中国材料数据库与公共服务平台建设

doi:10.3981/j.issn.1000-7857.2015.10.004

Abstract
Figure/Table
References (0)
Related Citation (15)

Download: PDF (1503 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract: Materials data play an increasingly vital role in national security, performance safety, scientific and technological innovation and smart manufacturing in the age of information technology. In 2011, the Materials Genome Initiative (MGI) was launched in the US, of which materials data together with materials computation and materials experimentation and characterization consist of the three tools for accelerating the materials development continuum and reducing the cost. Both the researchers and production managers come to realize the significant role materials data play. The attributes of materials data, such as variety, complex interrelationship, acquiring process as well as the intellectual property issues, drive the process of collection, storage and application ever more complicated. In this paper, the characteristics, classification and status quo of materials data are described. The strategies and obstacles are systematically analyzed for materials data development and database construction. A national platform of materials data for research and public service, a materials data hub in China, is essential and urgently required for MGI implementation. Four aspects are emphasized, that is, materials data repository, infrastructure and cloud service, data mining and international collaboration. The materials data repositories for civil and military uses will be constructed. On the platform, the standards are crucial for materials data and database and big data application, which need to be set up first. The customized database and data push will bring great benefit for materials database users. Materials data science will definitely become a brand new subject in materials science, including materials informatics and materials dataology.

Key words： materials date materials genome initiative materials database data mining smart manufacturing materials data science

Received: 02 April 2015

ZTFLH:

N37

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	YIN Haiqing
	LIU Guoquan
	JIANG Xue
	ZHANG Ruijie
	QU Xuanhui

Cite this article:

YIN Haiqing,LIU Guoquan,JIANG Xue, et al. Materials databases and constructing national public service platform of materials data[J]. Science & Technology Review, 2015, 33(10): 50-59.

URL:

http://www.kjdb.org/EN/10.3981/j.issn.1000-7857.2015.10.004 OR http://www.kjdb.org/EN/Y2015/V33/I10/50

[1] Hey T. The fourth paradigm: Data- intensive scientific discovery[M]. Redmond, WA: Microsoft Research, 2009: 109-130.
[2] 维克托·迈尔-舍恩伯格, 肯尼思·库克耶. 大数据时代:生活、工作与思维的大变革[M]. 盛杨燕, 周涛, 译. 杭州: 浙江人民出版社, 2013: 2-18. Mayer- Schönberger V, Cukier K. Big data: A revolution that will transform how we live, work and think[M]. Sheng Yangyan, Zhou Tao, trans. Hangzhou: Zhejiang People's Publishing House, 2013: 2-18.
[3] Xiang X D, Sun X, Briceno G, et al. A combinatorial approach to materials discovery[J]. Science, 1995, 268(5218): 1738-1740.
[4] Liu Z K, Chen L Q, Raghavan P, et al. An integrated framework for multi- scale materials simulation and design[J]. Journal of Computer- Aided Materials Design, 2004, 11(2-3): 183-199.
[5] Olsen G B. Pathways of discovery designing a new material world[J]. Science, 2000, 228(12): 933-998.
[6] Yang Y, Lin T, Weng X L, et al. Data flow modeling, data mining and QSAR in high- throughput discovery of functional nanomaterials[J]. Computers & Chemical Engineering, 2011, 35(4): 671-678.
[7] 王盼盼, 杨迪. 阿里巴巴拟以大数据推动智能制造[EB/OL]. 2014-11- 01. http://news.xinhuanet.com/fortune/2014-11/01/c_1113074082.htm. Wang Panan, Yang Di. Alibaba intends to promote smart manufacturing with big data [EB/OL]. 2014-11-01. http://news.xinhuanet.com/fortune/ 2014-11/01/c_1113074082.htm.
[8] 师昌绪. 材料大辞典[M]. 北京: 化学工业出版社, 1994. Shi Changxu. Materials comprehensive dictionary[M]. Beijing: Chemical Industry Press, 1994.
[9] 36大数据. 中国大数据行业面临的五大挑战以及应对策略[EB/OL]. 2014.11.21. http://www.36dsj.com/archives/17137. 36 big data. The strategies for the five challenges the big data industry are facing[EB/OL]. 2014.11.21. http://www.36dsj.com/archives/17137.
[10] Kietzmann J, Pitt L, Berthon P. Disruptions, decisions, and destinations: Enter the age of 3-D printing and additive manufacturing[J]. Business Horizons, 2014. http://dx.doi.org/10.1016/j.bushor.2014.11.005.
[11] Chou D T, Wells D, Hong D, et al. Novel processing of iron-manganese alloy-based biomaterials by inkjet 3-D printing[J]. Acta Biomaterialia, 2013, 9(10): 8593-8603.
[12] 胡良霖. 科学数据资源的质量控制和评估[J]. E-Science, 2009(1): 50-55. Hu Lianglin. The quality control and evaluation of scientific data resources[J]. E-Science, 2009(1): 50-55.
[13] Sargent P. Data quality in materials information systems[J]. Computer- Aided Design, 1992, 24(9): 477-490.
[14] Bhagwat S A, Patterson K Y, Holden J M. Validation study of the USDA's data quality evaluation system[J]. Journal of Food Composition and Analysis, 2009, 22(5): 366-372.
[15] 罗芳. 土地利用数据综合结果的质量评价[D]. 武汉: 武汉大学, 2013. Luo Fang. Quality evaluation for land use data generalization[D]. Wuhan: Wuhan University, 2013.
[16] 苗海芳. 多元交通信息数据质量评价和控制方法研究[D]. 长春: 吉林大学, 2014. Miao Haifang. Research on evaluation and control methods on multisource traffic information data[D]. Changchun: Jilin University, 2014.
[17] Curtarolo S, Morgan D, Persson K, et al. Predicting crystal structures with data mining of quantum calculations[J]. Physical Review Letters, 2003, 91(13): 135503.
[18] 黎建辉. 科学数据共享的新机制与新趋势[C]//第507次香山会议. 北京, 2014-10-22. Li Jianhui. New mechanism and trend of scientific data sharing[C]// No. 507 Xiangshang Science Conference, Beijing, 2014-10-22.
[19] Morgan D, Ceder G, Curtarolo S. High-throughput and data mining with ab initio methods[J]. Measurement Science and Technology, 2005, 16(1): 296.
[20] 朱扬勇, 熊赟. 数据学[M]. 上海: 复旦大学出版社, 2009. Zhu Yangyong, Xiong Yun. Dataology[M]. Shanghai: Fudan University Press, 2009.
[21] National Research Council (US). Committee on Integrated Computational Materials Engineering. Integrated computational materials engineering: A transformational discipline for improved competitiveness and national security[M]. Washington, DC: National Academies Press, 2008.
[22] Robinson L. New TMS study tackles the challenge of integrating materials simulations across length scales[J]. JOM, 2014, 66(8): 1356- 1359.
[23] Sally T, David L M, Amanda B, et al. Sharing data in materials science[J]. Nature, 2013, 503: 463-464.