Prediction of glass-forming ability based on multi-model fusion

At present, it is difficult to find metals with good glass-forming ability (GFA), seriously hindering the industrial application of metallic glasses (MGs). Therefore, it is urgent to develop a regression model that provides a prompt prediction for the critical diameter ( D_max) of alloys. Most established models rely heavily on elaborate feature descriptors and single algorithms, which may limit the predictive accuracy. In this work, the Stacking fusion is adopted to construct a more efficient model (Stack-Dmax). To avoid domain knowledge involved in feature engineering, only the alloy composition is treated as the input. Experiments revealed that compared to 6 individual models, the Stack-Dmax model is more robust and accurate regarding performance; it achieves improvement up to 41.61 %, 54.76 % and 35.67 % respectively in RMSE, R² and MAE. The Stack-Dmax model is also better than the five most recently reported models based on RF, XGB, and CNN. In practical applications, our model exhibits potential for designing new La- and Mg-based MGs. These findings suggest that the Stacking approach provides an effective GFA-prediction model only based on the composition of alloys.