Abstract：The "material genome project" is mainly to change the "trial and error" mode of material research. Through the collection and sorting of previous experimental data,combined with simulation computing technology and information technologies such as big data and blockchain,it can establish the basic database,big data management platform,high-throughput simulation computing,experiment and analysis platform of materials,and introduce advanced AI technology such as machine learning to provide effective data support for the rapid development of high-performance new materials. According to the data processing requirement in ultra-high strength steel research,the genome database of ultra-high strength steel and its management platform are established based on the basic data collection and high flux simulation calculation of ultra-high strength steel. It presents the structural framework of database system and the overall architecture of genome data management platform,and shows some applications developed on this platform,such as integrating experimental data to form experiment report,parsing computing data to form customized visualization,and so on. This platform can accommodate various data type,including numeric,text,rich text,table,function,image,etc. and support the storage and management of TB-level mass data and the index and retrieve of million-level data records. It can implement the intelligent management of the whole process from online collection,normalized processing,storing and managing to retrieving and analyzing,so as to provide requisite data supporting for the research work of ultra-high strength steel. As a result,the platform can effectively improve the data utilization and analysis capability of research group and provide high-quality data services for developing new ultra-high strength steel.
雍兮, 刘振宝, 王长军, 宁静. 超高强钢的基因组数据库管理平台建设及应用[J]. 钢铁, 2023, 58(2): 168-172.
YONG Xi, LIU Zhen-bao, WANG Chang-jun, NING Jing. Construction and application of genome database management platform of ultra high strength steel[J]. Iron and Steel, 2023, 58(2): 168-172.
[1] 李波,杜勇,邱联昌,等. 浅谈集成计算材料工程和材料基因工程:思想及实践[J]. 中国材料进展,2018,37(7):506. (LI Bo,DU Yong,QIU Lian-chang,et al. Shallow talk about integrated computational materials engineering and materials genome initiative:Ideas and practice[J]. Materials China,2018,37(7):506.) [2] 王卓,杨小渝,郑宇飞,等. 材料基因组框架下的材料集成设计及信息平台初探[J]. 科学通报,2013,58(35):3733. (WANG Zhuo,YANG Xiao-yu,ZHENG Yu-fei,et al. Integrated materials design and informatics platform within the materials genome framework[J]. Chinese Science Bulletin,2013,58(35):3733.) [3] 何鹏,林盼盼. 基于材料基因组理念的钎焊材料开发与智能钎焊技术创新系统工程[J]. 材料导报,2019,33(1):156. (HE Peng,LIN Pan-pan. The systematic project involving brazes development and intelligent brazing technology innovation:A materials genome perspective[J]. Materials Review,2019,33(1):156.) [4] 杨小渝,任杰,王娟,等. 基于材料基因组计划的计算和数据方法[J]. 科技导报,2016,34(24):62. (YANG Xiao-yu,REN Jie,WANG Juan,et al. Computational and data management based on material genome initiative[J]. Science and Technology Review,2016,34(24):62.) [5] 肖睿娟,李泓,陈立泉. 基于材料基因组方法的锂电池新材料开发[J]. 物理学报,2018,67(12):309. (XIAO Rui-juan,LI Hong,CHEN Li-quan. Development of new lithium battery materials by material genome initiative[J]. Acta Physica Sinica,2018,67(12):309.) [6] LU X G. Remarks on the recent progress of materials genome initiative[J]. Science Bulletin,2015,60(22):1966. [7] Drosback M. Materials genome initiative:Advances and initiatives[J]. Journal of Metals,2014,66(3):334. [8] 贾兴祺,李伟,许元涛,等. 基于增材制造的钢铁块体材料高通量实验方法[J]. 上海金属,2022,44(2):1. (JIA Xing-qi,LI Wei,XU Yuan-tao, et al. High-throughput experimental method for steel bulk materials based on additive manufacturing[J]. Shanghai Metals,2022,44(2):1.) [9] Dima A,Bhaskarla S,Becker C,et al. Informatics infrastructure for the materials genome initiative[J]. Journal of Metals,2016,68(8):2053. [10] 范晓丽. 材料基因组计划与第一性原理高通量计算[J]. 中国材料进展,2015,34(9):689. (FAN Xiao-li. Materials genome initiative and first-principles high-throughput computation[J]. Materials China,2015,34(9):689.) [11] LIU Ying-li,NIU Chen,WANG Zhuo,et al. Machine learning in materials genome initiative:A review[J]. Journal of Materials Science and Technology,2020,22:113. [12] 任子平,李德刚,李晓伟,等. 钢铁企业大数据研发平台的建设与思考[J]. 钢铁研究学报,2021,33(11):1118. (REN Zi-ping,LI De-gang,LI Xiao-wei,et al. Construction and thinking of big data research and development platform for steel enterprises[J]. Journal of Iron and Steel Research,2021,33(11):1118.) [13] 张福明. 智能化钢铁制造流程信息物理系统的设计研究[J]. 钢铁,2021,56(6):1. (ZHANG Fu-ming. Research and design on cyber physics system of intelligent iron and steel manufacturing process[J]. Iron and Steel,2021,56(6):1.) [14] 刘玠. 人工智能推动冶金工业变革[J]. 钢铁,2020,55(6):1. (LIU Jie. Artificial intelligence drives changes in metallurgical industry[J]. Iron and Steel,2020,55(6):1.) [15] HUANG Xiao-ya,WANG Hao,XUE Wei-hua,et al. Study on time-temperature-transformation diagrams of stainless steel using machine-learning approach[J]. Computational Materials Science,2020,171:109282. [16] 李姿昕,张能,熊斌,等. 材料科学数据库在材料研发中的应用与展望[J]. 数据与计算发展前沿,2020,2(2):78. (LI Zi-xin,ZHANG Neng,XIONG Bin,et al. Materials science database in material research and development:Recent applications and prospects[J]. Frontiers of Data and Computing,2020,2(2):78.) [17] 李建军. 钢铁企业大数据平台的开发及应用[J]. 自动化应用,2018(9):63. (LI Jian-jun. Development and application of big data platform for iron and steel enterprises[J]. Automation Application,2018(9):63.) [18] 吕庆,刘颂,刘小杰,等. 基于大数据技术的烧结全产线质量智能控制系统[J]. 钢铁,2018,53(7):1. (Ly Qing,LIU Song,LIU Xiao-jie,et al. Intelligent control system based on big data technology for whole production line of sintering quality[J]. Iron and Steel,2018,53(7):1.) [19] 赵先存,宋为顺,杨志勇,等. 高强度超高强度不锈钢[M]. 北京:冶金工业出版社,2008. (ZHAO Xian-cun,SONG Wei-shun,YANG Zhi-yong,et al. High Strength and Ultra-High Strength Stainless Steel[M]. Beijing:Metallurgical Industry Press,2008.) [20] 钟锦岩,李进,钟平. 超高强度不锈钢的性能及应用[M]. 北京:化学工业出版社,2021. (ZHONG Jin-yan,LI Jin,ZHONG Ping. The Applications and Properties of Ultra-High Strength Stainless Steel[M].Beijing:Chemical Industry Press,2021.)
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