Development of machine learning based models for design of high entropy alloys

High-entropy alloys (HEAs) can have superior properties due to the intermetallic (IM) or solid solution (SS) phase formation. In this work, machine learning (ML) based models have been implemented to categorise and estimate the phase prediction in HEAs with the objective of appreciably enhancing the model accuracy. Various features, VEC, delta r, Delta chi, lambda, ohm, Delta S, Delta H, and T-melt, are considered. With correlation matrix, enthalpy is observed to be the least significant feature. These datasets were used as inputs to four various ML algorithms, where all these models were optimised by hyper parameter tuning. The Algorithms implemented are: Support Vector Machine (SVM), Logistic Regression, Decision Tree, Random Forest, Artificial Neural Network (ANN) and Gradient Boosting algorithm. Gradient Boosting has demonstrated the best performance of more than 90% accuracy for the given data. It is established that Gradient Boosting predictions are found to be in good match with experimental data.