Design of high-performance high-entropy nitride ceramics via machine learning-driven strategy

High-entropy nitride (HEN) ceramics have exhibited excellent mechanical properties in comparison to transition metal nitrides. However, the huge unexplored compositional space makes the trial-and-error experiments and first-principles calculations cost highly. In this work, we proposed a data augmentation generative adversarial network (DAGAN)-driven machine learning (ML) design strategy to predict the hardness, modulus and wear resistance of novel HEN compositions. Several feature selection algorithms were compared to select the optimal descriptor combination. To overcome the data shortage problem of HENs, we established a property-conditioned DAGAN and the accuracies of ML models were maximumly increased by up to 14.67%. Eight super-hard HEN systems with the hardness above 40 GPa were found among the compositional space, in which seven have yet to be experimentally synthesized. The intrinsic effects of chemical descriptors were further explored through ternary property diagrams, which provides an efficient guidance for the design of novel high-performance HENs.