Interpretable Prediction of Interfacial Resistance for an Imidazoline–Sodium Molybdate–Tween 80 Corrosion Inhibitor Using Bayesian-Optimized Stacking

Predicting the performance of multicomponent corrosion inhibitors requires models that relate composition to electrochemical response and provide interpretable formulation guidance. This study combined Latin hypercube sampling and Bayesian-optimized stacking to investigate an imidazoline–sodium molybdate–Tween 80 inhibitor for mild steel in 3.5 wt.% NaCl. A dataset of 90 formulations linked component concentrations to total interfacial resistance, Rtotal, obtained from electrochemical impedance spectroscopy. The response comprised pore resistance and charge-transfer resistance. The stacking ensemble combined K-nearest neighbors, support vector regression and LightGBM with a ridge regression meta-learner. In ten-fold evaluation following dataset-level hyperparameter selection, stacking achieved R2 = 0.9036, a mean squared error of 969.46 (Ω·cm2)2 and a mean absolute percentage error of 2.22%. SHapley Additive exPlanations (SHAP) assigned normalized mean absolute attributions of 49.7%, 48.0% and 2.3% to Tween 80, imidazoline and sodium molybdate, respectively. These values identify influential predictors within the sampled domain rather than independent chemical contributions. Complementary polarization and surface observations supported adsorption-mediated protection with possible molybdate-associated interfacial reinforcement. By connecting formulation composition, electrochemical response and model attribution, this framework supports the prioritization of inhibitor mixtures under the investigated conditions.