Synergism of Computational Simulation Technique and Machine Learning Algorithm for Prediction of Anticorrosion Properties of Some Antipyrine Derivatives.

This study aimed to predict the selected antipyrine compounds' inhibitory efficiencies and anticorrosion properties in a hydrochloric acid (HCl) environment. Molecular descriptors and input variables were obtained using density functional theory (DFT), and the variance inflation factor (VIF) was employed to reduce redundant variables, leading to the selection of seven quantum chemical descriptors as input variables. Using machine learning techniques such as