A data-driven QSPR model for screening organic corrosion inhibitors for carbon steel using machine learning techniques

Abstract

An advanced machine learning workflow integrating the gradient boosting decision tree (GB) algorithm and the permutation feature importance (PFI) technique has been proposed to predict the corrosion inhibition efficiency (IE) of organic compounds.