Machine Learning (ML)-aided Modeling of Cyanopyran-based Corrosion Inhibitiors
Jagadeesan Saranya, Yalla Jeevan Nagendra Kumar, Abdelkader Zarrouk, G. Kausalya Sasikumar, Rajender Boddula
Corrosion is a significant challenge across multiple industries, impacting material longevity and safety. This chapter explores interdisciplinary approaches that combine computational methods with experimental validation to ensure practical applicability in real-world scenarios. Computational approaches have revolutionized the study of corrosion mechanisms, prediction, and mitigation strategies. In this study, machine learning methods have been studied to evaluate the corrosion performance of the inhibitor 2-amino-4-(4-hydroxyphenyl)-6-(p-tolyl)-4H-pyran-3-carbonitrile (HCN) on mild steel in 1M H2SO4. Experimental studies like the weight loss method were carried out to test the inhibition efficiency of the inhibitor molecule, which revealed that the inhibition efficiency increased with the increase in the concentration of the inhibitor. Quantum chemical studies were also performed to study the interaction of inhibitor molecules with metal. Regarding machine learning studies, it is identified that the Random Forest was the best algorithm that predicted the entire time profile of corrosion rates with the mean squared error ranging from 0.005 to 0.093. The sensitivity of corrosion rates to changes in the environmental variables is well-predicted by the trained Random Forest model.
Keywords
Inhibition Efficiency, Corrosion, Cyanopyran, Algorithm, Random Forest, Artificial Neural Network
Published online 1/5/2026, 20 pages
Citation: Jagadeesan Saranya, Yalla Jeevan Nagendra Kumar, Abdelkader Zarrouk, G. Kausalya Sasikumar, Rajender Boddula, Machine Learning (ML)-aided Modeling of Cyanopyran-based Corrosion Inhibitiors, Materials Research Foundations, Vol. 188, pp 208-227, 2026
DOI: https://doi.org/10.21741/9781644903919-11
Part of the book on Advances in Corrosion Science and Surface Engineering
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