Physics-Informed Hybrid Machine Learning Framework for Adsorption-Consistent Prediction of Corrosion Inhibition Efficiency

Accurately predicting corrosion inhibition efficiency (IE) remains a significant challenge in electrochemical systems because inhibitor performance depends on a complex interaction between molecular electronic structure, solution chemistry, and adsorption thermodynamics at the metal–electrolyte interface. Although machine learning (ML) models have shown strong predictive potential in corrosion studies, most existing approaches function as purely data-driven regressors and do not explicitly incorporate adsorption-based electrochemical principles. As a result, they may generate thermodynamically inconsistent predictions, particularly when applied beyond the range of the training data. In this work, a physics-informed hybrid machine learning framework is developed for adsorption-controlled corrosion systems. A Physics-Informed Neural Network (PINN) is used to incorporate the Langmuir adsorption relationship directly into the training process, ensuring monotonic consistency between surface coverage (θ), adsorption equilibrium constant (K_ads), and inhibition efficiency. To capture nonlinear interactions and multivariate effects beyond the analytical adsorption model, a Gradient Boosting Regressor (GBR) is introduced as a complementary data-driven component. The predictions from both models are then integrated using Ridge-based stacking, allowing a balanced combination of physical interpretability and statistical flexibility. The framework is trained using a composite dataset consisting of 100 experimentally reported corrosion inhibitors and 1000 physics-consistent synthetic samples generated within experimentally observed parameter ranges. Across five-fold cross-validation, the hybrid model achieves stable predictive performance with a mean test-set coefficient of determination of R² ≈ 0.90 while preserving adsorption-consistent behavior. The standalone GBR provides the highest numerical accuracy, while the PINN improves physical consistency and interpretability; the stacked model combines these advantages effectively. Overall, the results show that embedding adsorption thermodynamics within machine learning models improves predictive reliability, physical consistency, and model transparency in corrosion inhibition studies. Rather than introducing a fundamentally new algorithm, this work demonstrates a corrosion-specific integration of physics-informed learning and hybrid regression for more reliable inhibitor screening. The proposed framework provides a structured and reproducible approach for early-stage corrosion inhibitor design and can be extended to other adsorption-governed systems, including catalysis, surface protection, and electrochemical materials engineering.