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| 1 | 机器学习在二维材料探索中的应用显示文摘当前,机器学习已经成为探索和拓展二维材料家族的重要研究手段。传统实验与计算方法在研究二维材料时容错率低,并且需要花费大量时间和研发成本。机器学习因为拥有强大的数据处理能力和灵活多样的算法模型,能够绕过求解理论计算复杂的泛函方程以及缓慢的实验过程,缩短研究周期帮助减少发现和理解二维材料的时间和成本,以数据为基础高效预测扩展二维材料体系并探究其实验合成以及应用的潜力。将围绕机器学习的方法、机器学习在二维材料设计与合成、机器学习在二维材料物性与应用的探索等方面,详细介绍相关的前沿进展,最后对机器学习在二维材料领域开展研究所面临的挑战与发展趋势进行了展望。 | 张胜利 胡扬 周文瀚 曾海波 | 2022 | 金属功能材料2022，29，4： | 3 |
| 2 | Rapid thin-layer WS_(2) detection based on monochromatic illumination photographs显示文摘The thickness of two-dimensional(2D)nanomaterials shows a significant effect on their optical and electrical properties.Therefore,a rapid and automatic detection technology of 2D nanomaterials with desired layer-number is required to extend their practical application in optoelectronic devices.In this paper,an image recognition technology was proposed for rapid and reliable identification of thin-layer WS_(2) samples,which combining a layer-thickness identification criterion and a novel image segmentation algorithm.The criterion stemmed from optical contrast study of monochromatic illumination photographs,and the algorithm was based on Canny operator and edge connection iteration.This optical identification method can seek out thin-layer WS_(2) samples on complex surfaces,which provides a promising approach for automatic search of thin-layer nanomaterials. | Xiangmin Hu Cuicui Qiu Dameng Liu | 2021 | Nano Research2021，14，3： | 2 |
| 3 | Emerging role of machine learning in light-matter interaction显示文摘Machine learning has provided a huge wave of innovation in multiple fields,including computer vision,medical diagnosis,life sciences,molecular design,and instrumental development.This perspective focuses on the implementation of machine learning in dealing with light-matter interaction,which governs those fields involving materials discovery,optical characterizations,and photonics technologies.We highlight the role of machine learning in accelerating technology development and boosting scientific innovation in the aforementioned aspects.We provide future directions for advanced computing techniques via multidisciplinary efforts that can help to transform optical materials into imaging probes,information carriers and photonics devices. | Jiajia Zhou Bolong Huang Zheng Yan Jean-Claude G.Bunzli | 2019 | Light(Science & Applications)2019，8，1： | 1 |
| 4 | 基于差分反射高光谱成像的薄层TMDC材料检测技术研究显示文摘二维过渡金属硫化物(TMDC)材料因为独特的激子效应和材料学性质,在太阳电池、光催化、传感器、柔性电子器件等领域得到广泛的应用。层数对其性质有显著的调控作用,自动检测识别所需层数的样品是其从实验室走进半导体制造工业的重要技术需求。本文结合反射高光谱成像技术与图像处理算法,发展了一种二维TMDC薄层样品的显微成像自动检测技术。基于自主搭建的反射高光谱成像系统,对制备的不同层数TMDC标准样品进行了光学对比度的系统研究,阐明了层数的差分反射光谱机理,提出了可靠的层数判定方法。基于传统边缘检测技术优化设计了一套图像处理算法,实现了TMDC样品的图像检测及层数鉴定。本文方法具有普遍性、实用性,结合自动对焦的扫描控制,能够实现大规模的自动化样品检测,这也为其他表面目标的显微识别和检测提供了新的灵感和参考。 | 胡香敏 刘大猛 | 2022 | 光散射学报2022，34，1： | 0 |
| 5 | Applications of Machine Learning in Electrochemistry显示文摘The introduction of density functional theory(DFT)and electronic structure has brought computational methods into the field of materials science.In these theoretical calculations,quantum mechanics is predominantly used.Machine learning(ML)and high-throughput computing share some inherent similarities,as both can extract valuable information from massive datasets and possess parallelism and scalability.ML techniques simulate human thought processes,with algorithms that make decisions and have good scalability and strong generalization abilities.The combination of high-throughput and ML technologies leverages the advantages of high-throughput technology standardization and high capacity,addressing the challenges faced by ML at the front end.This complementary combination is expected to further enhance the efficiency of material screening and development.In data mining,using ML methods on various databases,the interrelationships between molecular structures and properties are discovered from large amounts of data.Mapping,current utilization of DFT,materials genomics,and high-throughput computing have generated a substantial amount of data.This review provides new insights into the development of electrochemistry. | Xianlin Shi Guangxun Zhang Yibo Lu Huan Pang | 2023 | Renewables2023，1，6： | 0 |
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