Screening for shape memory alloys with narrow thermal hysteresis using combined XGBoost and DFT calculation
文献类型：期刊论文
| 作者 | Tian, Xiaohua4; Zhou, Liwen4; Zhang, Kun2,3; Zhao, Qiu4; Li, Hongxing3; Shi, Dingding4; Ma, Tianyou3; Wang, Cheng3; Wen, Qinlong1; Tan, Changlong3 |
| 刊名 | COMPUTATIONAL MATERIALS SCIENCE
 |
| 出版日期 | 2022-08-01 |
| 卷号 | 211页码:7 |
| ISSN号 | 0927-0256 |
| 关键词 | Thermal hysteresis NiTi shape memory alloys Machine learning XGBoost First-principles calculations |
| DOI | 10.1016/j.commatsci.2022.111519 |
| 通讯作者 | Zhang, Kun(kunzhang@hrbust.edu.cn) ; Tan, Changlong(changlongtan@hrbust.edu.cn) |
| 英文摘要 | Shape memory alloys (SMAs) are desirable candidates for elastocaloric effect materials, but they all suffer from large thermal hysteresis (T-hys). This study analyzes multicomponent TiNi-based SMAs dataset by machine learning (ML) to explore new SMAs with narrow T-hys. The second-largest eigenvalue lambda(2) of the stretch trans-formation matrix U is added to the original dataset to guide the ML process as a feature. Firstly, lambda(2) is obtained by first-principles calculations combined with ML. XGBoost Regressor (XGBR) combined with Leave-One-Out Cross -Validation (LOO-CV) is selected from four algorithms for modeling with the highest coefficient of determination R-2 of 0.87. The introduction of lambda(2) improves the performance of the model. The dataset is divided into 15 groups based on different doping elements (such as Hf, Cu, Zr, etc.), among which TiNiCu is the most predictive component with the R-2 of 0.89. Over 500 TiNiCu components are randomly generated and predicted T-hys. Based on the contour maps created from the prediction results, it is found that T-hys is likely to decrease with the in-crease of Cu doping in general, and minimum T-hys occurs when the Cu is about 15 at. %, which is consistent with the existing experimental results. Eventually, a potential Thys minimum (1.2 K) region of TixNiyCuz (58.3%<= x <= 58.5%, 26.5% <= y <= 27%, 14.8% <= z <= 15.3%, x +y +z =100%) SMA composition is predicted. Our study not only provides a potential selection of narrow T-hys TiNi-based SMAs but also indicates combining of XGBoost and DFT calculation is an effective strategy for materials design. |
| 资助项目 | National Natural Science Foundation of China[51971085] ; National Natural Science Foundation of China[51871083] ; National Natural Science Foundation of China[52001101] ; China Postdoctoral Science Foundation[2021M693229] |
| WOS研究方向 | Materials Science |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000807750900007 |
| 资助机构 | National Natural Science Foundation of China ; China Postdoctoral Science Foundation |
| 源URL | [http://ir.imr.ac.cn/handle/321006/174350]  |
| 专题 | 金属研究所_中国科学院金属研究所 |
| 通讯作者 | Zhang, Kun; Tan, Changlong |
| 作者单位 | 1.Northwestern Polytech Univ, State Key Lab Solidificat Proc, Xi'an 710072, Peoples R China 2.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang 110016, Peoples R China 3.Harbin Univ Sci & Technol, Sch Sci, Harbin 150080, Peoples R China 4.Harbin Univ Sci & Technol, Sch Elect & Elect Engn, Harbin 150080, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tian, Xiaohua,Zhou, Liwen,Zhang, Kun,et al. Screening for shape memory alloys with narrow thermal hysteresis using combined XGBoost and DFT calculation[J]. COMPUTATIONAL MATERIALS SCIENCE,2022,211:7. |
| APA | Tian, Xiaohua.,Zhou, Liwen.,Zhang, Kun.,Zhao, Qiu.,Li, Hongxing.,...&Tan, Changlong.(2022).Screening for shape memory alloys with narrow thermal hysteresis using combined XGBoost and DFT calculation.COMPUTATIONAL MATERIALS SCIENCE,211,7. |
| MLA | Tian, Xiaohua,et al."Screening for shape memory alloys with narrow thermal hysteresis using combined XGBoost and DFT calculation".COMPUTATIONAL MATERIALS SCIENCE 211(2022):7. |
入库方式： OAI收割
来源：金属研究所
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