fig7

Predicting stacking fault energy in austenitic stainless steels via physical metallurgy-based machine learning approaches

Figure 7. Comparison of model predictions using different methods of introducing DF. (A) R2 and MAE for training set, testing set, and validation set; (B) distribution of predicted and measured values of SFE for austenitic stainless steels. DF: Driving force; R2: the squared correlation coefficient; MAE: mean absolute error; SFE: stacking fault energy.

Journal of Materials Informatics
ISSN 2770-372X (Online)
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