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您的检索式：作者名="Zhefeng Xin"
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| 1 | Identify crystal structures by a new paradigm based on graph theory for building materials big data显示文摘Material identification technique is crucial to the development of structure chemistry and materials genome project. Current methods are promising candidates to identify structures effectively, but have limited ability to deal with all structures accurately and automatically in the big materials database because different material resources and various measurement errors lead to variation of bond length and bond angle. To address this issue, we propose a new paradigm based on graph theory(GTscheme) to improve the efficiency and accuracy of material identification, which focuses on processing the 'topological relationship' rather than the value of bond length and bond angle among different structures. By using this method, automatic deduplication for big materials database is achieved for the first time, which identifies 626,772 unique structures from 865,458 original structures.Moreover, the graph theory scheme has been modified to solve some advanced problems such as identifying highly distorted structures, distinguishing structures with strong similarity and classifying complex crystal structures in materials big data. | Mouyi Weng Zhi Wang Guoyu Qian Yaokun Ye Zhefeng Chen Xin Chen Shisheng Zheng Feng Pan | 2019 | Science China Chemistry2019，62，8： | 3 |
| 2 | Fatigue Life Prediction of Gray Cast Iron for Cylinder Head Based on Microstructure and Machine Learning显示文摘Conventional fatigue tests on complex components are difficult to sample,time-consuming and expensive.To avoid such problems,several popular machine learning(ML)algorithms were used and compared to predict fatigue life of gray cast iron(GCI)with the complex microstructures.The feature analysis shows that the fatigue life of GCI is mainly influenced by the external environment such as the stress amplitude,and the internal microstructure parameters such as the percentage of graphite,graphite length,stress concentration factor at the graphite tip,matrix microhardness and Brinell hardness.For simplicity,collected datasets with some of the above features were used to train ML models including back-propagation neural network(BPNN),random forest(RF)and eXtreme gradient boosting(XGBoost).The comparison results suggest that the three models could predict the fatigue lives of GCI,while the implemented RF algorithm is the best performing model.Moreover,the S–N curves fitted by the Basquin relation in the predicted data have a mean relative error of 15%compared to the measured data.The results have demonstrated the advantages of ML,which provides a generic way to predict the fatigue life of GCI for reducing time and cost. | Xiaoyuan Teng Jianchao Pang Feng Liu Chenglu Zou Xin Bai Shouxin Li Zhefeng Zhang | 2023 | Acta Metallurgica Sinica(English Letters)2023，36，9： | 0 |
| 3 | Seaweed Fiber Fabricated with Agar Alkali-Free Extracted from Gracilaria lemaneiformis显示文摘The sulfate groups in agar structure played a good role in the formation of fiber.However,commercially available agar is usually extracted from red algae by alkali treatment to decrease the content of sulfate group for the purpose of high gel strength.In this paper,an alkali-free method of agar extraction from Gracilaria lemaneiformis was proposed for the wet-spinning purpose.This method is environmentally friendly,reduces the extraction steps,saves energy,and reduces the production cost of agar fiber.The improved agar preparation process not only has higher agar yield,but also has higher molecular weight and sulfate group content,which is beneficial to the preparation and forming of fiber and makes the fiber have higher mechanical strength and elongation.Therefore,this extraction technology has broad application prospect in the textile field. | Yuzhi Wu Cunzhen Geng Chaochao Cui Zhefeng Xin Yanzhi Xia Zhixin Xue | 2023 | Journal of Renewable Materials2023，11，3： | 0 |
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