Prediction of vacancy formation energies at tungsten grain boundaries from local structure via machine learning method

doi:10.1016/j.jnucmat.2021.153412

HFCAS OpenIR

	Prediction of vacancy formation energies at tungsten grain boundaries from local structure via machine learning method
	Wang, Yuxuan 1,2; Li, Xiaolin 1,2; Li, Xiangyan1 ; Zhang, Yuxiang 1,2; Zhang, Yange 1; Xu, Yichun1 ; Lei, Yawei 1; Liu, C. S.1; Wu, Xuebang1
	2022-02-01
发表期刊	JOURNAL OF NUCLEAR MATERIALS
ISSN	0022-3115
通讯作者	Li, Xiangyan(xiangyanli@issp.ac.cn) ; Wu, Xuebang(xbwu@issp.ac.cn)
摘要	Grain boundary (GB) plays a crucial role in the mechanical properties and irradiation resistance of nuclear materials. It is thus essential to understand and predict the defect properties near GBs. Here, we present a framework for predicting vacancy formation energy (E-V(f) ) near GBs in tungsten (W) by machine learning (ML) technique. The E-V(f) values of 4496 atomic sites near 46 types of [001] symmetry tilt GB (STGB) in W are calculated as database and eight appropriate variables are selected to characterizing the surrounding atomic configuration and location of atomic sites. Via the support vector machine with the radial basis kernel function (RBF-SVM), the good predicted results of cross validation (CV) and generalized verification prove the suitability and effectiveness of the selected variables and RBF-SVM method. Beside, due to their big differences in dislocation arrangement and atomic configuration, the STGBs need to be divided into three types, high angle, low angle-I and low angle-II STGBs, for adopting the Separate CV, and their predicted accuracies were found to have big improvements. Because the present method adopts geometrical factors, such as spatial size characteristic, density and location, as descriptors for the ML analysis, it is robust and general to other materials such as alpha-Fe, and beneficial to predict and understand the vacancy formation near interfaces. (C) 2021 Elsevier B.V. All rights reserved.
关键词	Vacancy formation energy Machine learning Tungsten Symmetry tilt grain boundary Support vector machine Cross validation
DOI	10.1016/j.jnucmat.2021.153412
关键词[WOS]	MOLECULAR-DYNAMICS ; RADIATION-DAMAGE ; POINT-DEFECTS ; FISSION ; METALS
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFE0302400] ; National Key Research and Development Program of China[2017YFA0402800] ; National Natural Science Foundation of China[52171084] ; National Natural Science Foundation of China[11735015] ; National Natural Science Foundation of China[51871207] ; National Natural Science Foundation of China[U1832206] ; Anhui Provincial Natural Science Foundation[1908085J17]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Anhui Provincial Natural Science Foundation
WOS研究方向	Materials Science ; Nuclear Science & Technology
WOS类目	Materials Science, Multidisciplinary ; Nuclear Science & Technology
WOS记录号	WOS:000799085700003
出版者	ELSEVIER
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/131128
专题	中国科学院合肥物质科学研究院
通讯作者	Li, Xiangyan; Wu, Xuebang
作者单位	1.Chinese Acad Sci, Inst Solid State Phys, Key Lab Mat Phys, HFIPS, Hefei 230031, Peoples R China 2.Univ Sci & Technol China, Hefei 230026, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yuxuan,Li, Xiaolin,Li, Xiangyan,et al. Prediction of vacancy formation energies at tungsten grain boundaries from local structure via machine learning method[J]. JOURNAL OF NUCLEAR MATERIALS,2022,559.
APA	Wang, Yuxuan.,Li, Xiaolin.,Li, Xiangyan.,Zhang, Yuxiang.,Zhang, Yange.,...&Wu, Xuebang.(2022).Prediction of vacancy formation energies at tungsten grain boundaries from local structure via machine learning method.JOURNAL OF NUCLEAR MATERIALS,559.
MLA	Wang, Yuxuan,et al."Prediction of vacancy formation energies at tungsten grain boundaries from local structure via machine learning method".JOURNAL OF NUCLEAR MATERIALS 559(2022).