Harnessing machine learning and virtual sample generation for corrosion studies of 2-alkyl benzimidazole scaffold small dataset with an experimental validation

Abstract

The present work deals with the development of a QSAR model based on Artificial Neural Network (ANN) algorithm for the prediction of inhibition efficiencies of 2-alkyl benzimidazole scaffold-based corrosion inhibitors for mild steel corrosion in 1 M HCl. The small dataset problems have been dealt with a Virtual Sample Generation (VSG) methodology using CTGAN algorithm, and credibility and generalizability of the proposed ANN+VSG model has been verified through experimental validation. Two new 2-alkyl benzimidazole scaffold-based corrosion inhibitor compounds namely EBIMOT and PBIMOT, were synthesised, their inhibition efficiencies were experimentally obtained, and the values showed a high resemblance with the predicted one by the model with good accuracy. Synthetic data embedding to the training samples enhances the model's recognition capacity of feature-target relationship and hence stabilizing and improving the correlation of the chemical quantum descriptors with the inhibition efficiency. This proposed method strengthens the prospect of ML for developing material designs, especially in the case of small datasets at a much cost-efficient, user-friendly, and accurate manner and can open doors to new and unexplored venues in the intersection of material science and computational intelligence.