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Integrating data mining and machine learning to discover high-strength ductile titanium alloys
Zou, Chengxiong1; Li, Jinshan1; Wang, William Yi1; Zhang, Ying1; Lin, Deye2; Yuan, Ruihao1; Wang, Xiaodan1; Tang, Bin1; Wang, Jun1; Gao, Xingyu3; Kou, Hongchao1; Hui, Xidong4; Zeng, Xiaoqin5; Qian, Ma6; Song, Haifeng3; Liu, Zi-Kui7; Xu, Dongsheng8
Corresponding AuthorLi, Jinshan(ljsh@nwpu.edu.cn) ; Wang, William Yi(wywang@nwpu.edu.cn) ; Song, Haifeng(song_haifeng@iapcm.ac.cn)
2021
Source PublicationACTA MATERIALIA
ISSN1359-6454
Volume202Pages:211-221
AbstractBased on the growing power of computational capabilities and algorithmic developments, with the help of data-driven and high-throughput calculations, a new paradigm accelerating materials discovery, design and optimization is emerging. Titanium (Ti) alloys have been chosen herein to highlight an integrated computational materials engineering case study with the aim of improving their strength and ductility. The electronic properties of elemental building blocks were derived from high-throughput first-principles calculations and presented in the form of the Mendeleev periodic table, including their electron work function (Phi), Fermi energy (E-F), bonding charge density (Delta rho), and lattice distortion energy. The atomic and electronic insights of the composition-structure-property relationships were revealed by a data mining approach, addressing the key features/principles for the design strategies of advanced alloys. Guided by defect engineering, the deformation fault energy and dislocation width were treated as the dominating criteria in improving the ductility. The proposed yield strength model was utilized quantitatively to present the contributions of solid-solution strengthening and grain refinement hardening. Machine learning was used collaboratively with fundamental knowledge and feed back into a new training model, shown to be superior to the empirical molybdenum equivalence method. The results draw a conclusion that the integration of data mining and machine learning will not only generate plausible explanations and address new hypotheses, but also enable the design of strong and ductile Ti alloys in a more efficient and cost-effective way. (C) 2020 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
KeywordHigh-throughput calculation Machine learning Electron work function Similar atomic environment Bonding charge density
Funding OrganizationNational Key Research and Development Program of China ; Science Challenge Project ; National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities in China
DOI10.1016/j.actamat.2020.10.056
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2016YFB0701304] ; National Key Research and Development Program of China[2016YFB0701303] ; Science Challenge Project[TZ2018002] ; National Natural Science Foundation of China[51690163] ; Fundamental Research Funds for the Central Universities in China[G2016KY0302]
WOS Research AreaMaterials Science ; Metallurgy & Metallurgical Engineering
WOS SubjectMaterials Science, Multidisciplinary ; Metallurgy & Metallurgical Engineering
WOS IDWOS:000599953700005
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Cited Times:20[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/158747
Collection中国科学院金属研究所
Corresponding AuthorLi, Jinshan; Wang, William Yi; Song, Haifeng
Affiliation1.Northwestern Polytech Univ, State Key Lab Solidificat Proc, Xian 710072, Shaanxi, Peoples R China
2.CAEP Software Ctr High Performance Numer Simulat, Beijing 100088, Peoples R China
3.Inst Appl Phys & Computat Math, Lab Computat Phys, Beijing, Peoples R China
4.Univ Sci & Technol Beijing, State Key Lab Adv Met & Mat, Beijing 100083, Peoples R China
5.Shanghai Jiao Tong Univ, Sch Mat Sci & Engn, Shanghai 200240, Peoples R China
6.RMIT Univ, Sch Engn, Ctr Addit Mfg, Melbourne, Vic 3000, Australia
7.Penn State Univ, Dept Mat Sci & Engn, University Pk, PA 16802 USA
8.Chinese Acad Sci, Inst Met Res, 72 Wenhua Rd, Shenyang 110016, Peoples R China
Recommended Citation
GB/T 7714
Zou, Chengxiong,Li, Jinshan,Wang, William Yi,et al. Integrating data mining and machine learning to discover high-strength ductile titanium alloys[J]. ACTA MATERIALIA,2021,202:211-221.
APA Zou, Chengxiong.,Li, Jinshan.,Wang, William Yi.,Zhang, Ying.,Lin, Deye.,...&Xu, Dongsheng.(2021).Integrating data mining and machine learning to discover high-strength ductile titanium alloys.ACTA MATERIALIA,202,211-221.
MLA Zou, Chengxiong,et al."Integrating data mining and machine learning to discover high-strength ductile titanium alloys".ACTA MATERIALIA 202(2021):211-221.
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