Machine learning-powered qualitative structure properties relationship models for prediction of corrosion inhibition efficiencies of triazoles.

Authors

Christopher Ikechukwu Ekeocha, Anthony C Ozurumba, Ikechukwu Nelson Uzochukwu, Ochu Linda Onyeke, Emeka Emmanuel Oguzie

Published in

Journal of computer-aided molecular design. Volume 40. Issue 1. Jul 29, 2026. Epub Jul 29, 2026.

Abstract

The development of a holistic theoretical framework that can predict the corrosion inhibition efficiency and evaluate the anti-corrosion potentials of novel materials has been a challenging one. This work aimed to address this challenge by integrating machine learning-based Quantitative Structure-Property Relationship (QSPR) models with computational simulation techniques. 25 descriptors derived from density functional theory (DFT) results for 130 triazole derivatives on mild and carbon steels in hydrochloric acid (HCl) solutions were used to develop predictive models. Random Forest, K-Nearest Neighbor, Gradient Boosting, Support Vector Regression, and Stacked Regression. Key features influencing the model's predicted outcomes were identified through Recursive Feature Elimination (RFE) and Shapley Additive ExPlanation (SHAP) analyses. The models demonstrated competitive performance with Stacked Regression being more pronounced, as indicated by results of some statistical metrics, including Mean Squared Error (60.50-64.90), Root Mean Squared Error (7.740-8.056), Mean Absolute Error (6.179-6.340), Mean Absolute Percentage Error (7.17-7.37), and a concordance correlation coefficient (0.30-0.31). Among the novel triazoles evaluated, T₁ emerged as the most effective inhibitor, exhibiting corrosion inhibition efficiencies (CIEs) ranging from 89.66 to 93.17%, followed by T₂ (88.76-94.38%) and T₃ (85.76-92.43%) across the models. Further analysis using Mulliken population, Frontier Molecular Orbital (FMO), and Radial Distribution Function (RDF) revealed molecular/atomistic interactions between the inhibitor molecules and the metal, indicating effective chemical adsorption characterized by high adsorption energies from - 143.56 kcal/mol to - 175.79 kcal/mol and favorable orientations for spontaneous and robust interactions. This research underscores the effectiveness of combining machine learning with computational simulations in corrosion science.

PMID:
42527743
Bibliographic data and abstract were imported from PubMed on 30 Jul 2026.

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