Machine learning in materials genome initiative:A review

被引：38

作者：

Yingli Liu ^{[1
,2
]}

Chen Niu ^{[1
]}

Zhuo Wang ^{[3
,4
]}

Yong Gan ^{[5
]}

Yan Zhu ^{[1
]}

Shuhong Sun ^{[6
]}

Tao Shen ^{[1
,2
]}

机构：

[1] Faculty of Information Engineering and Automation, Kunming University of Science and Technology

[2] Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology

[3] Light Alloy Research Institute, Central South University

[4] Chengdu MatAi Technology Co., Ltd

[5] Chinese Academy of Engineering

[6] Faculty of Materials Science and Engineering, Kunming University of Science and Technology

来源：

JournalofMaterialsScience&Technology | 2020年 / 57卷 / 22期

基金：

中国国家自然科学基金;

关键词：

D O I：

暂无

中图分类号：

TB30 [工程材料一般性问题]; TP181 [自动推理、机器学习];

学科分类号：

0805 ; 080502 ; 081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Discovering new materials with excellent performance is a hot issue in the materials genome initiative.Traditional experiments and calculations often waste large amounts of time and money and are also limited by various conditions. Therefore, it is imperative to develop a new method to accelerate the discovery and design of new materials. In recent years, material discovery and design methods using machine learning have attracted much attention from material experts and have made some progress. This review first outlines available materials database and material data analytics tools and then elaborates on the machine learning algorithms used in materials science. Next, the field of application of machine learning in materials science is summarized, focusing on the aspects of structure determination, performance prediction, fingerprint prediction, and new material discovery. Finally, the review points out the problems of data and machine learning in materials science and points to future research. Using machine learning algorithms, the authors hope to achieve amazing results in material discovery and design.

引用

页码：113 / 122

页数：10

共 42 条

[1] Impact of dataset uncertainties on machine learning model predictions: the example of polymer glass transition temperatures[J] . Anurag Jha,Anand Chandrasekaran,Chiho Kim,Rampi Ramprasad.Modelling and Simulation in Materials Science and . 2019 (2)
[2] Machine leaning aided study of sintered density in Cu-Al alloy[J] . Zhenghua Deng,Haiqing Yin,Xue Jiang,Cong Zhang,Kaiqi Zhang,Tong Zhang,Bin Xu,Qingjun Zheng,Xuanhui Qu.Computational Materials Science . 2018
[3] Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery
Oses, Corey
Toher, Cormac
Curtarolo, Stefano
[J]. MRS BULLETIN, 2018, 43 (09) : 670 - 675
[4] Matminer: An open source toolkit for materials data mining[J] . Logan Ward,Alexander Dunn,Alireza Faghaninia,Nils E.R. Zimmermann,Saurabh Bajaj,Qi Wang,Joseph Montoya,Jiming Chen,Kyle Bystrom,Maxwell Dylla,Kyle Chard,Mark Asta,Kristin A. Persson,G. Jeffrey Snyder,Ian Foster,Anubhav Jain.Computational Materials Science . 2018
[5] Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets[J] . Zijiang Yang,Yuksel C. Yabansu,Reda Al-Bahrani,Wei-keng Liao,Alok N. Choudhary,Surya R. Kalidindi,Ankit Agrawal.Computational Materials Science . 2018
[6] Machine learning for crystal identification and discovery
Spellings, Matthew
Glotzer, Sharon C.
[J]. AICHE JOURNAL, 2018, 64 (06) : 2198 - 2206
[7] MatCloud: A high-throughput computational infrastructure for integrated management of materials simulation, data and resources[J] . Xiaoyu Yang,Zongguo Wang,Xushan Zhao,Jianlong Song,Mingming Zhang,Haidong Liu.Computational Materials Science . 2018
[8] Materials 4.0: Materials big data enabled materials discovery[J] . Rajan Jose,Seeram Ramakrishna.Applied Materials Today . 2018
[9] A machine learning approach for viral genome classification[J] . Mohamed Amine Remita,Ahmed Halioui,Abou Abdallah Malick Diouara,Bruno Daigle,Golrokh Kiani,Abdoulaye Baniré Diallo.BMC Bioinformatics . 2017 (1)
[10] Crack detection in lithium-ion cells using machine learning[J] . Lukas Petrich,Daniel Westhoff,Julian Feinauer,Donal P. Finegan,Sohrab R. Daemi,Paul R. Shearing,Volker Schmidt.Computational Materials Science . 2017

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