Wenjiang Huang

Civil, environmental and sustainable engineering

Hometown: Wenzhou, Zhejiang, China

Graduation date: Spring 2019

Energy icon, disabled. An orange lightning bolt.

MORE | Spring 2019

ML Phase Prediction of High-Entropy Alloys

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

 

Mentor:

QR code for the current page

It’s hip to be square.

Students presenting projects at the Fulton Forge Student Research Expo are encouraged to download this personal QR code and include it within your poster. This allows expo attendees to explore more about your project and about you in the future. 

Right click the image to save it to your computer.