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您的检索式：作者名="Guangfei Pan"
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| 1 | Atomic-scale simulations in multi-component alloys and compounds:A review on advances in interatomic potential显示文摘Multi-component alloys have demonstrated excellent performance in various applications,but the vast range of possible compositions and microstructures makes it challenging to identify optimized alloys for specific purposes.To overcome this challenge,large-scale atomic simulation techniques have been widely used for the design and optimization of multi-component alloys.The capability and reliability of large-scale atomic simulations essentially rely on the quality of interatomic potentials that describe the interactions between atoms.This work provides a comprehensive summary of the latest advances in atomic simulation techniques for multi-component alloys.The focus is on interatomic potentials,including both conventional empirical potentials and newly developed machine learning potentials(MLPs).The fitting processes for different types of interatomic potentials applied to multi-component alloys are also discussed.Finally,the challenges and future perspectives in developing MLPs are thoroughly addressed.Overall,this review provides a valuable resource for researchers interested in developing optimized multicomponent alloys using atomic simulation techniques. | Feiyang Wang Hong-Hui Wu Linshuo Dong Guangfei Pan Xiaoye Zhou Shuize Wang Ruiqiang Guo Guilin Wu Junheng Gao Fu-Zhi Dai Xinping Mao | 2023 | Journal of Materials Science & Technology2023，，34： | 0 |
| 2 | Advances in machine learning-and artificial intelligence-assisted material design of steels显示文摘With the rapid development of artificial intelligence technology and increasing material data,machine learning-and artificial intelligence-assisted design of high-performance steel materials is becoming a mainstream paradigm in materials science.Machine learning methods,based on an interdisciplinary discipline between computer science,statistics and material science,are good at discovering correlations between numerous data points.Compared with the traditional physical modeling method in material science,the main advantage of machine learning is that it overcomes the complex physical mechanisms of the material itself and provides a new perspective for the research and development of novel materials.This review starts with data preprocessing and the introduction of different machine learning models,including algorithm selection and model evaluation.Then,some successful cases of applying machine learning methods in the field of steel research are reviewed based on the main theme of optimizing composition,structure,processing,and performance.The application of machine learning methods to the performance-oriented inverse design of material composition and detection of steel defects is also reviewed.Finally,the applicability and limitations of machine learning in the material field are summarized,and future directions and prospects are discussed. | Guangfei Pan Feiyang Wang Chunlei Shang Honghui Wu Guilin Wu Junheng Gao Shuize Wang Zhijun Gao Xiaoye Zhou Xinping Mao | 2023 | International Journal of Minerals,Metallurgy and Materials2023，30，6： | 0 |
| 3 | Improved data-driven performance of Charpy impact toughness via literature-assisted production data in pipeline steel显示文摘Pipeline transportation is one of the most economical ways to transport crude oil and natural gas over long distances.High toughness is one of the important qualities of pipeline steel to ensure safe transportation,wherein a key factor characterizing toughness is Charpy impact toughness(CIT).In this work,according to the production line data provided by a steel mill and the experimental data collected in literature,two machine learning model construction strategies were proposed.One was based solely on the production line dataset,and the other was based on the production line dataset together with the literature dataset.In these two strategies,the random forest model displayed the best prediction results,the accuracy of strategy I was 0.58,and the accuracy of strategy II was 0.90,wherein literature data effectively improved the CIT prediction accuracy.Finally,an optimized CIT model based on machine learning algorithms was established.The proposed strategy of literature data-assisted production line data provides a new perspective for optimizing and predicting the performance of traditional structural materials. | SHANG ChunLei WANG ChuanJun WU HongHui LIU WenYue CHEN YiMian PAN GuangFei WANG ShuiZe WU GuiLin GAO JunHeng ZHAO HaiTao ZHANG ChaoLei MAO XinPing | 2023 | Science China(Technological Sciences)2023，66，7： | 0 |
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