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, Juan1,2; Yang, Xiaoyu1,2; Zeng, Zhi3; Zhang, Xiaoli3; Zhao, Xushan1; Wang, Zongguo1
2017-10-01
Source PublicationCOMPUTATIONAL MATERIALS SCIENCE
Volume138Pages:135-148
AbstractElastic 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.
SubtypeArticle
KeywordPrediction Of Elastic Constants Materials Informatics Dft Calculation Neural Network General Regression Neural Network Support Vector Regression
WOS HeadingsScience & Technology ; Technology
Funding OrganizationNational Natural Science Foundation of China (NSFC)(11534012 ; National Natural Science Foundation of China (NSFC)(11534012 ; 61472394) ; 61472394) ; National Natural Science Foundation of China (NSFC)(11534012 ; National Natural Science Foundation of China (NSFC)(11534012 ; 61472394) ; 61472394)
DOI10.1016/j.commatsci.2017.06.015
WOS KeywordTOTAL-ENERGY CALCULATIONS ; WAVE BASIS-SET ; SINGLE-CRYSTALS ; INTERMETALLIC COMPOUNDS ; TEMPERATURE-DEPENDENCE ; ROOM-TEMPERATURE ; MARTENSITIC-TRANSFORMATION ; STIFFNESS COEFFICIENTS ; PRESSURE-DEPENDENCE ; LATTICE-DYNAMICS
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China (NSFC)(11534012 ; National Natural Science Foundation of China (NSFC)(11534012 ; 61472394) ; 61472394) ; National Natural Science Foundation of China (NSFC)(11534012 ; National Natural Science Foundation of China (NSFC)(11534012 ; 61472394) ; 61472394)
WOS Research AreaMaterials Science
WOS SubjectMaterials Science, Multidisciplinary
WOS IDWOS:000410617000017
Citation statistics
Document Type期刊论文
Identifierhttp://ir.hfcas.ac.cn:8080/handle/334002/33667
Collection中科院固体物理研究所
Affiliation1.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
Recommended Citation
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.
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