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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 Energy conversion materials for the space solar power station Xiao-Na Ren(任晓娜), Chang-Chun Ge(葛昌纯), Zhi-Pei Chen(陈志培), Irfan(伊凡), Yongguang Tu(涂用广), Ying-Chun Zhang(张迎春), Li Wang(王立), Zi-Li Liu(刘自立), and Yi-Qiu Guan(关怡秋) Chin. Phys. B, 2023, 32 (7): 078802.   DOI: 10.1088/1674-1056/acbdee Abstract （143）   HTML （2）    PDF （3747KB）（115）       Knowledge map    Since it was first proposed, the space solar power station (SSPS) has attracted great attention all over the world; it is a huge space system and provides energy for Earth. Although several schemes and abundant studies on the SSPS have been proposed and conducted, it is still not realized. The reason why SSPS is still an idea is not only because it is a giant and complex project, but also due to the requirement for various excellent space materials. Among the diverse required materials, we believe energy materials are the most important. Herein, we review the space energy conversion materials for the SSPS. Select Research progress in quantum key distribution Chun-Xue Zhang(张春雪), Dan Wu(吴丹), Peng-Wei Cui(崔鹏伟), Jun-Chi Ma(马俊驰),Yue Wang(王玥), and Jun-Ming An(安俊明) Chin. Phys. B, 2023, 32 (12): 124207.   DOI: 10.1088/1674-1056/acfd16 Abstract （112）   HTML （2）    PDF （601KB）（156）       Knowledge map    Quantum key distribution (QKD) is a sophisticated method for securing information by leveraging the principles of quantum mechanics. Its objective is to establish a confidential key between authorized partners who are connected via both a quantum channel and a classical authentication channel. This paper presents a comprehensive overview of QKD protocols, chip-based QKD systems, quantum light sources, quantum detectors, fiber-based QKD networks, space-based QKD systems, as well as the applications and prospects of QKD technology. Select Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives Cheng-Zhou Zhang(张城洲) and Xiao-Qian Fu(付小倩) Chin. Phys. B, 2023, 32 (12): 126103.   DOI: 10.1088/1674-1056/ad01a4 Abstract （110）   HTML （0）    PDF （3403KB）（65）       Knowledge map    Optoelectronic materials are essential for today's scientific and technological development, and machine learning provides new ideas and tools for their research. In this paper, we first summarize the development history of optoelectronic materials and how materials informatics drives the innovation and progress of optoelectronic materials and devices. Then, we introduce the development of machine learning and its general process in optoelectronic materials and describe the specific implementation methods. We focus on the cases of machine learning in several application scenarios of optoelectronic materials and devices, including the methods related to crystal structure, properties (defects, electronic structure) research, materials and devices optimization, material characterization, and process optimization. In summarizing the algorithms and feature representations used in different studies, it is noted that prior knowledge can improve optoelectronic materials design, research, and decision-making processes. Finally, the prospect of machine learning applications in optoelectronic materials is discussed, along with current challenges and future directions. This paper comprehensively describes the application value of machine learning in optoelectronic materials research and aims to provide reference and guidance for the continuous development of this field. Select Multifunctional light-field modulation based on hybrid nonlinear metasurfaces Shuhang Qian(钱树航), Kai Wang(王凯), Jiaxing Yang(杨加兴), Chao Guan(关超), Hua Long(龙华), and Peixiang Lu(陆培祥) Chin. Phys. B, 2023, 32 (10): 107803.   DOI: 10.1088/1674-1056/acdc13 Abstract （94）   HTML （0）    PDF （3015KB）（176）       Knowledge map    The generation characteristics of nonlinear optical signals and their multi-dimensional modulation at micro-nano scale have become a prominent research area in nanophotonics, and also the key to developing various novel nonlinear photonics devices. In recent years, the demand for higher nonlinear conversion efficiency and device integration has led to the rapid progress of hybrid nonlinear metasurfaces composed of nanostructures and nonlinear materials. As a joint platform of stable wavefront modulation, nonlinear metasurface and efficient frequency conversion, hybrid nonlinear metasurfaces offer a splendid opportunity for developing the next-generation of multipurpose flat-optics devices. This article provides a comprehensive review of recent advances in hybrid nonlinear metasurfaces for light-field modulation. The advantages of hybrid systems are discussed from the perspectives of multifunctional light-field modulation, valleytronic modulation, and quantum technologies. Finally, the remaining challenges of hybrid metasurfaces are summarized and future developments are also prospected. Select Recent advances in protein conformation sampling by combining machine learning with molecular simulation Yiming Tang(唐一鸣), Zhongyuan Yang(杨中元), Yifei Yao(姚逸飞), Yun Zhou(周运), Yuan Tan(谈圆),Zichao Wang(王子超), Tong Pan(潘瞳), Rui Xiong(熊瑞), Junli Sun(孙俊力), and Guanghong Wei(韦广红) Chin. Phys. B, 2024, 33 (3): 030701.   DOI: 10.1088/1674-1056/ad1a92 Abstract （50）   HTML （5）    PDF （3732KB）（50）       Knowledge map    The rapid advancement and broad application of machine learning (ML) have driven a groundbreaking revolution in computational biology. One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules. This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape. We first highlight the recent development of ML-aided enhanced sampling methods, including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential, or facilitate the exploration of the unsampled region of the energy landscape. Further, we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations. Lastly, we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights. Collectively, this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.