Please wait a minute...
材料导报  2024, Vol. 38 Issue (13): 23020098-17    https://doi.org/10.11896/cldb.23020098
  金属与金属基复合材料 |
材料基因工程技术在电工材料研发中的应用与展望
王博, 盛鹏*, 徐丽*, 李圣驿, 白会涛, 李慧, 薛晴
国网智能电网研究院有限公司,北京 102209
Material Genome Engineering in Electrical Material Research and Development: Recent Applications and Prospects
WANG Bo, SHENG Peng*, XU Li*, LI Shengyi, BAI Huitao, LI Hui, XUE Qing
State Grid Smart Grid Research Institute Co., Ltd., Beijing 102209, China
下载:  全 文 ( PDF ) ( 49181KB ) 
输出:  BibTeX | EndNote (RIS)      
摘要 电工材料是电气装备的基础,其材料特性直接决定电气装备的极限电磁参数。随着科学技术的进步,生产、生活水平的不断提高,人们对电气装备具有的功能和性能的需求日益增加。理论研究和工程实践表明,以传统电工材料为基础生产的电气装备在功能和性能方面不能完全满足人类社会对先进电气装备快速增长的需求。电工新材料及其应用研究应使未来的电工装备具有挑战更高电磁参数极限的能力。材料基因工程为电工材料设计筛选提供了新的技术手段,通过利用高通量的方式设计与筛选材料,大幅缩短了材料的研发周期,显著降低了研发成本。本文综述了近几年材料基因工程先进理念与前沿技术在电工材料开发中的应用案例,这些案例表明了材料基因工程技术在电工材料研发中的适用性。本文还重点分析了以机器学习为代表的大数据技术在电工材料研发中的应用现状。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
王博
盛鹏
徐丽
李圣驿
白会涛
李慧
薛晴
关键词:  电工材料  材料基因工程  机器学习  高通量计算  高通量实验    
Abstract: Electrical materials are the basis of electrical equipment, and their characteristics directly determine the limit of electromagnetic parameters of electrical equipment. With the improvement of science, production, and living standards, the requirements for the functions and performance of electrical equipment have increased. Theoretical research and engineering practice show that electrical equipment based on traditional electrical materials cannot fully satisfy the rapidly growing needs of human society. The research of new electrician materials and their applications should allow the future electrician equipment to have the ability to challenge the limit of electromagnetic parameters, Material Genome Engineering provides new technical means for the design and screening of electrical materials in a high-throughput way, reducing the development cycle and cost of electrical materials by more than half. This paper presents examples of material genome engineering applied to the development of electrical materials. These cases show the applicability of material genome engineering techniques in the development of electrical materials. This paper also focuses on the current status of the application of big data technology represented by machine learning in the research and development of electrical materials.
Key words:  electrical material    material genome engineering    machine learning    high-throughput computing    high-throughput experiments
出版日期:  2024-07-10      发布日期:  2024-08-01
ZTFLH:  TM20  
基金资助: 国网智能电网研究院有限公司科技项目(525500200052)
通讯作者:  *盛鹏,国网智能电网研究院有限公司高级工程师。2014年于中科院化学所获得博士学位。目前主要从事电化学储能材料及器件的研发工作。发表论文40余篇,申请专利30余项,授权专利6项。280103281@qq.com
徐丽,国网智能电网研究院有限公司教授级高级工程师。2007年7月于北京科技大学获得博士学位。目前主要从事储能材料研发及应用技术工作,发表学术论文60余篇,授权国家专利近20项,出版及参编专著4部。sinoxuly@foxmail.com   
作者简介:  王博,国网智能电网研究院有限公司高级工程师,2014年1月于美国明尼苏达大学获得博士学位、目前主要从事储能材料研发及应用技术工作。发表文章30余篇,授权专利3项。
引用本文:    
王博, 盛鹏, 徐丽, 李圣驿, 白会涛, 李慧, 薛晴. 材料基因工程技术在电工材料研发中的应用与展望[J]. 材料导报, 2024, 38(13): 23020098-17.
WANG Bo, SHENG Peng, XU Li, LI Shengyi, BAI Huitao, LI Hui, XUE Qing. Material Genome Engineering in Electrical Material Research and Development: Recent Applications and Prospects. Materials Reports, 2024, 38(13): 23020098-17.
链接本文:  
http://www.mater-rep.com/CN/10.11896/cldb.23020098  或          http://www.mater-rep.com/CN/Y2024/V38/I13/23020098
1 Lin H, Lin Y, Pan F. Energy Storage Science and Technology, 2016, 5(6), 922 (in Chinese).
林海, 林原, 潘锋. 储能科学与技术, 2016, 5(6), 922.
2 Montoya J H, Persson K A. npj Computational Materials, 2017, 3(1), 1.
3 Mamun O, Winther K T, Boes J R, et al. Scientific Data, 2019, 6(1), 1.
4 Kirklin S, Meredig B, Wolverton C. Advanced Energy Materials, 2013, 3(2), 252.
5 Aykol M, Kim S, Hegde V I, et al. Nature Communications, 2016, 7(1), 1.
6 Feng R, Zhang C, Gao M C, et al. Nature Communications, 2021, 12(1), 1.
7 Qiao L, Liu Y, Zhu J. Journal of Alloys and Compounds, 2021, 877, 160295.
8 Shi C X, Zhong Z Y. Superalloys fifty years of China, Metallurgical Industry Press, China, 2006, pp.53(in Chinese).
师昌绪, 仲增墉. 中国高温合金五十年, 冶金工业出版社, 2006, pp.53.
9 Guo J T. Materials science and engineering for superalloys, Science Press, China, 2008, pp.342(in Chinese).
郭建亭. 高温合金材料学(上册): 应用基础理论, 科学出版社, 2008, pp.342.
10 Guo J T. Materials science and engineering for superalloys, Science Press, China, 2010, pp.102(in Chinese).
郭建亭. 高温合金材料学 (下册): 高温合金材料与工程应用, 科学出版社, 2010, pp.102.
11 Liu Y, Wang J, Xiao B, et al. Journal of Materials Informatics, 2022, 2(1), 3.
12 Jiang Y, Yang Z, Guo J, et al. Nature Communications, 2021, 12(1), 5950.
13 Zheng W D, Zhang H R, Hu H Q, et al. The Chinese Journal of Nonferrous Metals, 2019, 29(4), 803(in Chinese).
郑伟达, 张惠然, 胡红青, 等. 中国有色金属学报, 2019, 29(4), 803.
14 Ren F, Ward L, Williams T, et al. Science Advances, 2018, 4(4), 1566.
15 Council N R. Application of lightweighting technology to military aircraft, vessels, and vehicles, National Academies Press, Washington, DC, 2012, pp.32.
16 Yang X Y, Wang J, Ren J, et al. Chinese Journal of Computational Physics, 2017, 34(6), 697 (in Chinese).
杨小渝, 王娟, 任杰, 等. 计算物理, 2017, 34(6), 697.
17 Chen X J, Yang X Y, Yang P, et al. E-science Technology & Application, 2016(1), 67 (in Chinese).
陈晓婕, 杨小渝, 张平, 等. 科研信息化技术与应用, 2016(1), 67.
18 Yang X Y, Ren J, Wang J, et al. Science & Technology Review, 2016, 34(24), 62 (in Chinese).
杨小渝, 任杰, 王娟, 等. 科技导报, 2016, 34(24), 62.
19 Wang J, Yang X Y, Wang Z G, et al. E-science Technology & Application, 2016, 7(5), 3 (in Chinese).
王军, 杨小渝, 王宗国, 等. 科研信息化技术与应用, 2016, 7(5), 3.
20 Jain A, Ong S P, Hautier G, et al. APL Materials, 2013, 1(1), 11002.
21 Curtarolo S, Setyawan W, Wang S, et al. Computational Materials Science, 2012, 58, 227.
22 Jain A, Ong S P, Chen W, et al. Concurrency and Computation, Practice and Experience, 2015, 27(17), 5037.
23 Ong S P, Richards W D, Jain A, et al. Computational Materials Science, 2013, 68, 314.
24 Mounet N, Gibertini M, Schwaller P, et al. Nature Nanotechnology, 2018, 13(3), 246.
25 Yang X, Wang Z, Zhao X, et al. Computational Materials Science, 2018, 146, 319.
26 Zhao Q, Yang H, Liu J, et al. Materials & Design, 2021, 197, 109248.
27 Zhang H, Fu H, He X, et al. Acta Materialia, 2020, 200, 803.
28 Wang C, Fu H, Jiang L, et al. npj Computational Materials, 2019, 5, 1.
29 Ozerdem M S. Journal of Materials Processing Technology, 2008, 208(1-3), 470.
30 Li J, Zhang Y, Cao X, et al. Communications Materials, 2020, 1(1), 73.
31 Li X, Wang W, Guo Z, et al. In: 2020 International Symposium on Electrical Insulating Materials (ISEIM). Japan, 2020, pp.317.
32 Kang H, Lee J H, Choe Y, et al. Nanomaterials, 2021, 11(4), 872.
33 Alhindawi F, Altarazi S. In: 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). Thailand, 2018, pp.715.
34 Li B, Du Y, Qiu L C, et al. Materials China, 2018, 37(7), 506 (in Chinese).
李波, 杜勇, 邱联昌, 等. 中国材料进展, 2018, 37(7), 506.
[1] 张琦祥, 苑峻豪, 李震, 李文杰, 孙丹, 王清, 董闯. 基于第一性原理计算的固溶体合金集成学习设计方法[J]. 材料导报, 2024, 38(13): 23030089-8.
[2] 龙武剑, 罗盛禹, 程博远, 冯甘霖, 李利孝. 机器学习算法用于自密实混凝土性能设计的研究进展[J]. 材料导报, 2024, 38(11): 22110224-10.
[3] 高志玉, 樊献金, 高思达, 薛维华. 基于多模型机器学习的合金结构钢回火力学性能研究[J]. 材料导报, 2023, 37(6): 21090025-7.
[4] 王世杰, 杨杰, 马硕, 韩硕, 王龙, 段国林. 机器学习在功能梯度材料设计-制备中的应用综述[J]. 材料导报, 2023, 37(21): 22030237-9.
[5] 徐潇航, 胡张莉, 刘加平, 李文伟, 刘建忠. 基于机器学习回归模型的三峡大坝混凝土强度预测[J]. 材料导报, 2023, 37(2): 22010068-9.
[6] 余春秀, 王云凯, 贺子娟, 李玮, 陈家林, 李世鸿, 李俊鹏. 电子封装用环氧胶粘剂改性研究进展[J]. 材料导报, 2023, 37(15): 21120084-10.
[7] 李孝晨, 丁文艺, 朱霄汉, 郑明杰. 基于机器学习的RAFM钢中子辐照脆化预测模型研究[J]. 材料导报, 2023, 37(1): 22010142-7.
[8] 杜青铉, 张宇航, 孙伟豪, 刘蕊, 庄尧量, 夏军武. 基于混合模型的煤矸石透水混凝土透水系数预测[J]. 材料导报, 2022, 36(Z1): 22040077-5.
[9] 赵巍, 花福安, 李建平. 基于机器学习的Laves相生成焓预测研究[J]. 材料导报, 2022, 36(Z1): 21120179-5.
[10] 侯腾跃, 孙炎辉, 孙舒鹏, 肖瑛, 郑雁公, 王兢, 杜海英, 吴隽新. 机器学习在材料结构与性能预测中的应用综述[J]. 材料导报, 2022, 36(6): 20080205-12.
[11] 侯雅青, 苏航, 张浩, 王畅畅. 金属材料多尺度高通量制备研究进展[J]. 材料导报, 2022, 36(1): 20080258-10.
[12] 宋庆功, 常斌斌, 董珊珊, 顾威风, 康建海, 王明超, 刘志锋. 机器学习及其在材料研发中的作用[J]. 材料导报, 2022, 36(1): 20080139-7.
[13] 张猛, 花福安, 赵巍. 基于机器学习的高熵合金生成相预测研究[J]. 材料导报, 2021, 35(Z1): 331-335.
[14] 郑玉杰, 梁鑫斌, 张起, 孙文博, 施童超, 杜鹃, 孙宽. 基于分子指纹及机器学习回归模型的有机光伏材料效率预测[J]. 材料导报, 2021, 35(8): 8207-8212.
[15] 黄建国, 任淑彬. 选区激光熔化成型铝合金的研究现状及展望[J]. 材料导报, 2021, 35(23): 23142-23152.
[1] Yanzhen WANG, Mingming CHEN, Chengyang WANG. Preparation and Electrochemical Properties Characterization of High-rate SiO2/C Composite Materials[J]. Materials Reports, 2018, 32(3): 357 -361 .
[2] Yimeng XIA, Shuai WU, Feng TAN, Wei LI, Qingmao WEI, Chungang MIN, Xikun YANG. Effect of Anionic Groups of Cobalt Salt on the Electrocatalytic Activity of Co-N-C Catalysts[J]. Materials Reports, 2018, 32(3): 362 -367 .
[3] Qingshun GUAN,Jian LI,Ruyuan SONG,Zhaoyang XU,Weibing WU,Yi JING,Hongqi DAI,Guigan FANG. A Survey on Preparation and Application of Aerogels Based on Nanomaterials[J]. Materials Reports, 2018, 32(3): 384 -390 .
[4] Lijing YANG,Zhengxian LI,Chunliang HUANG,Pei WANG,Jianhua YAO. Producing Hard Material Coatings by Laser-assisted Cold Spray:a Technological Review[J]. Materials Reports, 2018, 32(3): 412 -417 .
[5] Zhiqiang QIAN,Zhijian WU,Shidong WANG,Huifang ZHANG,Haining LIU,Xiushen YE,Quan LI. Research Progress in Preparation of Superhydrophobic Coatings on Magnesium Alloys and Its Application[J]. Materials Reports, 2018, 32(1): 102 -109 .
[6] Wen XI,Zheng CHEN,Shi HU. Research Progress of Deformation Induced Localized Solid-state Amorphization in Nanocrystalline Materials[J]. Materials Reports, 2018, 32(1): 116 -121 .
[7] Xing LIANG, Guohua GAO, Guangming WU. Research Development of Vanadium Oxide Serving as Cathode Materials for Lithium Ion Batteries[J]. Materials Reports, 2018, 32(1): 12 -33 .
[8] Hao ZHANG,Yongde HUANG,Yue GUO,Qingsong LU. Technological and Process Advances in Robotic Friction Stir Welding[J]. Materials Reports, 2018, 32(1): 128 -134 .
[9] Laima LUO, Mengyao XU, Xiang ZAN, Xiaoyong ZHU, Ping LI, Jigui CHENG, Yucheng WU. Progress in Irradiation Damage of Tungsten and Tungsten AlloysUnder Different Irradiation Particles[J]. Materials Reports, 2018, 32(1): 41 -46 .
[10] Fengsen MA,Yan YU,Jie ZHANG,Haibo CHEN. A State-of-the-art Review of Cytotoxicity Evaluation of Biomaterials[J]. Materials Reports, 2018, 32(1): 76 -85 .
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed