Machine learning approach to predict new multiphase high entropy alloys

High entropy alloys with multi-principal elements have interested the research community due to the promising properties and tunable microstructure. In the current study, the multiphase alloy system with a mixture of solid solution and intermetallic (SS + IM) was predicted using a machine learning approach with a data set of 636 alloys. The Algorithms used are Logistic Regression, Decision Tree, Support Vector Machine (SVM) classifier, Random Forest, Gradient Boosting Classifier, and Artificial Neural Network (ANN). ANN has shown the best accuracy of more than 80% for the test data. The new alloys were prepared and characterized to verify the prediction and it is found that ANN is having more accurate prediction in the studied alloy system. Statistical analysis of the established data set reveals an overlapping boundary between the design parameters that hinders the successful prediction. Experimental data confirms the formation of new multiphase alloys. ? 2021 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Automated summary

The study used machine learning to predict a multiphase alloy system SS + IM with high entropy alloys, with Artificial Neural Network (ANN) showing the highest accuracy. Experimental verification confirmed the accuracy of ANN predictions in the alloy system, but also revealed overlapping design parameters hindering successful prediction.