Machine learning approach for predicting the fracture toughness of Nb–Si based alloys

Machine learning based data analysis was used to quantitatively examine the effects of constituent elements on the room-temperature fracture toughness of Nb–Si based alloys. Correlation between the room-temperature fracture toughness and alloying elements was analyzed using the machine learning model developed in this study based on the collected Nb–Si based alloy data. In addition, using a reasonably trained data analysis approach, the accuracies of several machine learning algorithms were compared to determine the specific machine learning algorithm with showing the highest accuracy. The results of the correlation analysis between the input and output features coincided with widely accepted facts regarding the effects of alloying elements additions on the room-temperature fracture toughness of Nb–Si based alloys. It was confirmed that the random forest algorithm outperformed other three representative machine learning algorithms.