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Published in last 1 year |  In last 2 years |  In last 3 years |  All Please wait a minute... For selected: Download citations EndNote Ris BibTeX Toggle thumbnails Select SolarDesign: An online photovoltaic device simulation and design platform Wei E. I. Sha(沙威), Xiaoyu Wang(王啸宇), Wenchao Chen(陈文超), Yuhao Fu(付钰豪), Lijun Zhang(张立军), Liang Tian(田亮), Minshen Lin(林敏慎), Shudi Jiao(焦书迪), Ting Xu(徐婷), Tiange Sun(孙天歌), and Dongxue Liu(刘冬雪) Chin. Phys. B, 2025, 34 (1): 018801.   DOI: 10.1088/1674-1056/ad9017 Abstract （521）   HTML （3）    PDF （871KB）（297）       Knowledge map    SolarDesign (https://solardesign.cn/) is an online photovoltaic device simulation and design platform that provides engineering modeling analysis for crystalline silicon solar cells, as well as emerging high-efficiency solar cells such as organic, perovskite, and tandem cells. The platform offers user-updatable libraries of basic photovoltaic materials and devices, device-level multi-physics simulations involving optical-electrical-thermal interactions, and circuit-level compact model simulations based on detailed balance theory. Employing internationally advanced numerical methods, the platform accurately, rapidly, and efficiently solves optical absorption, electrical transport, and compact circuit models. It achieves multi-level photovoltaic simulation technology from "materials to devices to circuits" with fully independent intellectual property rights. Compared to commercial softwares, the platform achieves high accuracy and improves speed by more than an order of magnitude. Additionally, it can simulate unique electrical transport processes in emerging solar cells, such as quantum tunneling, exciton dissociation, and ion migration. Select Identifying important nodes of hypergraph: An improved PageRank algorithm Yu-Hao Piao(朴宇豪), Jun-Yi Wang(王俊义), and Ke-Zan Li(李科赞) Chin. Phys. B, 2025, 34 (4): 048902.   DOI: 10.1088/1674-1056/adb269 Abstract （363）   HTML （2）    PDF （871KB）（456）       Knowledge map    Hypergraphs can accurately capture complex higher-order relationships, but it is challenging to identify their important nodes. In this paper, an improved PageRank (ImPageRank) algorithm is designed to identify important nodes in a directed hypergraph. The algorithm introduces the Jaccard similarity of directed hypergraphs. By comparing the numbers of common neighbors between nodes with the total number of their neighbors, the Jaccard similarity measure takes into account the similarity between nodes that are not directly connected, and can reflect the potential correlation between nodes. An improved susceptible-infected (SI) model in directed hypergraph is proposed, which considers nonlinear propagation mode and more realistic propagation mechanism. In addition, some important node evaluation methods are transferred from undirected hypergraphs and applied to directed hypergraphs. Finally, the ImPageRank algorithm is used to evaluate the performance of the SI model, network robustness and monotonicity. Simulations of real networks demonstrate the excellent performance of the proposed algorithm and provide a powerful framework for identifying important nodes in directed hypergraphs. Select Model-free prediction of chaotic dynamics with parameter-aware reservoir computing Jianmin Guo(郭建敏), Yao Du(杜瑶), Haibo Luo(罗海波), Xuan Wang(王晅), Yizhen Yu(于一真), and Xingang Wang(王新刚) Chin. Phys. B, 2025, 34 (4): 040505.   DOI: 10.1088/1674-1056/adb733 Abstract （256）   HTML （3）    PDF （12791KB）（323）       Knowledge map    Model-free, data-driven prediction of chaotic motions is a long-standing challenge in nonlinear science. Stimulated by the recent progress in machine learning, considerable attention has been given to the inference of chaos by the technique of reservoir computing (RC). In particular, by incorporating a parameter-control channel into the standard RC, it is demonstrated that the machine is able to not only replicate the dynamics of the training states, but also infer new dynamics not included in the training set. The new machine-learning scheme, termed parameter-aware RC, opens up new avenues for data-based analysis of chaotic systems, and holds promise for predicting and controlling many real-world complex systems. Here, using typical chaotic systems as examples, we give a comprehensive introduction to this powerful machine-learning technique, including the algorithm, the implementation, the performance, and the open questions calling for further studies. Select A program for modeling the RF wave propagation of ICRF antennas utilizing the finite element method Lei-Yu Zhang(张雷宇), Yi-Xuan Li(李屹轩), Ming-Yue Han(韩明月), and Quan-Zhi Zhang(张权治) Chin. Phys. B, 2025, 34 (4): 045201.   DOI: 10.1088/1674-1056/adaccc Abstract （217）   HTML （2）    PDF （1151KB）（323）       Knowledge map    Controlled nuclear fusion represents a significant solution for future clean energy, with ion cyclotron range of frequency (ICRF) heating emerging as one of the most promising technologies for heating the fusion plasma. This study primarily presents a self-developed 2D ion cyclotron resonance antenna electromagnetic field solver (ICRAEMS) code implemented on the MATLAB platform, which solves the electric field wave equation by using the finite element method, establishing perfectly matched layer (PML) boundary conditions, and post-processing the electromagnetic field data. This code can be utilized to facilitate the design and optimization processes of antennas for ICRF heating technology. Furthermore, this study examines the electric field distribution and power spectrum associated with various antenna phases to investigate how different antenna configurations affect the electromagnetic field propagation and coupling characteristics. Select Progressive quantum algorithm for maximum independent set with quantum alternating operator ansatz Xiao-Hui Ni(倪晓慧), Ling-Xiao Li(李凌霄), Yan-Qi Song(宋燕琪), Zheng-Ping Jin(金正平), Su-Juan Qin(秦素娟), and Fei Gao(高飞) Chin. Phys. B, 2025, 34 (7): 070304.   DOI: 10.1088/1674-1056/addd83 Abstract （157）   HTML （0）    PDF （1456KB）（272）       Knowledge map    The quantum alternating operator ansatz algorithm (QAOA+) is widely used for constrained combinatorial optimization problems (CCOPs) due to its ability to construct feasible solution spaces. In this paper, we propose a progressive quantum algorithm (PQA) to reduce qubit requirements for QAOA+ in solving the maximum independent set (MIS) problem. PQA iteratively constructs a subgraph likely to include the MIS solution of the original graph and solves the problem on it to approximate the global solution. Specifically, PQA starts with a small-scale subgraph and progressively expands its graph size utilizing heuristic expansion strategies. After each expansion, PQA solves the MIS problem on the newly generated subgraph using QAOA+. In each run, PQA repeats the expansion and solving process until a predefined stopping condition is reached. Simulation results show that PQA achieves an approximation ratio of 0.95 using only $5.57%$ ($2.17%$) of the qubits and $17.59%$ ($6.43%$) of the runtime compared with directly solving the original problem with QAOA+ on Erdös-Rényi (3-regular) graphs, highlighting the efficiency and scalability of PQA. Select 3D-GTDSE: A GPU-based code for solving 3D-TDSE in Cartesian coordinates Ke Peng(彭科), Aihua Liu(刘爱华), Jun Wang(王俊), and Xi Zhao(赵曦) Chin. Phys. B, 2025, 34 (9): 094203.   DOI: 10.1088/1674-1056/adee00 Abstract （79）   HTML （0）    PDF （1332KB）（26）       Knowledge map    We present a graphics processing units (GPU) parallelization based three-dimensional time-dependent Schrödinger equation (3D-TDSE) code to simulate the interaction between single-active-electron atom/molecule and arbitrary types of laser pulses with either velocity gauge or length gauge in Cartesian coordinates. Split-operator method combined with fast Fourier transforms (FFT) is used to perform the time evolution. Sample applications in different scenarios, such as stationary state energies, photon ionization spectra, attosecond clocks, and high-order harmonic generation (HHG), are given for the hydrogen atom. Repeatable results can be obtained with the benchmark program PCTDSE, which is a 3D-TDSE Fortran solver parallelized using message passing interface (MPI) library. With the help of GPU acceleration and vectorization strategy, our code running on a single NVIDIA 3090 RTX GPU can achieve about 10 times faster computation speed than PCTDSE running on a 144 Intel Xeon CPU cores server with the same accuracy. In addition, 3D-GTDSE can also be modified slightly to simulate non-adiabatic dynamics involving the coupling of nuclear and electronic wave packets, as well as pure nuclear wave packet dynamics in the presence of strong laser fields within 3 dimensions. Additionally, we have also discussed the limitations and shortcomings of our code in utilizing GPU memory. The 3D-GTDSE code provides an alternative tool for studying the ultrafast nonlinear dynamics under strong laser fields. Select Accurate quantum critical points and nonlocal string order parameters in the spin tetrahedron chain Zhi-Yong Wu(吴志勇), Kai-Ming Zhang(张凯铭), and Li-Xiang Cen(岑理相) Chin. Phys. B, 2025, 34 (11): 117502.   DOI: 10.1088/1674-1056/ae0c7c Abstract （47）   HTML （0）    PDF （749KB）（18）       Knowledge map    The ground-state phase diagram and nonlocal order parameters of an infinite spin tetrahedron chain with inhomogeneous exchange couplings are investigated. It is shown that the phase boundaries of the three phases in the model can be determined precisely, in line with the precision of its ground-state energy. Numerical calculations using the regularized time-evolving block decimation (rTEBD) algorithm yield the locations of the two quantum critical points with an accuracy about $10$ digits. Moreover, we explain how to calculate the parity-associated string order for the output wave function obtained through the rTEBD procedure, which not only reveals the presence of long-range correlations but also identifies the symmetry-protected topological order within the intermediate phase of the model. Select VASPilot: MCP-facilitated multi-agent intelligence for autonomous VASP simulations Jiaxuan Liu(刘家轩), Tiannian Zhu(朱天念), Caiyuan Ye(叶财渊), Zhong Fang(方忠), Hongming Weng(翁红明), and Quansheng Wu(吴泉生) Chin. Phys. B, 2025, 34 (11): 117106.   DOI: 10.1088/1674-1056/ae0681 Abstract （37）   HTML （0）    PDF （1799KB）（6）       Knowledge map    Density-functional-theory (DFT) simulations with the Vienna Ab initio Simulation Package (VASP) are indispensable in computational materials science but often require extensive manual setup, monitoring, and postprocessing. Here, we introduce VASPilot, an open-source platform that fully automates VASP workflows via a multi-agent architecture built on the CrewAI framework and a standardized model context protocol (MCP). VASPilot's agent suite handles every stage of a VASP study from retrieving crystal structures and generating input files to submitting Slurm jobs, parsing error messages, and dynamically adjusting parameters for seamless restarts. A lightweight Quart-based web interface provides intuitive task submission, real-time progress tracking, and drill-down access to execution logs, structure visualizations, and plots. We validated VASPilot on both routine and advanced benchmarks: automated band-structure and density-of-states calculations (including on-the-fly symmetry corrections), plane-wave cutoff convergence tests, lattice-constant optimizations with various van der Waals corrections, and cross-material band-gap comparisons for transition-metal dichalcogenides. In all cases, VASPilot completed the missions reliably and without manual intervention. Moreover, its modular design allows easy extension to other DFT codes simply by deploying the appropriate MCP server. By offloading technical overhead, VASPilot enables researchers to focus on scientific discovery and accelerates high-throughput computational materials research. Select Improved physics-informed neural networks incorporating lattice Boltzmann method optimized by tanh robust weight initialization Chenghui Yang(杨程晖), Minglei Shan(单鸣雷), Mengyu Feng(冯梦宇), Ling Kuai(蒯玲), Yu Yang(杨雨), Cheng Yin(殷澄), and Qingbang Han(韩庆邦) Chin. Phys. B, 2025, 34 (11): 110701.   DOI: 10.1088/1674-1056/adfc43 Abstract （37）   HTML （0）    PDF （2549KB）（10）       Knowledge map    Physics-informed neural networks (PINNs) have shown considerable promise for performing numerical simulations in fluid mechanics. They provide mesh-free, end-to-end approaches by embedding physical laws into their loss functions. However, when addressing complex flow problems, PINNs still face some challenges such as activation saturation and vanishing gradients in deep network training, leading to slow convergence and insufficient prediction accuracy. We present physics-informed neural networks incorporating lattice Boltzmann method optimized by tanh robust weight initialization (T-PINN-LBM) to address these challenges. This approach fuses the mesoscopic lattice Boltzmann model with the automatic differentiation framework of PINNs. It also implements a tanh robust weight initialization method derived from fixed point analysis. This model effectively mitigates activation and gradient decay in deep networks, improving convergence speed and data efficiency in multiscale flow simulations. We validate the effectiveness of the model on the classical arithmetic example of lid-driven cavity flow. Compared to the traditional Xavier initialized PINN and PINN-LBM, T-PINN-LBM reduces the mean absolute error (MAE) by one order of magnitude at the same network depth and maintains stable convergence in deeper networks. The results demonstrate that this model can accurately capture complex flow structures without prior data, providing a new feasible pathway for data-free driven fluid simulation.