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

ABSTRACT: Machine learning (ML) techniques have shown great potential for screening corrosion inhibitors. In this study, a data-driven quantitative structure-property relationship (QSPR) model using the gradient boosting decision tree (GB) algorithm combined with the permutation feature importance (PFI) technique was developed to predict the corrosion inhibition efficiency (IE) of organic compounds on carbon steel. The results showed that the PFI method effectively selected the molecular descriptors most relevant to the IE. Using these important molecular descriptors, an IE predictive model was trained on a dataset encompassing various categories of organic corrosion inhibitors for carbon steel, achieving RMSE, MAE, and R² of 6.40%, 4.80%, and 0.72, respectively. The integration of

SUBMITTER: Pham TH 

PROVIDER: S-EPMC10999907 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature