College - Author 1

College of Engineering

Department - Author 1

Materials Engineering Department

College - Author 2

College of Engineering

Department - Author 2

Materials Engineering Department

College - Author 3

College of Engineering

Department - Author 3

Materials Engineering Department


Dr. Mohsen Kivy, College of Engineering, Materials Engineering

Funding Source

The Spraque Family Foundation




High-entropy alloys (HEA) are a very new development in the field of metallurgical materials. They are made up of multiple principle atoms unlike traditional alloys, which contributes to their high configurational entropy. The microstructure and properties of HEAs are are not well predicted with the models developed for more common engineering alloys, and there is not enough data available on HEAs to fully represent the complex behavior of these alloys. To that end, we explore how the use of machine learning models can be used to model the complex, high dimensional behavior in the HEA composition space. Based on our review and experimentation, the use of a variational autoencoder will allow future research to lower the dimensionality (and therefore complexity) of the HEA composition space and allow for novel alloy generation based on one or more desired properties.




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