Identifying inorganic solids for harsh environments via machine learning

Abstract

Developing multifunctional materials with superior mechanical properties, including high hardness and oxidation resistance, remains essential for aerospace, defense, and industrial applications. Machine learning offers a powerful, data-driven pathway for discovering new hard, oxidation-resistant materials for these uses, providing an efficient and scalable alternative to conventional materials discovery methods. Here, we present a pair of extreme gradient boosting (XGBoost) models, trained on compositional and structural descriptors. A Vickers hardness (H_V) model was developed using a curated dataset of 1225 while a model for predicting the oxidation temperature (T_p) was constructed using 348 compounds. The model was subsequently validated against a diverse dataset of 18 inorganic compounds, including borides, silicides, and intermetallics, with previously unmeasured oxidation temperatures. Integrating the updated structure-informed hardness model with the new oxidation model enabled the identification of multifunctional materials that simultaneously exhibit superior hardness and enhanced oxidation resistance. This work highlights the potential of machine learning to accelerate materials discovery and provides a robust framework for identifying compounds capable of withstanding extreme environments.