Accomplishment and challenge of materials database toward big data

doi:10.1088/1674-1056/27/11/118901

中国物理B ›› 2018, Vol. 27 ›› Issue (11): 118901-118901.doi: 10.1088/1674-1056/27/11/118901

所属专题： TOPICAL REVIEW — Physics research in materials genome

• SPECIAL TOPIC—Recent advances in thermoelectric materials and devices • 上一篇下一篇

Accomplishment and challenge of materials database toward big data

Yibin Xu(徐一斌)

National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 3050047, Japan

收稿日期:2018-06-04 修回日期:2018-09-16 出版日期:2018-11-05 发布日期:2018-11-05
通讯作者: Yibin Xu E-mail:xu.yibin@nims.go.jp
基金资助:
Project supported by "Materials Research by Information Integration" Initiative (MI²I) project of the Support Program for Starting Up Innovation Hub from Japan Science and Technology Agency (JST).

Accomplishment and challenge of materials database toward big data

Yibin Xu(徐一斌)

National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 3050047, Japan

Received:2018-06-04 Revised:2018-09-16 Online:2018-11-05 Published:2018-11-05
Contact: Yibin Xu E-mail:xu.yibin@nims.go.jp
Supported by:
Project supported by "Materials Research by Information Integration" Initiative (MI²I) project of the Support Program for Starting Up Innovation Hub from Japan Science and Technology Agency (JST).

摘要/Abstract

摘要：

The history and current status of materials data activities from handbook to database are reviewed, with introduction to some important products. Through an example of prediction of interfacial thermal resistance based on data and data science methods, we show the advantages and potential of material informatics to study material issues which are too complicated or time consuming for conventional theoretical and experimental methods. Materials big data is the fundamental of material informatics. The challenges and strategy to construct materials big data are discussed, and some solutions are proposed as the results of our experiences to construct National Institute for Materials Science (NIMS) materials databases.

关键词: material database, big data, material informatics, machine learning, interfacial thermal resistance, material identification

Abstract:

Key words: material database, big data, material informatics, machine learning, interfacial thermal resistance, material identification

中图分类号: (Computer science and technology)

89.20.Ff

01.65.+g (History of science) 65.80.-g (Thermal properties of small particles, nanocrystals, nanotubes, and other related systems)

徐一斌. Accomplishment and challenge of materials database toward big data[J]. 中国物理B, 2018, 27(11): 118901-118901.

Yibin Xu(徐一斌). Accomplishment and challenge of materials database toward big data[J]. Chin. Phys. B, 2018, 27(11): 118901-118901.

参考文献 23

[1]	Luckenbach R 1981 J. Chem. Inf. Comput. Sci. 21 82 doi: 10.1021/ci00030a006
[2]	Reaxys, Elsevier, https://www.elsevier.com/solutions/reaxys[2018-05-25]
[3]	SpringerMaterials-The Landolt-Börnstein Database, SpringerMateri-als, Customer Presentation, 2009
[4]	SpringerMaterials, https://materials.springer.com[2018-05-25]
[5]	Journal of Physical and Chemical Reference Data, https://www.nist.gov/srd/journal-physical-and-chemical-reference-data-jpcrd[2018-05-25]
[6]	Creep data sheet, http://smds.nims.go.jp/creep/indexen.html[2018-05-25]
[7]	Fatigue data sheet, http://smds.nims.go.jp/fatigue/indexen.html[2018-05-25]
[8]	Corrosion data sheet, http://smds.nims.go.jp/corrosion/indexen.html[2018-05-25]
[9]	ASM handbooks, https://www.asminternational.org/handbooks[2018-05-25]
[10]	Touloukian Y S and Ho C Y 1970 Thermophysical Properties of Matter, the TPRC Data Series (Purdue University)
[11]	Codd E F 1972 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.86.9277[2018-05-25] doi: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.86.9277[2018-05-25]
[12]	The Pauling File project, http://paulingfile.com/index.php?p=scope#thePAULINGFILEproject[2018-05-25]
[13]	Inorganic materials database AtomWork, http://crystdb.nims.go.jp/indexen.html[2018-05-25]
[14]	Inorganic materials database AtomWork Adv., https://atomworkadv.nims.go.jp[2018-05-28]
[15]	NIMS materials database MatNavi, http://mits.nims.go.jp/indexen.html[2018-05-25]
[16]	NOMAD repository, http://nomad-repository.eu[2018-05-25]
[17]	The Materials Project, https://materialsproject.org[2018-05-25]
[18]	Materials Research by Information Integration Initiative Data Platform,https://mi2i.nims.go.jp/en[2018-05-25]
[19]	Swartz E T and Pohl R O 1989 Rev. Mod. Phys. 61 605 doi: 10.1103/RevModPhys.61.605
[20]	Zhan T, Fang L and Xu Y 2017 Sci. Rep. 7 7109 doi: 10.1038/s41598-017-07150-7
[21]	Xu Y, Kato R and Goto M 2010 J. Appl. Phys. 108 104317 doi: 10.1063/1.3514563
[22]	Zhan T, Xu Y, Goto M, Tanaka Y, Kato R and Sasaki M 2015 RSC Adv. 5 49703 doi: 10.1039/C5RA04412J
[23]	MatML, https://www.matml.org/overview.htm[2018-09-16]

Accomplishment and challenge of materials database toward big data

Accomplishment and challenge of materials database toward big data

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

参考文献 23

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	Jinlong Hu(胡锦龙), Yuting Zuo(左钰婷), Yuzhou Hao(郝昱州), Guoyu Shu(舒国钰), Yang Wang(王洋), Minxuan Feng(冯敏轩), Xuejie Li(李雪洁), Xiaoying Wang(王晓莹), Jun Sun(孙军), Xiangdong Ding(丁向东), Zhibin Gao(高志斌), Guimei Zhu(朱桂妹), Baowen Li(李保文). Prediction of lattice thermal conductivity with two-stage interpretable machine learning[J]. 中国物理B, 2023, 32(4): 46301-046301.
[2]	Xue-Yi Guo(郭学仪), Shang-Shu Li(李尚书), Xiao Xiao(效骁), Zhong-Cheng Xiang(相忠诚), Zi-Yong Ge(葛自勇), He-Kang Li(李贺康), Peng-Tao Song(宋鹏涛), Yi Peng(彭益), Zhan Wang(王战), Kai Xu(许凯), Pan Zhang(张潘), Lei Wang(王磊), Dong-Ning Zheng(郑东宁), and Heng Fan(范桁). Variational quantum simulation of thermal statistical states on a superconducting quantum processer[J]. 中国物理B, 2023, 32(1): 10307-010307.
[3]	Jing Yue(岳靖), Jian Li(李剑), Wen Zhang(张文), and Zhangxin Chen(陈掌星). The coupled deep neural networks for coupling of the Stokes and Darcy-Forchheimer problems[J]. 中国物理B, 2023, 32(1): 10201-010201.
[4]	Yong Huang(黄勇) and Yang Li(李扬). Data-driven modeling of a four-dimensional stochastic projectile system[J]. 中国物理B, 2022, 31(7): 70501-070501.
[5]	Tonghe Ying(应通和), Jianbao Zhu(朱健保), and Wenguang Zhu(朱文光). Machine learning potential aided structure search for low-lying candidates of Au clusters[J]. 中国物理B, 2022, 31(7): 78402-078402.
[6]	Shi-Jie Pan(潘世杰), Lin-Chun Wan(万林春), Hai-Ling Liu(刘海玲), Yu-Sen Wu(吴宇森), Su-Juan Qin(秦素娟), Qiao-Yan Wen(温巧燕), and Fei Gao(高飞). Quantum algorithm for neighborhood preserving embedding[J]. 中国物理B, 2022, 31(6): 60304-060304.
[7]	Ruoting Zhao(赵若廷), Bangyu Xing(邢邦昱), Huimin Mu(穆慧敏), Yuhao Fu(付钰豪), and Lijun Zhang(张立军). Evaluation of performance of machine learning methods in mining structure—property data of halide perovskite materials[J]. 中国物理B, 2022, 31(5): 56302-056302.
[8]	Yan-Yan Hou(侯艳艳), Jian Li(李剑), Xiu-Bo Chen(陈秀波), and Yuan Tian(田源). Quantum partial least squares regression algorithm for multiple correlation problem[J]. 中国物理B, 2022, 31(3): 30304-030304.
[9]	Jintao Yang(杨锦涛), Junpeng Cao(曹俊鹏), and Wen-Li Yang(杨文力). Dynamical learning of non-Markovian quantum dynamics[J]. 中国物理B, 2022, 31(1): 10314-010314.
[10]	Yicheng Wu(吴义成), Bin Xu(徐斌), Yitao Sun(孙奕韬), and Pengfei Guan(管鹏飞). Quantitative structure-plasticity relationship in metallic glass: A machine learning study[J]. 中国物理B, 2021, 30(5): 57103-057103.
[11]	Yuansheng Chen(陈元盛) and Fei Tong(仝飞). Stability analysis of hydro-turbine governing system based on machine learning[J]. 中国物理B, 2021, 30(12): 120509-120509.
[12]	朱震, 董宝娟, 郭怀红, 杨腾, 张志东. Fundamental band gap and alignment of two-dimensional semiconductors explored by machine learning[J]. 中国物理B, 2020, 29(4): 46101-046101.
[13]	M R C Mahdy, Hamim Mahmud Rivy, Ziaur Rahman Jony, Nabila Binte Alam, Nabila Masud, Golam Dastegir Al Quaderi, Ibraheem Muhammad Moosa, Chowdhury Mofizur Rahman, M Sohel Rahman. Dielectric or plasmonic Mie object at air-liquid interface: The transferred and the traveling momenta of photon[J]. 中国物理B, 2020, 29(1): 14211-014211.
[14]	张博, 郑新奇, 赵同云, 胡凤霞, 孙继荣, 沈保根. Machine learning technique for prediction of magnetocaloric effect in La(Fe, Si/Al)₁₃-based materials[J]. 中国物理B, 2018, 27(6): 67503-067503.
[15]	李锐, 刘瑶, 左淑兰, 赵同云, 胡凤霞, 孙继荣, 沈保根. Composition design for (PrNd-La–Ce)₂Fe₁₄B melt-spun magnets by machine learning technique[J]. 中国物理B, 2018, 27(4): 47501-047501.