Using support vector machine for materials design

doi:10.1007/s40436-013-0025-2

Advances in Manufacturing

Using support vector machine for materials design

Wen-Cong Lu • Xiao-Bo Ji • Min-Jie Li •Liang Liu • Bao-Hua Yue • Liang-Miao Zhang

Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, People’s Republic of China

收稿日期:2013-04-20 修回日期:2013-05-05 出版日期:2013-06-25 发布日期:2013-05-07
通讯作者: e-mail: wclu@shu.edu.cn
作者简介:e-mail: wclu@shu.edu.cn
基金资助:
The National Natural Science Foundation of China (Grant No. 21273145)

Using support vector machine for materials design

Wen-Cong Lu • Xiao-Bo Ji • Min-Jie Li •Liang Liu • Bao-Hua Yue • Liang-Miao Zhang

Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, People’s Republic of China

Received:2013-04-20 Revised:2013-05-05 Online:2013-06-25 Published:2013-05-07
Contact: e-mail: wclu@shu.edu.cn
About author:e-mail: wclu@shu.edu.cn
Supported by:
The National Natural Science Foundation of China (Grant No. 21273145)

摘要/Abstract

摘要： Materials design is the most important and fundamental work on the background of materials genome initiative for global competitiveness proposed by the
National Science and Technology Council of America. As far as the methodologies of materials design, besides the thermodynamic and kinetic methods combing databases,both deductive approaches so-called the first principle methods and inductive approaches based on data mining methods are gaining great progress because of their successful applications in materials design. In this paper, support vector machine (SVM), including support vector classification (SVC) and support vector regression (SVR) based on the statistical learning theory (SLT) proposed by Vapnik, is introduced as a relatively new data mining method to meet the different tasks of materials design in our lab. The advantage of using SVM for materials design is discussed based on the applications in the formability of perovskite or BaNiO3 structure, the prediction of energy gaps of binary compounds, the prediction of sintered cold
modulus of sialon-corundum castable, the optimization of electric resistances of VPTC semiconductors and the thickness control of In2O3 semiconductor film preparation. The results presented indicate that SVM is an effective modeling tool for the small sizes of sample sets with great potential applications in materials design.

关键词: Support vector machine, , Materials genome initiative , Materials design, , Data mining , Quantitative

Abstract: Materials design is the most important and fundamental work on the background of materials genome initiative for global competitiveness proposed by the
National Science and Technology Council of America. As far as the methodologies of materials design, besides the thermodynamic and kinetic methods combing databases,both deductive approaches so-called the first principle methods and inductive approaches based on data mining methods are gaining great progress because of their successful applications in materials design. In this paper, support vector machine (SVM), including support vector classification (SVC) and support vector regression (SVR) based on the statistical learning theory (SLT) proposed by Vapnik, is introduced as a relatively new data mining method to meet the different tasks of materials design in our lab. The advantage of using SVM for materials design is discussed based on the applications in the formability of perovskite or BaNiO3 structure, the prediction of energy gaps of binary compounds, the prediction of sintered cold
modulus of sialon-corundum castable, the optimization of electric resistances of VPTC semiconductors and the thickness control of In2O3 semiconductor film preparation. The results presented indicate that SVM is an effective modeling tool for the small sizes of sample sets with great potential applications in materials design.

Key words: Support vector machine, , Materials genome initiative , Materials design, , Data mining , Quantitative

Wen-Cong Lu, Xiao-Bo Ji,Min-Jie Li,Liang Liu,Bao-Hua Yue,Liang-Miao Zhan. Using support vector machine for materials design[J]. Advances in Manufacturing, doi: 10.1007/s40436-013-0025-2.

Wen-Cong Lu,Xiao-Bo Ji,Min-Jie Li,Liang Liu,Bao-Hua Yue,Liang-Miao Zhang. Using support vector machine for materials design[J]. Advances in Manufacturing, doi: 10.1007/s40436-013-0025-2.

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Using support vector machine for materials design

Using support vector machine for materials design

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