Identifying inorganic solids for harsh environments <i>via</i> 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 di

SUBMITTER: Hickey JC 

PROVIDER: S-EPMC12506783 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature