  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 is 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
has 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.
