Given the high toxicity of inorganic inhibitors, organic substances, primarily heterocycles,
have been proven to be one of the most efficient, cost-effective, and practical alternatives …

In this work, we developed a QSAR model using the K-Nearest Neighbor (KNN) algorithm to
predict the corrosion inhibition performance of the inhibitor compound. To overcome the …

This paper reviews the application of machine learning to the inhibition of corrosion by
organic molecules. The methodologies considered include quantitative structure‐property …

This paper proposes a quantitative structure–property relationship model (QSPR) based on
machine learning (ML) for a pyrimidine-pyrazole hybrid as a corrosion inhibitor. Based on …

This study proposes a novel approach that combines machine learning (ML) and density
functional theory (DFT) methods to construct a quantitative structure-properties relationship …

This scientific paper aims to investigate the best machine learning (ML) for predicting the
corrosion inhibition efficiency (CIE) value of amino acid compounds. The study applied a …

In this investigation, a quantitative structure-property relationship (QSPR) model coupled
with a quantum neural network (QNN) was used to explore the corrosion inhibition efficiency …

Theoretical approaches for example quantum calculation and Monte Carlo (MC) simulations
are very much important in studying corrosion inhibitors due to a comparatively rapid …

Materials informatics often struggles with small datasets. Our study introduces the Gaussian
Mixture Model Virtual Sample Generation (GMM-VSG) approach to enhance feature …

This investigation employs a quantum neural network (QNN) synergistically integrated with a
quantitative structure-property relationship (QSPR) model for the comprehensive evaluation …