Integrating machine learning, density functional theory, and molecular dynamics for smart corrosion inhibitor design: a critical review

The rational development of sustainable and high-performance corrosion inhibitors for mild steel in acidic environments remains constrained by slow experimental screening, fragmented computational workflows, and limited predictive reliability. Conventional laboratory approaches typically yield empirical performance metrics with limited mechanistic resolution, while Density Functional Theory (DFT), Molecular Dynamics (MD), and Machine Learning (ML) are frequently applied as isolated tools, resulting in inconsistent datasets and weak translational relevance to industrial corrosion systems. This critical review systematically examines the design of smart corrosion inhibitors through an integrated ML–DFT–MD framework. DFT provides quantum-level insight into electronic structure and adsorption energetics, MD captures time-dependent interfacial behavior and competitive ion interactions under acidic conditions, and ML enables data-driven prediction and high-throughput screening. However, a critical analysis of recent studies reveals that most reported ML-based corrosion models remain fundamentally limited by data scarcity, non-standardized descriptor selection, insufficient physical interpretability, and poor generalization across chemically diverse inhibitor systems and operating environments. By synthesizing representative case studies and recent advances, this review identifies key methodological bottlenecks that prevent current ML–DFT–MD workflows from achieving reliable predictive capability and industrial scalability. In contrast to previous reviews that address these techniques in isolation, this work critically evaluates their integration readiness and outlines concrete requirements for physically informed, interpretable, and transferable modeling strategies. Future perspectives emphasize the need for standardized open datasets, explicit solvation and interfacial modeling, uncertainty-aware and physics-informed ML architectures, and generative design frameworks capable of coherently linking quantum chemistry, interfacial dynamics, and data-driven prediction. Overall, this review establishes a unified roadmap toward predictive, scalable, and industrially relevant corrosion inhibitor design.