Accelerated design for elastocaloric performance in NiTi-based alloys through machine learning
文献类型：期刊论文
| 作者 | Tian, Xiaohua1; Zhao, Qiu1; Zhang, Kun2,4; Li, Hongxing4; Han, Binglun4; Shi, Dingding1; Zhou, Liwen1; Ma, Tianyou4; Wang, Cheng4; Wen, Qinlong3 |
| 刊名 | JOURNAL OF APPLIED PHYSICS
 |
| 出版日期 | 2022-01-07 |
| 卷号 | 131期号:1页码:10 |
| ISSN号 | 0021-8979 |
| DOI | 10.1063/5.0068290 |
| 通讯作者 | Zhang, Kun(kunzhang@hrbust.edu.cn) |
| 英文摘要 | NiTi-based shape memory alloys (SMAs) are regarded as one of the most promising materials for engineering applications of elastocaloric refrigeration. A critical mission is to efficiently explore the new NiTi-based SMAs with large adiabatic temperature change (& UDelta;T-ad). We proposed a new material design method that combines highly correlated microscale physical information (volume change, & UDelta; V) into machine learning to predict & UDelta;T-ad of NiTi-based alloys. First, we tightly coupled machine learning with first-principles calculations to accelerate receiving lattice parameters before and after the phase transformation and predict the & UDelta; V, which shows excellent performance with the coefficient of determination R-2 > 0.99. Then, relevant features, including the & UDelta; V, are considered to predict the & UDelta;T-ad in NiTi-based SMAs. Moreover, due to the small dataset, the principal component analysis and the independent component analysis are added. We evaluate the performance of three machine learning models [Lasso regression, support vector regression, and decision tree regression (DTR)]. Finally, the DTR model exhibits a high accuracy for predicting & UDelta;T-ad (R-2 > 0.9). Introducing the feature of & UDelta; V into the machine learning process can improve the accuracy and efficiency of model design. Further, this work paves the way to accelerate the discovery of new excellent materials for practical applications of elastocaloric refrigeration. |
| 资助项目 | National Natural Science Foundation of China (NNSFC)[51971085] ; National Natural Science Foundation of China (NNSFC)[51871083] ; National Natural Science Foundation of China (NNSFC)[52001101] ; China Postdoctoral Science Foundation[2021M693229] |
| WOS研究方向 | Physics |
| 语种 | 英语 |
| 出版者 | AIP Publishing |
| WOS记录号 | WOS:000744570400010 |
| 资助机构 | National Natural Science Foundation of China (NNSFC) ; China Postdoctoral Science Foundation |
| 源URL | [http://ir.imr.ac.cn/handle/321006/173601]  |
| 专题 | 金属研究所_中国科学院金属研究所 |
| 通讯作者 | Zhang, Kun |
| 作者单位 | 1.Harbin Univ Sci & Technol, Sch Elect & Elect Engn, Harbin 150080, Peoples R China 2.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, 72 Wenhua Rd, Shenyang 110016, Liaoning, Peoples R China 3.Northwestern Polytech Univ, State Key Lab Solidificat Proc, Xian 710072, Peoples R China 4.Harbin Univ Sci & Technol, Sch Sci, Harbin 150080, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tian, Xiaohua,Zhao, Qiu,Zhang, Kun,et al. Accelerated design for elastocaloric performance in NiTi-based alloys through machine learning[J]. JOURNAL OF APPLIED PHYSICS,2022,131(1):10. |
| APA | Tian, Xiaohua.,Zhao, Qiu.,Zhang, Kun.,Li, Hongxing.,Han, Binglun.,...&Tan, Changlong.(2022).Accelerated design for elastocaloric performance in NiTi-based alloys through machine learning.JOURNAL OF APPLIED PHYSICS,131(1),10. |
| MLA | Tian, Xiaohua,et al."Accelerated design for elastocaloric performance in NiTi-based alloys through machine learning".JOURNAL OF APPLIED PHYSICS 131.1(2022):10. |
入库方式： OAI收割
来源：金属研究所
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