Predicting Oxidation Behavior of Multi-Principal Element Alloys by Machine Learning Methods

In this work, we operate on a small dataset available from the technical literature to predict the oxidation-induced mass change at 1000 °C of thousands of new alloy compositions using “Tree-based Pipeline Optimization Tool” , an automated machine learning (ML) method. The ML pipeline we develop is trained on the log10 of the mass change per unit area. This yields a mean absolute error of 0.34 on the test set’s values, which span 3.5 decades. With additional insights from thermodynamic simulations, a set of seven alloys is selected, manufactured, and characterized. Of these, the oxidation behavior of five alloys is well-predicted by the ML-based model, while results for two alloys show orders of magnitude deviations from predictions. The results show that ML-based methods can be useful for predicting composition-dependent oxidation behavior, despite its many complexities.