A New Phase Classifier with an Optimized Feature Set in ML-Based Phase Prediction of High-Entropy Alloys
Yifan Zhang,
Wei Ren,
Weili Wang
et al.
Abstract:The phases of high-entropy alloys (HEAs) are closely related to their properties. However, phase prediction bears a significant challenge due to the extensive search space and complex formation mechanisms of HEAs. This study demonstrates a precise and timely methodology for predicting alloy phases. It first developed a machine learning classifier using 145 features and a dataset with 1009 samples to differentiate the four types of alloy phases. Feature selection was performed on the feature set using an Embedd… Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.