Predicting the onset temperature (T) of GeSe glass transition: a feature selection based two-stage support vector regression method.

Despite the usage of both experimental and topological methods, realizing a rapid and accurate measurement of the onset temperature (T) of GeSe glass transition remains an open challenge. In this paper, a predictive model for the T in GeSe 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 T from the candidate features of GeSe glass system. Secondly, in order to simulate the two-stage characteristic of T 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 T 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 T of other glass systems with the multi-stage characteristic.