A simple recipe for designing multicomponent ultra-high temperature ceramic classes by using structure maps coupled with machine learning

Successful synthesis of novel high entropy ceramic (HEC) for ultra-high temperature application classes, namely, borides, carbides, and nitrides, has been experiencing a bottleneck in having a suitable design and successful synthesis strategy. Producing high-entropy ultra-high-temperature ceramics from their oxides offers a major processing benefit, while employing a design approach using machine learning enhances the efficiency of the formation of single-phase HECs. In this regard, we propose a generalized strategy to generate a semi-synthetic database for each of these classes using literature data and atomic environment mapping-based structure plots, which can further be used to build machine learning models. The imbalance of the dataset was addressed using adaptive synthetic sampling and the edited nearest neighbors technique. The trained models are able to accurately predict over 90% of the single-phase chemistry for each of the classes. Furthermore, a few compositions representing these classes were successfully synthesized from the corresponding oxide mixture to validate the effectiveness of the proposed strategy.