Prediction of High-Entropy Alloy Phases Using Soft Computing Techniques

High-entropy alloys (HEAs) have excellent properties that are being explored for potential applications in many engineering fields. Their excellent properties strongly depend on their phases. The vastness of alloy compositions that can be synthesized makes it extremely challenging to experimentally investigate all the possible HEA types. To mitigate these challenges, more efficient and systematic computational techniques can be applied to the existing experimental data to accelerate HEA design and discovery. Therefore, this study developed three soft computing classification models based on artificial neural network, k-nearest neighbor (kNN), and support vector machine (SVM) to classify solid solution, amorphous and intermetallic phases in HEAs. Empirical studies showed that hyperparameter optimization improved classification accuracies of the classifiers with kNN (92%) outperforming ANN (86%) and SVM (90%) using all five predictive features. Feature selection did not improve the classification accuracy of any of the model. This studied demonstrated the importance of applying soft computing techniques and hyperparameter optimization for enhancing the classification accuracies of models to predict the phases in HEAs.