Screening for shape memory alloys with narrow thermal hysteresis using combined XGBoost and DFT calculation

doi:10.1016/j.commatsci.2022.111519

IMR OpenIR

	Screening for shape memory alloys with narrow thermal hysteresis using combined XGBoost and DFT calculation
	Tian, Xiaohua 1; Zhou, Liwen 1; Zhang, Kun 2,3; Zhao, Qiu 1; Li, Hongxing 2; Shi, Dingding 1; Ma, Tianyou 2; Wang, Cheng 2; Wen, Qinlong 4; Tan, Changlong 2
通讯作者	Zhang, Kun(kunzhang@hrbust.edu.cn) ; Tan, Changlong(changlongtan@hrbust.edu.cn)
	2022-08-01
发表期刊	COMPUTATIONAL MATERIALS SCIENCE
ISSN	0927-0256
卷号	211 页码:7
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.
关键词	Thermal hysteresis NiTi shape memory alloys Machine learning XGBoost First-principles calculations
资助者	National Natural Science Foundation of China ; China Postdoctoral Science Foundation
DOI	10.1016/j.commatsci.2022.111519
收录类别	SCI
语种	英语
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
WOS类目	Materials Science, Multidisciplinary
WOS记录号	WOS:000807750900007
出版者	ELSEVIER
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/174351
专题	中国科学院金属研究所
通讯作者	Zhang, Kun; Tan, Changlong
作者单位	1.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
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.