Design high-entropy carbide ceramics from machine learning

High-entropy ceramics(HECs)have shown great application potential under demanding conditions,such as high stresses and temperatures.However,the immense phase space poses great challenges for the rational design of new high-performance HECs.In this work,we develop machine-learning(ML)models to discover high-entropy ceramic carbides(HECCs).Built upon attributes of HECCs and their constituent precursors,our ML models demonstrate a high prediction accuracy(0.982).Using the well-trained ML models,we evaluate the single-phase probability of 90 HECCs that are not experimentally reported so far.Several of these predictions are validated by our experiments.We further establish the phase diagrams for non-equiatomic HECCs spanning the whole composition space by which the single-phase regime can be easily identified.Our ML models can predict both equiatomic and non-equiatomic HECs based solely on the chemical descriptors of constituent transition-metal-carbide precursors,which paves the way for the high-throughput design of HECCs with superior properties.