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The corrosion of materials poses a significant challenge in various industries, leading to substantial economic impacts. In this context, pyrimidine compounds emerge as promising, non-toxic, cost-effective, and versatile corrosion inhibitors. However, conventional methods for identifying such inhibitors are typically time-consuming, expensive, and labor-intensive. Addressing this challenge, our study leverages machine learning (ML) to predict pyrimidine compounds corrosion inhibition efficiency (CIE). Using a quantitative structure-property relationship (QSPR) model, we compared 14 linear and 12 non-linear ML algorithms to identify the most accurate predictor of CIE. The bagging regressor model demonstrated superior performance, achieving a root mean square error (RMSE) of 5.38, a mean square error (MSE) of 28.93, a mean absolute error (MAE) of 4.23, and a mean absolute percentage error (MAPE) of 0.05 in predicting the CIE values for pyrimidine compounds. This research marks a significant advancement in corrosion science, offering a novel and efficient ML-based approach as an alternative to traditional experimental methods. It shows that machine learning can quickly and accurately determine how well organic chemical inhibitors like pyrimidine stop material corrosion. This method gives the industry a new perspective and a workable solution to a problem that has existed for a long time. [ABSTRACT FROM AUTHOR]

2, 4, 6-Triaminopyrimidinium 4-Nitrophenolate (2, 4, 6-TAPNP) was grown by slow evaporation technique at ambient temperature. The crystal was subjected to single crystal XRD analysis and it was found that the grown crystal has triclinic system. The functional groups present in the (2,4,6-TAPNP) crystal was identified by FT-IR and FT Raman analysis. The optical absorption spectrum shows the absence of absorption between 470 nm and 800 nm. It confirms the suitability of the crystal for NLO applications. [ABSTRACT FROM AUTHOR]