Implementation of Polynomial Functions to Improve the Accuracy of Machine Learning Models in Predicting the Corrosion Inhibition Efficiency of Pyridine-Quinoline Compounds as Corrosion Inhibitors

Historically, the exploration of corrosion inhibitor technology has relied extensively on experimental methodologies, which are inherently associated with substantial costs, prolonged durations, and significant resource utilization. However, the emergence of ML approaches has recently garnered attention as a promising avenue for investigating potential materials with corrosion inhibition properties. This study endeavors to enhance the predictive capacity of ML models by leveraging polynomial functions. Specifically, the investigation focuses on assessing the effectiveness of pyridinequinoline compounds in mitigating corrosion. Diverse ML models were systematically evaluated, integrating polynomial functions to augment their predictive capabilities. The integration of polynomial functions notably amplifies the predictive accuracy across all tested models. Notably, the SVR model emerges as the most adept, exhibiting R² of 0.936 and RMSE of 0.093. The outcomes of this inquiry underscore a significant enhancement in predictive accuracy facilitated by the incorporation of polynomial functions within ML models. The proposed SVR model stands out as a robust tool for prognosticating the corrosion inhibition potential of pyridine-quinoline compounds. This pioneering approach contributes invaluable insights into advancing machine learning methodologies geared toward designing and engineering materials with promising corrosion inhibition properties. Keywords: machine learning, polynomial, corrosion inhibition, pyridine-quinoline