Treffer: Prediction of supercooled liquid region of Fe-based metallic glasses by deep learning: Prediction of supercooled liquid region...

In this study, the supercooled liquid region (ΔT<subscript>x</subscript>) of Fe-based metallic glasses was predicted using a convolutional neural network (CNN), which is one of the popular deep-learning models. ΔT<subscript>x</subscript> is defined as T<subscript>x</subscript>-T<subscript>g</subscript>, where T<subscript>x</subscript> is the onset crystallization temperature and T<subscript>g</subscript> is the glass temperature. ΔT<subscript>x</subscript> can be considered a characteristic indicator of thermally stable bulk metallic glass. CNN-guided supervised learning was performed using only the compositional features without structural information and physics-informed features. The chemical formula in the material dataset was used to generate 140 features with the Python libraries 'Pymatgen' and 'Matminer'. The regression performance to predict ΔT<subscript>x</subscript> was evaluated using the determination coefficient (R² score) and root-mean-squared errors (RMSE). To establish the CNN network, various reconstructed shapes of 140 compositional features were employed as the input vectors, such as 10 × 14, 14 × 10, 7 × 20, and 20 × 7. Among them, the 14 × 10 compositional feature vectors exhibited the best prediction performance for ΔT<subscript>x</subscript> with an R² score of 0.924 and RMSE of 7.7 K. As a case study, the trained CNN model was applied to predict ΔT<subscript>x</subscript> of recently developed Fe-based metallic glasses, corresponding to the unseen dataset. The predictions for the unseen dataset were found to be consistent with the reported experimental results for ΔT<subscript>x</subscript>, indicating that the composition-based CNN model opens a new path to searching for promising Fe-based metallic glasses with high glass-forming ability. [ABSTRACT FROM AUTHOR]

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