| 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 Author | Li, Jinshan(ljsh@nwpu.edu.cn) ; Wang, William Yi(wywang@nwpu.edu.cn) ; Song, Haifeng(song_haifeng@iapcm.ac.cn) |
| 2021 | |
| Source Publication | ACTA MATERIALIA
 |
| ISSN | 1359-6454 |
| Volume | 202Pages:211-221 |
| Abstract | Based 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. |
| Keyword | High-throughput calculation Machine learning Electron work function Similar atomic environment Bonding charge density |
| Funding Organization | National 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 |
| DOI | 10.1016/j.actamat.2020.10.056 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National 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 Area | Materials Science ; Metallurgy & Metallurgical Engineering |
| WOS Subject | Materials Science, Multidisciplinary ; Metallurgy & Metallurgical Engineering |
| WOS ID | WOS:000599953700005 |
| Publisher | PERGAMON-ELSEVIER SCIENCE LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.imr.ac.cn/handle/321006/158747 |
| Collection | 中国科学院金属研究所 |
| Corresponding Author | Li, Jinshan; Wang, William Yi; Song, Haifeng |
| Affiliation | 1.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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