Machine Learning-Driven Prediction of Corrosion Inhibition Performance by Pyrimidine Derivatives

Corrosion inhibition is a crucial aspect of material protection in various industries. Pyrimidine derivatives have shown promising results as corrosion inhibitors, but predicting their performance remains challenging. This study leverages advanced machine learning techniques to develop a predictive model for the corrosion inhibition performance of pyrimidine derivatives. Utilizing a comprehensive dataset of molecular descriptors, algorithms including random forest, support vector machines, and neural networks were applied to model the relationship between molecular structure and inhibition efficiency.<br/><br/>The model demonstrated excellent predictive performance, with metrics such as R-squared of 0.95 and MAE of 3.2, significantly outperforming traditional methods. This machine learning-driven approach enables the rapid screening and design of pyrimidine-based corrosion inhibitors, accelerating the discovery of new materials. The findings have significant implications for the development of sustainable and efficient corrosion protection strategies, offering a transformative approach for materials science and industrial chemistry.