Prediction of glass forming ability in amorphous alloys based on different machine learning algorithms

Abstract In this work, we adopted four machine learning (ML) models, i.e., random forest (RF), K nearest neighbor (KNN), gradient boosted decision trees (GBDTs) and eXtreme gradient boosting (XGBoost) to predict the glass forming ability (GFA) of amorphous alloys using the dataset of Deng. The critical casting diameter (Dmax) of these alloys represents their GFA. The correlation coefficient (R) and root mean square error (RMSE) of the RF, KNN, GBDTs as well as XGBoost models are 0.75 and 3.29, 0.734 and 3.431, 0.724 and 3.474, and 0.755 and 3.277, respectively. Based on 10-fold cross-validation, it is found that the XGBoost model exhibits the highest predictive performance than the other above-mentioned three ML models and twelve previously reported criteria. Our results imply that machine learning method is very powerful and efficient, and has great potential for designing new amorphous alloys with desired GFA.