Predicting New Single/Multiphase-Structure High-Entropy Alloys Using a Pattern Recognition Network

Abstract

Machine learning methods were employed to predict the phase structures of high-entropy alloys (HEAs). These alloys were classified into four categories: bcc (body-centered cubic), fcc (face-centered cubic), bcc+fcc (body-centered cubic and face-centered cubic) and others (containing intermetallic compounds and other structural alloys). The utilized algorithm was a Pattern Recognition Network (PRN) utilizing cross-entropy as the loss function, enabling the prediction of HEAs’ phase formation probability. The PRN algorithm demonstrated an accuracy exceeding 87% based on the test data. The PRN algorithm successfully predicted the transformation from fcc to fcc+bcc and subsequently to a bcc structure with the increase in Al content in AlxCoCu6Ni6Fe6 and AlxCoCrCuNiFe HEAs. In addition, AlxCoCu6Ni6Fe6 (x = 1, 3, 6, 9) HEAs were prepared using a vacuum arc furnace, and the microstructure of the as-cast alloy was tested by means of XRD, SEM, and EBSD, confirming the high consistency between the predicted and observed phase structures. This study showcases the efficacy of the PRN algorithm in predicting both single- and multiphase-structure high-entropy alloys, offering valuable insights into alloy design and development.