Predicting the onset temperature (Tg) of GexSe1-x glass transition: a feature selection based two-stage support vector regression method

• 来源期刊: 科学通报（英文版）
• 年，卷（期）: 2019,（16）
• 页码范围: 1195-1203
• 页数: 9页
• 语种: 英文

摘要

Despite the usage of both experimental and topological methods,realizing a rapid and accurate measurement of the onset temperature (Tg) of GexSe1-x glass transition remains an open challenge.In this paper,a predictive model for the Tg in GexSe1-x glass system is presented by a machine learning method named feature selection based two-stage support vector regression (FSTS-SVR).Firstly,Pearson correlation coefficient (PCC) is used to select features highly correlated with Tg from the candidate features of GexSe1-x glass system.Secondly,in order to simulate the two-stage characteristic of Tg which is caused by structural variation with a turning point at x =0.33 via the structural analysis,SVR is utilized to build predictive models for two stages separately and then the two achieved models are synthesized using a minimum error based model for Tg prediction.Compared with the topological and other methods based on SVR,the FSTS-SVR gives the highest predictive accuracy with the root mean square error (RMSE) and mean absolute percentage error (MAPE) of 10.64 K and 2.38％,respectively.This method is also expected to be more efficient for the prediction of Tg of other glass systems with the multi-stage characteristic.