Accelerated design for elastocaloric performance in NiTi-based alloys through machine learning

doi:10.1063/5.0068290

IMR OpenIR

	Accelerated design for elastocaloric performance in NiTi-based alloys through machine learning
	Tian, Xiaohua 1; Zhao, Qiu 1; Zhang, Kun 2,3; Li, Hongxing 2; Han, Binglun 2; Shi, Dingding 1; Zhou, Liwen 1; Ma, Tianyou 2; Wang, Cheng 2; Wen, Qinlong 4; Tan, Changlong 2
通讯作者	Zhang, Kun(kunzhang@hrbust.edu.cn)
	2022-01-07
发表期刊	JOURNAL OF APPLIED PHYSICS
ISSN	0021-8979
卷号	131 期号:1 页码:10
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.
资助者	National Natural Science Foundation of China (NNSFC) ; China Postdoctoral Science Foundation
DOI	10.1063/5.0068290
收录类别	SCI
语种	英语
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
WOS类目	Physics, Applied
WOS记录号	WOS:000744570400010
出版者	AIP Publishing
引用统计	被引频次：12[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/321006/173602
专题	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.Harbin Univ Sci & Technol, Sch Sci, Harbin 150080, Peoples R China 3.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, 72 Wenhua Rd, Shenyang 110016, Liaoning, Peoples R China 4.Northwestern Polytech Univ, State Key Lab Solidificat Proc, Xian 710072, 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.