Artificial intelligence and machine learning in corrosion inhibitor design & development: Advances, challenges, and future perspectives

The emerging field of artificial intelligence (AI) and machine learning (ML) has opened new frontiers in corrosion science, particularly in the design, screening and performance prediction of corrosion inhibitors. Traditional experimental and quantum chemical approaches, while reliable, are often time-consuming and limited by empirical correlations. AI and ML driven models now offer a data-intensive alternative capable of predicting inhibitor efficiency, adsorption behavior, and electrochemical response with remarkable precision. Here in this study, recent progress in applying AI and ML algorithms such as artificial neural networks, support vector machines, decision trees, and deep learning frameworks to predict corrosion inhibition efficiency, adsorption mechanisms, and electrochemical parameters derived from potentiodynamic and impedance measurements are critically examined. The study reviews the data foundation essential for AI workflows including quantum, electrochemical, and image-based descriptors along with classical (SVR, RF, ANN), deep-learning (3L-DMPNN, ChemBERTa), and hybrid quantum ML architectures for inhibition efficiency prediction. Emerging generative models like MoIGPT have demonstrated the ability to design molecules conditioned on factors such as performance and toxicity. Meanwhile, integrated AI Electrochemistry pipelines connect machine learning predictions directly to experimental validation through electrochemical impedance spectroscopy and potentiodynamic polarization techniques. Despite remarkable advances, challenges remain in data standardization, model interpretability, scalability, and sustainability. Addressing these bottlenecks through FAIR data infrastructure, explainable and trustworthy AI, and green computational practices, will be critical for realizing the long-term vision of fully autonomous, eco-conscious, and self-optimizing corrosion-management ecosystems.