Automatic Featurization Aided Data-Driven Method for Estimating the Presence of Intermetallic Phase in Multi-Principal Element Alloys

Abstract

Multi-principal element alloys (MPEAs) are characterized by a high-dimensional materials design space, and data-driven models can be considered as the best tools to describe the structure– property relationship in this class of materials. Predicting the prevalence of an intermetallic (IM) phase in a high-entropy alloy (HEA) regime of MPEAs has become a very important research direction recently. In this work, Automatic Featurization capability has been deployed computationally to extract composition and property features from the datasets of MPEAs. Data visualization has been performed, and through principal component analysis, the relative impacts of the input features on the two principal components have been specified. Artificial neural network is then trained upon the set of compostion, property and phase information features. A GUI interface is subsequently developed on top of the prediction model to enable the user-friendly computer environment for detection of the IM phase in a compositionally complex alloy.