) model using an integral hemi-ellipsoidal shell mathematical model to simulate the interaction of Gaussian laser beams with bulk materials. Furthermore, we calculate the transmittance, absorption coefficient, refractive index, and reflectivity of $4H$-SiC. Then, the absorptance of a 1030 nm laser in 350 {\mu}m-thick $4H$-SiC material is abtained to simulate the energy loss during the actual processing. Finally, the study analyzes the movement trajectories of $\mathrm{V}_\mathrm{Si}$ color centers and explains the source of $\mathrm{V}_\mathrm{Si}$ on the surface. This analysis explains the reasons for the enrichment of color centers in the crater-shaped regions formed after laser deposition. Our work provides an effective 3D-MD modeling approach to study the processing mechanisms of laser interaction with semiconductor materials, offering insights into efficient $\mathrm{V}_\mathrm{Si}$ color center creation processes.

physics.optics↗

arXiv2019

First principles calculations on theoretical band gap improvement of IIIA-VA zinc-blende semiconductor InAs

The structural, electronic, dielectric and vibrational properties of zinc-blende (ZB) InAs were studied within the framework of density functional theory (DFT) by employing local density approximation and norm-conserving pseudopotentials. The optimal lattice parameter, direct band gap, static dielectric constant, phonon frequencies and Born effective charges calculated by treating In-4d electrons as valence states are in satisfactory agreement with other reported theoretical and experimental findings. The calculated band gap is reasonably accurate and improved in comparison to other findings.

physics.comp-ph↗

arXiv2019

Application of k . p method on band structure of GaAs obtained through joint density functional theory

The structural and electronic properties of zinc-blende (ZB) GaAs were calculated within the framework of plane wave density-functional theory (DFT) code JDFTx by using Becke 86 in 2D and PBE exchange correlation functionals from libXC. The standard optimized norm-conserving Vanderbilt pseudopotentials were used to calculate optimized lattice constant, band gap and spin-orbit split-off parameter. The calculated values of optimized lattice constant and direct band gap are in satisfactory agreement with other published theoretical and experimental findings. By including spin-orbit (SO) coupling, conduction band and valence bands were studied under parabolicity to calculate effective masses. The calculated values of effective masses and spin-orbit split-off parameter are in satisfactory agreement with most recent findings. This work will be useful for more computational studies related to semiconductor spintronic devices.

physics.comp-ph↗