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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:

2017年03期

Page:

87-

Research Field:

计算机工程

Publishing date:

Info

Title:

Photocatalytic Activity Prediction of Rare Earth Doped TiO₂Based on Machine Learning Algorithm

Author(s):

Wu Xinghui¹; Wu Di²; Zhou Yuping¹; Shi Zaifeng²

(1.School of Computer Science and Technology,Hainan Normal University,Haikou 571158,China)(2.School of Chemistry and Chemical Engineering,Hainan Normal University,Haikou 571158,China)

Keywords:

rare earth doped TiO₂; data analysis; machine learning; photocatalytic activity prediction

PACS:

TP181

DOI:

10.3969/j.issn.1672-1292.2017.03.013

Abstract:

Machine learning is one of the hot points of the research in the area of artificial intelligence and pattern recognition,and its theories and methods have been widely used to resolve complex problems in science and engineering applications. In the chemical area,rare earth doped TiO₂ photocatalysts for improving photocatalytic activity has been massively studied and got many important results. However,the doping mechanism is not fully clear,even though the first-principle algorithm is applied to mechanism simulation,the calculation process is complicated and there are many inevitable errors. To solve the problem,it is expected that the simulation can be done without complicated chemical model,the prediction accuracy can be improved through a simply computer algorithm only based on element’s essential data as the prior knowledge. In the present paper,the linear regression,Gaussian process regression and support vector machines regression,k-nearest neighbor algorithm are used to predict the photocatalytic reaction rate constant of rare earth doped TiO₂. Then,experiments are used to verify the key factor analysis effectiveness based on stepwise multiple regression and advantages of the key factor analysis based on k-NN regression model. Results show that the pridiction performance is better with key factors obtained from stepwise regression analysis. Compared with other regression methods,it may be found the prediction performance of k-NN is the best.

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Last Update: 2017-09-30