MORE | Spring 2019

ML Phase Prediction of High-Entropy Alloys

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Researchers apply Machine learning (ML) algorithms to efficiently explore phase selection rules using a comprehensive experimental dataset consisting of 401 different HEAs including 174 SS, 54 IM, and 173 SS+IM phases. We adopt three different ML algorithms: K-nearest neighbors (KNN), support vector machine (SVM), and artificial neural network (ANN). The purpose of the work is to provide an alternative route of computational design of HEAs, which is also applicable to accelerate the discovery of other metal alloys for modern engineering applications


Student researcher

Wenjiang Huang

Civil, environmental and sustainable engineering

Hometown: Wenzhou, Zhejiang, China

Graduation date: Spring 2019