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

•Four machine learning (KNN, RF, GBDT and XGBoost) models for predicting the glass forming ability (GFA) of amorphous alloys are developed.•The 10-fold cross-validation method divides the data set into validation set and training set, and makes full use of each data to prevent overfitting.•Grid-search was used to determine the hyperparameters, and cross-validation was used to determine this model's rationality.•The XGBoost model provides the highest accuracy in the glass forming ability prediction.•The machine learning based models show good predictive and generalization ability. 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.