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.
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.
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.