Abstract
This paper presents a machine learning (ML) methodology grounded in quantitative structure-property relationship (QSPR) principles for the prediction of corrosion inhibition efficiency (CIE) values, specifically focusing on pyridazine inhibitor compounds. The training phase incorporates the kernel density estimation (KDE) function to generate virtual samples, aiming to enhance the prediction accuracy of the ML model. The study evaluates the performance of three models, namely gradient boosting (GB), random forest (RF), and k-nearest neighbor (KNN). The results exhibit a substantial enhancement in predictive ability following the incorporation of virtual samples. Specifically, coefficient of determination (R2) values for GB, RF, and KNN models increase from − 0.33 to 0.97, -0.20 to 0.96, and − 0.17 to 0.95, respectively, with the addition of 1000 virtual samples. Correspondingly, root mean square error (RMSE) values for each model experience a significant decrease, reducing from 9.20 to 1.57, 9.07 to 1.81, and 8.60 to 2.12., respectively. This augmentation enhances the correlation between features and targets, resulting in more accurate predictions and eliminating the necessity for feature selection. Furthermore, it implies resilience to model variations, eliminating the need for model selection. The proposed methodology is a crucial link between theoretical research and experimental synthesis, providing a reliable and accurate prediction tool. This tool proves instrumental in efficiently designing and exploring corrosion inhibitor candidates, thereby contributing to the advancement of effective corrosion inhibition strategies.