The discovery of effective and environmentally friendly organic corrosion inhibitors remains constrained by slow experimental screening and fragmented computational workflows . This study presents an integrated hybrid framework combining density functional theory (DFT), molecular dynamics (MD) simulations, and machine learning (ML) for high-throughput screening of organic corrosion inhibitors. DFT provides quantum-level insights into electronic structure and adsorption energetics through frontier molecular orbital analysis (EHOMO, ELUMO, energy gap ΔE), while MD captures time-dependent interfacial behavior and competitive ion interactions . A comprehensive dataset of 284 phenyl phthalimide derivatives was generated through DFT and MD simulations, with electronic properties correlated to experimental inhibition efficiency values . Among various ML models evaluated, Artificial Neural Networks demonstrated the highest prediction accuracy, achieving R² values of 93.18% for EHOMO and 91.12% for ELUMO . SHAP and PFI feature importance analyses revealed that descriptors B06[C-N] and qnmax are essential for inhibitor efficacy . The integrated framework addresses key limitations in current approaches including data scarcity, non-standardized descriptor selection, insufficient physical interpretability, and poor generalization across chemically diverse systems . Experimental validation through electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization confirmed the predictive capability of the ML models, with excellent agreement between predicted and measured inhibition efficiencies. This work establishes a unified, scalable, and physically informed computational framework for rational design and discovery of next-generation corrosion inhibitors.
Rebout,F . (2026). A Hybrid Machine Learning-DFT Framework for High-Throughput Screening of Organic Corrosion Inhibitors: From Electronic Structure Prediction to Experimental Validation. Eurasian Journal of Chemical, Medicinal and Petroleum Research, 5(3), 174-185. doi: 10.5281/zenodo.21196288
MLA
Rebout,F . "A Hybrid Machine Learning-DFT Framework for High-Throughput Screening of Organic Corrosion Inhibitors: From Electronic Structure Prediction to Experimental Validation", Eurasian Journal of Chemical, Medicinal and Petroleum Research, 5, 3, 2026, 174-185. doi: 10.5281/zenodo.21196288
HARVARD
Rebout F. (2026). 'A Hybrid Machine Learning-DFT Framework for High-Throughput Screening of Organic Corrosion Inhibitors: From Electronic Structure Prediction to Experimental Validation', Eurasian Journal of Chemical, Medicinal and Petroleum Research, 5(3), pp. 174-185. doi: 10.5281/zenodo.21196288
CHICAGO
F Rebout, "A Hybrid Machine Learning-DFT Framework for High-Throughput Screening of Organic Corrosion Inhibitors: From Electronic Structure Prediction to Experimental Validation," Eurasian Journal of Chemical, Medicinal and Petroleum Research, 5 3 (2026): 174-185, doi: 10.5281/zenodo.21196288
VANCOUVER
Rebout F. A Hybrid Machine Learning-DFT Framework for High-Throughput Screening of Organic Corrosion Inhibitors: From Electronic Structure Prediction to Experimental Validation. Eurasian J. Chem. Med. Pet. Res.. 2026;5(3):174-185. doi: 10.5281/zenodo.21196288
