期刊首页 优先出版 当期阅读 过刊浏览 作者中心 关于期刊 English

《工程(英文)》 >> 2020年 第6卷 第6期 doi: 10.1016/j.eng.2020.05.001

数据标识编码——连接材料基因组工程数据库与可传承集成智能制造的桥梁

a State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi’an 710072, China
b CAEP Software Center for High Performance Numerical Simulation, Institute of Applied Physics and Computational Mathematics, Beijing 100088, China
c CRRC Tangshan Co., Ltd., Tangshan 063035, China
d Beijing Star Travel Space Technology. Co. Ltd., Beijing 100013, China
e Department of Materials Science and Engineering, Pennsylvania State University, University Park, PA 16802, USA

收稿日期: 2019-04-14 修回日期: 2019-04-23 录用日期: 2019-10-16 发布日期: 2020-05-07

下一篇 上一篇

摘要

数据标识符(DID)是所有类型数据库中必不可少的标签,尤其是与集成计算材料工程(ICME)、可传承集成智能制造(I3M)和工业物联网有关的数据库。依据全球多个官方文件的展望蓝图的指引和先进材料的快速发展,需要进行更多的研究工作来建立材料信息学的相关数据标准。本文提出了由一系列构建单元(意义段)组成的DID统一格式,该格式与国际和国家标准(如ISO/IEC29168-1:2000、GB/T 27766–2011、GA/T 543.2–2011、GM/T 0006–2012、GJB 7365–2011、SL 325–2014、SL 607–2018、WS 363.2–2011和QX/T 39–2005)中标识符的经典格式一致。每个构建单元均由大写字母和数字组成,不包含特殊符号。此外,依据ISO/IEC 10646国际标准中统一编码标识符单元的格式,对每个构建单元的总长度不做限制规定。基于这些规则,本研究提出的DID具有灵活性,便于在各种云平台之间进行扩展和共享。相应地,智能手机或特定机器可以构造和精确识读这些常见的二维(2D)码,包括汉信码(Hanxin Code)、龙贝(Lots Perception Matrix, LP)码、快速反应(Quick Response, QR)码、网格矩阵(Grid Matrix, GM)码和数据矩阵(Data Matrix, DM)码。通过将这些二维码作为一组与云平台相连的数据指纹,人们可以自动跟踪成分-工艺-结构-性能-服役全流程中的进度和更新,为加速先进材料的发现和制造以及提高研究产出、效能和协作铺平道路。

图片

图1

图2

图3

图4

图5

图6

图7

参考文献

[ 1 ] Zhou J, Li P, Zhou Y, Wang B, Zang J, Meng L. Toward new-generation intelligent manufacturing. Engineering 2018;4(1):11–20. 链接1

[ 2 ] Zhong RY, Xu X, Klotz E, Newman ST. Intelligent manufacturing in the context of Industry 4.0: a review. Engineering 2017;3(5):616–30. 链接1

[ 3 ] National Academies of Sciences, Engineering, and Medicine. The Fourth Industrial Revolution: proceedings of a workshop—in brief. Washington, DC: National Academies Press; 2017.

[ 4 ] National Science Technology Council. Materials genome initiative for global competitiveness. Washington, DC: National Science and Technology Council; 2011. 链接1

[ 5 ] Wang WY, Li JS, Liu WM, Liu ZK. Integrated computational materials engineering for advanced materials: a brief review. Comp Mater Sci 2019;158:42–8. 链接1

[ 6 ] de Pablo J, Jackson NE, Webb MA, Chen LQ, Moore JE, Morgan D, et al. New frontiers for the materials genome initiative. NPJ Comp Mater 2019;5(1):41. 链接1

[ 7 ] Jain A, Persson KA, Ceder G. Research update: the materials genome initiative: data sharing and the impact of collaborative ab initio databases. APL Mater 2016;4(5):053102. 链接1

[ 8 ] Jose R, Ramakrishna S. Materials 4.0: materials big data enabled materials discovery. Appl Mater Today 2018;10:127–32. 链接1

[ 9 ] Liu Y, Zhao T, Ju W, Shi S. Materials discovery and design using machine learning. J Mater 2017;3(3):159–77. 链接1

[10] Meredig B, Wolverton C. A hybrid computational–experimental approach for automated crystal structure solution. Nat Mater 2013;12(2):123–7. 链接1

[11] Kaufman L, Ågren J. CALPHAD, first and second generation—birth of the materials genome. Scr Mater 2014;70:3–6. 链接1

[12] Olson GB, Kuehmann CJ. Materials genomics: from CALPHAD to flight. Scr Mater 2014;70:25–30. 链接1

[13] Liu ZK. Ocean of data: integrating first-principles calculations and CALPHAD modeling with machine learning. J Phase Equilib Diffus 2018;39(5):635–49. 链接1

[14] Zhou BC, Wang WY, Liu ZK, Arróyave R. Electrons to phases of magnesium. In: Horstemeyer MF, editor. Integrated computational materials engineering (ICME) for metals: concepts and case studies. New York: John Wiley & Sons; 2018. 链接1

[15] Committee on Integrated Computational Materials Engineering, National Research Council. Integrated computational materials engineering: a transformational discipline for improved competitiveness and national security. Washington, DC: National Academies Press; 2008.

[16] Pollock TM. Alloy design for aircraft engines. Nat Mater 2016;15(8):809–15. 链接1

[17] Krajewski PE, Hector LG, Qi Y, Mishra RK, Sachdev AK, Bower AF, et al. Atoms to autos: a multi-scale approach to modeling aluminum deformation. JOM 2011;63(11):24–32. 链接1

[18] Modeling across scales: a roadmapping study for connecting materials models and simulations across length and time scales [Internet]. Warrendale: The Minerals, Metals & Materials Society; c2015 [cited 2019 Apr 23]. Available from: https://www.tms.org/multiscalestudy.

[19] Mounet N, Gibertini M, Schwaller P, Campi D, Merkys A, Marrazzo A, et al. Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds. Nat Nanotech 2018;13(3):246–52. 链接1

[20] Sendek AD, Yang Q, Cubuk ED, Duerloo KAN, Cui Y, Reed EJ. Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials. Energy Environ Sci 2017;10(1):306–20. 链接1

[21] Cubuk ED, Ivancic RJS, Schoenholz SS, Strickland DJ, Basu A, Davidson ZS, et al. Structure-property relationships from universal signatures of plasticity in disordered solids. Science 2017;358(6366):1033–7. 链接1

[22] Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, et al. The FAIR guiding principles for scientific data management and stewardship. Sci Data 2016;3:160018. 链接1

[23] Liu ZK, McDowell DL. The Penn State-Georgia Tech CCMD: ushering in the ICME Era. Integr Mater Manuf Innovation 2014;3:409–28. 链接1

[24] Liu X, Furrer D, Kosters J, Holmes J. Vision 2040: a roadmap for integrated, multiscale modeling and simulation of materials and systems. Technical paper. Washington, DC: NASA; 2018. No.: NASA/CR-2018-219771. 链接1

[25] Aspuru-Guzik A, Persson K, Tribukait-Vasconelos H. Materials acceleration platform—accelerating advanced energy materials discovery by integrating high-throughput methods with artificial intelligence [Internet]. Mission Innovation; 2018 Jan [cited 2019 Apr 23]. Available from: http://missioninnovation.net/wp-content/uploads/2018/01/Mission-Innovation-IC6-ReportMaterials-Acceleration-Platform-Jan-2018.pdf.

[26] Umehara M, Stein HS, Guevarra D, Newhouse PF, Boyd DA, Gregoire JM. Analyzing machine learning models to accelerate generation of fundamental materials insights. NPJ Comp Mater 2019;5:34. 链接1

[27] Broderick SR, Santhanam GR, Rajan K. Harnessing the big data paradigm for ICME: shifting from materials selection to materials enabled design. JOM 2016;68:2109–15. 链接1

[28] Xia Z. Hydrogen evolution: guiding principles. Nat Energy 2016;1(10):16155. 链接1

[29] Zhang Y, Ling C. A strategy to apply machine learning to small datasets in materials science. NPJ Comp Mater 2018;4(1):25. 链接1

[30] Ulissi ZW, Medford AJ, Bligaard T, Nørskov JK. To address surface reaction network complexity using scaling relations machine learning and DFT calculations. Nat Commun 2017;8(1):14621. 链接1

[31] Wang WY, Tang B, Lin DY, Zou C, Zhang Y, Shang SL, et al. A brief review of data-driven ICME for intelligently discovering advanced structural metal materials: insight into atomic and electronic building blocks. J Mater Res 2020;35:872–89. 链接1

[32] Xiong W, Olson GB. Cybermaterials: materials by design and accelerated insertion of materials. NPJ Comp Mater 2016;2(1):15009. 链接1

[33] Nosengo N. The material code. Nature 2016;533(7602):22–5. 链接1

[34] Olson GB. Designing a new material world. Science 2000;288(5468):993–8. 链接1

[35] Frankel D, Hatcher N, Snyder D, Sebastian J, Olson GB, Vernon G, et al. Improving manufacturing quality using integrated computational materials engineering. In: Proceedings of the 4th World Congress on Integrated Computational Materials Engineering; 2017 May 21–25; Ypsilanti, MI, USA; 2017. p. 23–32; 2017.

[36] May M. Companies in the cloud: digitizing lab operations. Science 2017;355 (6324):532–4. 链接1

[37] Madhavan K, Zentner L, Farnsworth V, Shivarajapura S, Zentner M, Denny N, et al. nanoHUB.org: cloud-based services for nanoscale modeling, simulation, and education. Nanotechnol Rev 2013;2(1):107–17. 链接1

[38] The Joint Technical Committee. ISO/IEC JTC 1. ISO/IEC 29168-1:2011: Information technology—open systems interconnection—part 1: object identifier resolution system. ISO standard. Geneva: International Organization for Standardization; 2011.

[39] Zhang W, Zhang YP, Liu B, Zhang DY, Fan XC. GB/T 27766–2011: Twodimensional barcode—grid matrix code. Chinese standard. Beijing: General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China, Standardization Administration; 2011. Chinese.

[40] Ma WY, Zhang SJ, Tang YJ, Chi SC, Luo Z, Xu WH, et al. GA/T 543.2–2011: The data element of public security (2). Chinese standard. Beijing: The Ministry of Public Security of the People’s Republic of China; 2011. Chinese. 链接1

[41] Liu P, Liu XD, Kong FY, Li YZ, Xu Q, Liu ZS, et al. GM/T 0006–2012: Cryptographic application identifier criterion specification. Chinese standard. Beijing: State Cryptography Administration; 2012. Chinese. 链接1

[42] Fan XC, Geng GP, Pan H, Huang XL, Zhang YP, Zhang G, et al. GJB 7365–2011: Grid matrix code. Chinese standard. Beijing: Chinese PLA General Armament Department; 2011. Chinese. 链接1

[43] Mao XW, Peng H, Pan MM, Gao JJ, Li YT, Cheng YL, et al. SL 325–2014: Structure and identifier for water quality database. Chinese standard. Beijing: Ministry of Water Resources of the People’s Republic of China; 2014. Chinese. 链接1

[44] Jiang B, Liu Q, Xu P, Xia P, Yu YC, Shao YT, et al. SL 607–2013: Structure and identifier in water resources literature database. Chinese standard. Beijing: Ministry of Water Resources of the People’s Republic of China; 2013. Chinese. 链接1

[45] Meng Q, Liu LH, Li LJ, Xu KJ, Ren QQ, Wang Y. WS 363.2–2011: Health data element dictionary—part 2: identification. Chinese standard. Beijing: Ministry of Health of the People’s Republic of China; 2011. Chinese. 链接1

[46] Wang GF, Wu ZQ. QX/T 39–2005: Core metadata content of meteorological dataset. Chinese standard. Beijing: China Meteorological Administration; 2005. Chinese. 链接1

[47] Shapira P. Making the future. Science 2017;358(6366):1007. 链接1

[48] Raccuglia P, Elbert KC, Adler PDF, Falk C, Wenny MB, Mollo A, et al. Machinelearning-assisted materials discovery using failed experiments. Nature 2016;533(7601):73–6. 链接1

[49] Kavvas ES, Catoiu E, Mih N, Yurkovich JT, Seif Y, Dillon N, et al. Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance. Nat Commun 2018;9 (1):1–9. 链接1

[50] Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Carvalhais N. Deep learning and process understanding for data-driven Earth system science. Nature 2019;566(7743):195–204. 链接1

[51] Gray J, Banchi L, Bayat A, Bose S. Machine-learning-assisted many-body entanglement measurement. Phys Rev Lett 2018;121:150503. 链接1

[52] Kim K, Ward L, He J, Krishna A, Agrawal A, Wolverton C. Machine-learningaccelerated high-throughput materials screening: discovery of novel quaternary Heusler compounds. Phys Rev Mater 2018;2(12):123801. 链接1

[53] Chen Y. Integrated and intelligent manufacturing: perspectives and enablers. Engineering 2017;3(5):588–95. 链接1

相关研究