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

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 paper presents a quantitative structure–property relationship (QSPR)-based machine
learning (ML) framework designed for predicting corrosion inhibition efficiency (CIE) values …

This paper endeavors to present an in-depth investigation into the corrosion inhibition
efficiency (CIE) of novel triazole derivatives serving as corrosion inhibitors. Among the array …

This work used a variational quantum circuit (VQC) in conjunction with a quantitative
structure-property relationship (QSPR) model to completely investigate the corrosion …

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 …

Corrosion is a major concern for the industrial and academic sectors because it causes
significant losses in many fields. Currently, there is a great deal of interest in the topic of …