Pyrimidines have been shown as promising nontoxic corrosion inhibitors for carbon steel in
acid media that can replace toxic chemicals currently in use. However, the discovery of this …

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 …

Pyrimidine compounds have proven to be effective and efficient additives capable of
protecting mild steel in acidic media. This class of organic compounds often functions as …

Twenty pyridazine derivatives with previously reported experimental data were utilized to
develop predictive models for the anticorrosion abilities of pyridazine-based compounds …

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 …

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 …

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

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 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 …