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This study investigates the mechanical, thermal, and chemical stability of tetradecanoic acid-modified Zn/ZnO (Zn-MA) coatings using machine learning (ML) for predictive modeling. Abrasion tests showed that Zn-MA coatings retained superhydrophobicity (WCA > 135°) up to 600 cycles. Among ML models, XGBoost best captured nonlinear mechanical degradation (Test R² = 0.9596), polynomial regression (order 3) accurately predicted thermal stability (Test R² = 0.9870), and random forest effectively modeled chemical resistance (Test R² = 0.8666). Integrated ML predictions under combined stresses indicated that Zn-MA coatings maintain hydrophobicity under thermal and chemical challenges, with reduction in mechanical resilience. These findings demonstrate that ML-driven modeling can reliably predict coating performance.
Mamgain et al. (Wed,) studied this question.