Machine learning guided phase formation prediction of high entropy alloys

High entropy alloys (HEAs) have attracted intensive attention in recent years, because of their numerous structures and unusual properties. Structure prediction plays a key role in HEAs development due to the strong link between structures and properties. Thus, a new approach to rapidly predict HEAs phase formation with high accuracy has to be proposed. Here, we built a HEAs phase selection strategy based on a large as-cast dataset containing 2043 alloys data. Our dataset consists of HEAs, binary and ternary alloys. Our phase selection strategy is a combination of multi k-nearest neighbor learners with an ensemble learning method. Two new thermody-namic parameters have been proposed to improve the machine learning model's predicting performance. Our strategy shows a surprisingly high predictability (test accuracy is 93%), and all the test accuracy values of prediction for each phase are above 97%, which means multi phases formation could be completely and detailed predicted via our phase selection strategy. Our strategy provides an alternative route of HEAs phase formation prediction that helps accelerate the development of HEAs.

Automated summary

High entropy alloys (HEAs) have garnered attention for their diverse structures and unique properties. Structure prediction plays a crucial role in HEAs development, and a new phase selection strategy has been proposed in this study to accurately predict phase formation, showing a high test accuracy of 93% and facilitating the development of HEAs.