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AtomGAN:unsupervised deep learning for fast and accurate defect detection of 2D materials at the atomic scale

Science China(Information Sciences) 2023年第6期66卷 102-114页

作者： Danpeng CHENG wuxin sha Zuo XU Shide LI Zhigao YIN Yuling LANG Shun TANG Yuan-Cheng CAO State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and Technology School of Computer Science and Technology Huazhong University of Science and Technology CITIC Dicastal Co. Ltd.

The extraction of atomic-level material features from electron microscope images is crucial for studying structure-property relationships and discovering new materials. However, traditional electron microscope analyses rely on time-consuming and complex human operations; thus, they are only applicable to images with a small number of atoms. In addition, the analysis results vary due to observers’ individual deviations. Although efforts to introduce automated methods have been performed previously, many of these methods lack sufficient labeled data or require various conditions in the detection process that can only be applied to the target material. Thus, in this study, we developed AtomGAN, which is a robust, unsupervised learning method, that segments defects in classical 2D material systems and the heterostructures of MoS2/WS2automatically. To solve the data scarcity problem, the proposed model is trained on unpaired simulated data that contain point and line defects for MoS2/WS2. The proposed AtomGAN was evaluated on both simulated and real electron microscope images. The results demonstrate that the segmented point defects and line defects are presented perfectly in the resulting figures, with a measurement precision of 96.9%. In addition, the cycled structure of AtomGAN can quickly generate a large number of simulated electron microscope images.

关键词： deep learning generative adversarial network defect detection atomic resolution 2D materials

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Degradation mechanism analysis of LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2) single crystal cathode materials through machine learning

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npj Computational Materials 2022年第1期8卷 2119-2127页

作者： wuxin sha Yaqing Guo Danpeng Cheng Qigao Han Ping Lou Minyuan Guan Shun Tang Xinfang Zhang Songfeng Lu Shijie Cheng Yuan-Cheng Cao School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan430074China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan430074China State Grid Huzhou Electric Power Supply Company Huzhou313000China School of Cyber Science&Engineering Huazhong University of Science and TechnologyWuhan430074China

LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2)(NCM523)has become one of the most popular cathode materials for current lithium-ion batteries due to its high-energy density and cost ***,the rapid capacity fading of NCM severely hinders its development and ***,the single crystal NCM523 materials under different degradation states are characterized using scanning transmission electron microscopy(STEM).Then we developed a neural network model with a two-sequential attention block to recognize the crystal structure and locate defects in STEM *** number of point defects in NCM523 is observed to experience a trend of increasing first and then decreasing in the degradation *** space between the transition metal columns shrinks obviously,inducing dramatic capacity *** analysis sheds light on the defect evolution and chemical transformation correlated with layered material *** also provides interesting hints for researchers to regenerate the electrochemical capacity and design better battery materials with longer life.

关键词： battery crystal analysis

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Machine learning in polymer informatics

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InfoMat 2021年第4期3卷 353-361页

作者： wuxin sha Yan Li Shun Tang Jie Tian Yuming Zhao Yaqing Guo Weixin Zhang Xinfang Zhang Songfeng Lu Yuan-Cheng Cao Shijie Cheng State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhanChina Shenzhen Power Supply Col.Ltd. ShenzhenChina School of Computer Science and Technology Huazhong University of Science and TechnologyWuhanChina

Polymers have been widely used in energy storage,construction,medicine,aerospace,and so ***,the complexity of chemical composition and morphology of polymers has brought challenges to their *** to the integration of machine leaming algorithms and large data resources,the data-driven methods have opened up a new road for the development of poly-mer science and *** emerging polymer informatics attempts to accelerate the performance prediction and process optimization of new poly-mers by using machine learning models based on reliable *** the grad-ual supplement of currently available databases,the emergence of new databases and the continuous improvement of machine learning algorithms,the research paradigm of polymer informatics will be more efficient and widely *** on these points,this paper reviews the development trends of machine learning assisted polymer informatics and provides a simple introduc-tion for researchers in materials,artificial intelligence,and other fields.

关键词： polymer learning artificial

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谈“媒体主义技术”与“教育技术”

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中国医学教育技术 2008年第5期22卷 406-408页

作者：石武信石莎西安电子科技大学网络教育学院陕西西安710071 中国电子科技集团公司第五十二研究所浙江杭州310012

分析了“媒体主义技术观”产生的根源及对我国教育产生的影响，指出教育技术的核心应是教学设计，即教学目标、教学策略、教学过程的研究和教学评价理论等。

关键词：媒体主义技术观问题分析教学策略学习过程研究

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论网络远程教育的品牌问题及创建战略

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中国医学教育技术 2009年第4期23卷 365-368页

作者：石武信石莎西安电子科技大学陕西西安710071 北京邮电大学网络技术研究院北京100876

论述了远程教育院校实施品牌战略的意义，品牌创建的策略、步骤、方法以及创建中应注意的问题。

关键词：远程教育品牌的意义创建战略

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不同营养水平对鄂尔多斯细毛羊体重及产毛性能的影响

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畜牧兽医科学（电子版） 2022年第12期 7-9页

作者：娜美日嘎德德玛伊波勒图奇汗邹俊王伟沙如拉斯登丹巴内蒙古鄂尔多斯市农牧业科学研究院内蒙古鄂尔多斯017200 内蒙古乌审旗家畜改良工作站内蒙古鄂尔多斯017300 内蒙古自治区纤维质量检测中心内蒙古呼和浩特010000

研究通过比较2组不同营养水平供给对鄂尔多斯细毛羊体重、产毛性能的影响,总结鄂尔多斯细毛羊冬春季节最佳的饲喂饲养管理方法,为鄂尔多斯细毛羊的生产提供科学依据。结果表明按营养需求饲喂(试验组)的后备母羊、育成母羊体重分别比传... 详细信息

研究通过比较2组不同营养水平供给对鄂尔多斯细毛羊体重、产毛性能的影响,总结鄂尔多斯细毛羊冬春季节最佳的饲喂饲养管理方法,为鄂尔多斯细毛羊的生产提供科学依据。结果表明按营养需求饲喂(试验组)的后备母羊、育成母羊体重分别比传统饲喂(对照组)的在冬春季节少下降3.2 kg和2.1 kg;试验组基础母羊、后备母羊、育成母羊均比对照组传统饲喂羊羊毛长度长,分别长0.67、0.98、0.82 cm,差异性显著(P<0.05),其产毛量、羊毛细度、羊毛密度均差异性显著(P<0.05)。由本研究结果可知,按营养需求饲喂可有效防止鄂尔多斯细毛羊体重大幅度减小,并可改善提高羊毛品质。

关键词：鄂尔多斯细毛羊营养需求体重产毛性能

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