Machine learning based phase prediction and powder metallurgy assisted experimental validation of medium entropy compositionally complex alloys

Abstract

Medium entropy alloys (MEAs) are a subset of compositionally complex alloys whose mixing entropy lies between R and 1.5 R where R is the universal gas constant. The properties of MEAs largely depend on the phases present in the alloy such as solid solution (SS), solid solution + intermetallic (SS + IM) and amorphous (AM). Hence, the correct prediction of phases can enable the efficient selection of material compositions with anticipated properties. In this paper, three machine learning (ML) algorithms viz. k nearest neighbors (KNNs), artificial neural network (ANN), and random forest (RF) were employed for the ternary phase classification problem. An MEA dataset was constructed by utilizing all reported MEAs till February 2023 to the best of authors’ knowledge. The study implied that the use of only three features (mixing enthalpy, atomic size mismatch, and a strain energy related parameter) were sufficient for the phase prediction in MEAs. Among the three ML algorithms, ANN had the highest macro averaged F1 score (86.7%) and accuracy (87.3%) in predicting the phases in MEAs, while RF has the lowest macro F1 score (84.67%) and accuracy (84.8%). However, for phase prediction between single phase SS and multi-phase SS (binary classification), distance-based algorithm (KNN) was found to be suitable. The prediction performance of ML model over a completely unseen data was assessed in the case study section. The experimentally determined phase details of three new MEA compositions fabricated by powder metallurgy route was also included in the unseen dataset. The SS and AM phases were correctly labeled nine times out of eleven instances by using ANN model. However, the model prediction for SS + IM phase was found to be less reliable (three out of five correct) owing to its relatively poor F1 score.