CardiGAN: A generative adversarial network model for design and discovery of multi principal element alloys

Abstract

• A generative model was developed for predicting and designing MPEAs. • The model presented herein, termed cardiGAN, was demonstrated as capable of functioning with high accuracy in performance. • The cardiGAN model may be utilized to generate large numbers of candidate MPEAs with minimal computational burden, generating novel compositions. Multi-principal element alloys (MPEAs), inclusive of high entropy alloys (HEAs), continue to attract significant research attention owing to their potentially desirable properties. Although MPEAs remain under extensive research, traditional (i.e. empirical) alloy production and testing are both costly and time-consuming, partly due to the inefficiency of the early discovery process which involves experiments on a large number of alloy compositions. It is intuitive to apply machine learning in the discovery of this novel class of materials, of which only a small number of potential alloys have been probed to date. In this work, a proof-of-concept is proposed, combining generative adversarial networks (GANs) with discriminative neural networks (NNs), to accelerate the exploration of novel MPEAs. By applying the GAN model herein, it was possible to directly generate novel compositions for MPEAs, and to predict their phases. To verify the predictability of the model, alloys designed by the model are presented and a candidate produced – as validation. This suggests that the model herein offers an approach that can significantly enhance the capacity and efficiency of development of novel MPEAs.