<![CDATA[Corrosion is a significant issue in both industrial and academic sectors, with widespread negative impacts on various aspects, including economics and safety. To address this problem, the use of corrosion inhibitors has proven effective. This study explores the application of Machine Learning (ML) methods based on Quantitative Structure-Properties Relationship (QSPR) to develop a predictive model for the efficiency of quinoxaline compounds as corrosion inhibitors. By conducting a comparative analysis among three algorithms: AdaBoost Regressor (ADB), Gradient Boosting Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR), and optimizing parameters through hyperparameter tuning using Grid Search and Random Search, this research demonstrates that the XGBR model yields the most superior prediction results. The XGBR optimized with hyperparameter tuning using Grid Search achieved the highest R² value of 0.970 and showed the lowest RMSE, MSE, MAD, and MAPE values of 0.368, 0.135, 0.119, and 0.273, respectively, indicating high predictive accuracy. These results are expected to contribute to the development of more effective methods for identifying corrosion inhibitor candidates.]]>
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