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Screening for shape memory alloys with narrow thermal hysteresis using combined XGBoost and DFT calculation
Tian, Xiaohua1; Zhou, Liwen1; Zhang, Kun2,3; Zhao, Qiu1; Li, Hongxing2; Shi, Dingding1; Ma, Tianyou2; Wang, Cheng2; Wen, Qinlong4; Tan, Changlong2
Corresponding AuthorZhang, Kun(kunzhang@hrbust.edu.cn) ; Tan, Changlong(changlongtan@hrbust.edu.cn)
2022-08-01
Source PublicationCOMPUTATIONAL MATERIALS SCIENCE
ISSN0927-0256
Volume211Pages:7
AbstractShape 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.
KeywordThermal hysteresis NiTi shape memory alloys Machine learning XGBoost First-principles calculations
Funding OrganizationNational Natural Science Foundation of China ; China Postdoctoral Science Foundation
DOI10.1016/j.commatsci.2022.111519
Indexed BySCI
Language英语
Funding ProjectNational 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 Research AreaMaterials Science
WOS SubjectMaterials Science, Multidisciplinary
WOS IDWOS:000807750900007
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/174351
Collection中国科学院金属研究所
Corresponding AuthorZhang, Kun; Tan, Changlong
Affiliation1.Harbin Univ Sci & Technol, Sch Elect & Elect Engn, Harbin 150080, Peoples R China
2.Harbin Univ Sci & Technol, Sch Sci, Harbin 150080, Peoples R China
3.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang 110016, Peoples R China
4.Northwestern Polytech Univ, State Key Lab Solidificat Proc, Xi'an 710072, Peoples R China
Recommended Citation
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.
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