Molecular modelling of compounds used for corrosion inhibition studies: a review

• Published in: Physical chemistry chemical physics : PCCP 2021-09, Vol.23 (36), p.19987-227
• Main Authors: Ebenso, Eno E, Verma, Chandrabhan, Olasunkanmi, Lukman O, Akpan, Ekemini D, Verma, Dakeshwar Kumar, Lgaz, Hassane, Guo, Lei, Kaya, Savas, Quraishi, M. A
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Corrosion; Corrosion inhibitors; Corrosion potential; Corrosion tests; Density functional theory; Molecular dynamics; Organic compounds
• ISSN: 1463-9076; 1463-9084
• DOI: 10.1039/d1cp00244a

Molecular modelling of organic compounds using computational software has emerged as a powerful approach for theoretical determination of the corrosion inhibition potential of organic compounds. Some of the common techniques involved in the theoretical studies of corrosion inhibition potential and mechanisms include density functional theory (DFT), molecular dynamics (MD) and Monte Carlo (MC) simulations, and artificial neural network (ANN) and quantitative structure-activity relationship (QSAR) modeling. Using computational modelling, the chemical reactivity and corrosion inhibition activities of organic compounds can be explained. The modelling can be regarded as a time-saving and eco-friendly approach for screening organic compounds for corrosion inhibition potential before their wet laboratory synthesis would be carried out. Another advantage of computational modelling is that molecular sites responsible for interactions with metallic surfaces (active sites or adsorption sites) and the orientation of organic compounds can be easily predicted. Using different theoretical descriptors/parameters, the inhibition effectiveness and nature of the metal-inhibitor interactions can also be predicted. The present review article is a collection of major advancements in the field of computational modelling for the design and testing of the corrosion inhibition effectiveness of organic corrosion inhibitors. This perspective review features, among others, the use of DFT, QSAR modeling, artificial neural network (ANN) modeling, molecular dynamics simulations and Monte Carlo simulations in modelling organic corrosion inhibitors. It is a compendium of studies on the subject.