New methods for prediction of elastic constants based on density functional theory combined with machine learning

doi:10.1016/j.commatsci.2017.06.015

HFCAS OpenIR > 中科院固体物理研究所

	New methods for prediction of elastic constants based on density functional theory combined with machine learning
	Wang, Juan 1,2; Yang, Xiaoyu 1,2; Zeng, Zhi 3; Zhang, Xiaoli3 ; Zhao, Xushan 1; Wang, Zongguo 1
	2017-10-01
发表期刊	COMPUTATIONAL MATERIALS SCIENCE
摘要	Elastic constants play critical roles in researching mechanical properties, but they are usually difficult to be measured. While density functional theory (DFT) calculations provide a reliable method to meet this challenge, the results contain inherent errors caused by various approximations. The data-driven approach of machine learning also laid a foundation for predicting material properties. In order to increase the accuracy of theoretical calculations results, in this paper we investigate using machine learning methods to both correct the elastic constants by DFT calculation, and to directly predict elastic constants. The single-hidden layer feedforward neural network trained by back propagation algorithm (SLFN), general regression neural network (GRNN) and support vector machine for regression (SVR) techniques are employed to build regression models to correct the elastic constants by DFT calculation for metal or metallic binary alloys. We also build regression models to predict the elastic constants of metallic binary alloys with cubic crystal system rather than using DFT calculations. It has been demonstrated that the elastic constants corrected by regression models has higher accuracy than those calculated by DFT, and the elastic constants of binary alloys directly predicted by model using the outperformed SLFN technique is prospective. (C) 2017 Elsevier B.V. All rights reserved.
文章类型	Article
关键词	Prediction Of Elastic Constants Materials Informatics Dft Calculation Neural Network General Regression Neural Network Support Vector Regression
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.commatsci.2017.06.015
关键词[WOS]	TOTAL-ENERGY CALCULATIONS ; WAVE BASIS-SET ; SINGLE-CRYSTALS ; INTERMETALLIC COMPOUNDS ; TEMPERATURE-DEPENDENCE ; ROOM-TEMPERATURE ; MARTENSITIC-TRANSFORMATION ; STIFFNESS COEFFICIENTS ; PRESSURE-DEPENDENCE ; LATTICE-DYNAMICS
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China (NSFC)(11534012 ; National Natural Science Foundation of China (NSFC)(11534012 ; National Natural Science Foundation of China (NSFC)(11534012 ; National Natural Science Foundation of China (NSFC)(11534012 ; National Natural Science Foundation of China (NSFC)(11534012 ; National Natural Science Foundation of China (NSFC)(11534012 ; National Natural Science Foundation of China (NSFC)(11534012 ; National Natural Science Foundation of China (NSFC)(11534012 ; 61472394) ; 61472394) ; 61472394) ; 61472394) ; 61472394) ; 61472394) ; 61472394) ; 61472394)
WOS研究方向	Materials Science
WOS类目	Materials Science, Multidisciplinary
WOS记录号	WOS:000410617000017
引用统计	被引频次：17[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/334002/33667
专题	中科院固体物理研究所
作者单位	1.Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Solid State Phys, Hefei 230031, Anhui, Peoples R China
推荐引用方式 GB/T 7714	Wang, Juan,Yang, Xiaoyu,Zeng, Zhi,et al. New methods for prediction of elastic constants based on density functional theory combined with machine learning[J]. COMPUTATIONAL MATERIALS SCIENCE,2017,138:135-148.
APA	Wang, Juan,Yang, Xiaoyu,Zeng, Zhi,Zhang, Xiaoli,Zhao, Xushan,&Wang, Zongguo.(2017).New methods for prediction of elastic constants based on density functional theory combined with machine learning.COMPUTATIONAL MATERIALS SCIENCE,138,135-148.
MLA	Wang, Juan,et al."New methods for prediction of elastic constants based on density functional theory combined with machine learning".COMPUTATIONAL MATERIALS SCIENCE 138(2017):135-148.