AI-enabled prediction and transformation analysis of phases in multi-component nitride thin films and coatings

This study investigates the relationship between composition, descriptors, and phase structure in nitride thin films and coatings using machine learning. A dedicated dataset of 79 systems, comprising 627 individual records, is analyzed using features derived from elemental properties and thermodynamic parameters. The Gradient Boosting model performs excellently on the 8 key features, achieving 0.94 mean accuracy in phase classification. Critical descriptors such as D _CR and C _N are identified as dominant predictors through feature importance analysis. Interpretable models reveal a pronounced preference for FCC phase formation at higher values of D _CR (>0.33) and C _N (>0.35). The study further proposes data inclusion principles to address the limitations of existing models under limited data conditions. By reformulating the classification task as a regression problem via a sigmoid function, an explicit predictive expression based on the key descriptors is derived. These findings highlight the value of diverse compositional databases and interpretable machine learning for understanding complex material systems and guiding the design of materials with stable structures.