A machine learning framework for screening phenyl phthalimide derivatives as corrosion inhibitors based on dataset generated by DFT and molecular dynamics simulations

• A framework that combines computational calculation (DFT and MD) with machine learning to screen phenyl phtalamide derivative compounds as corrosion inhibitor. • ANN shows highest accuracy (R 2 =93.18), in comparison to XGBoost (R 2 =81.29), XTRees (R 2 =84.59), KNN (R 2 =58.36), and SVR (R 2 =73.74). • B06[C-N] and qnmax are the most important factor to affect inhibitor efficiency. Corrosion in metals, especially carbon steel, incurs substantial economic losses, estimated at 2.5 trillion USD per year, or 3.4% of the global GDP. This research examines the corrosion inhibition efficacy of 284 phenyl phthalimide derivatives through computational techniques and machine learning. The electronic structure properties, comprising E HOMO (mean: -6.36 eV, standard deviation: 0.49 eV), E LUMO (mean: -2.41 eV, standard deviation: 0.30 eV), and adsorption energy (mean: -171.23 kJ/mol, standard deviation: 27.50 kJ/mol), were determined through Density Functional Theory (DFT) and Molecular Dynamics (MD) simulations, and correlated with experimentally obtained inhibition efficiency. Various machine learning models, such as Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and XGBoost, were utilized, with ANN demonstrating the highest prediction accuracy, reaching R² values of 93.18% for E HOMO and 91.12% for E LUMO . The complex relationship between molecular descriptor and electronic properties makes ANN perform better than XGBoost and XTRees. SHAP and PFI studies of feature importance revealed that descriptors B06[C-N] (PFI score: 0.016) and qnmax (PFI score: 0.009) are essential for inhibitor efficacy. Correlation-based filtering for feature reduction revealed that models utilizing a diminished set of 1,078 descriptors (down from 5,627) maintained robust performance, attaining a R² score of 91.47% for E HOMO . These findings create a strong computational framework for the screening and optimization of corrosion inhibitors, minimizing reliance on experimental trials and facilitating cost-effective, scalable synthesis of customized inhibitors. Subsequent efforts will be focused on experimental validation and the implementation of this methodology in more extensive chemical contexts.