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A machine learning approach for corrosion small datasets
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 …
predict the corrosion inhibition performance of the inhibitor compound. To overcome the …
A critical review of coordination chemistry of pyrimidine and pyridazine compounds: Bonding, chelation and corrosion inhibition
Metallic deterioration remains a formidable challenge in numerous industrial sectors,
necessitating the continuous, intense search for effective, sustainable and non-toxic …
necessitating the continuous, intense search for effective, sustainable and non-toxic …
Development of quantum machine learning to evaluate the corrosion inhibition capability of pyrimidine compounds
This investigation employs a quantum neural network (QNN) synergistically integrated with a
quantitative structure-property relationship (QSPR) model for the comprehensive evaluation …
quantitative structure-property relationship (QSPR) model for the comprehensive evaluation …
Data-driven investigation to model the corrosion inhibition efficiency of Pyrimidine-Pyrazole hybrid corrosion inhibitors
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 …
machine learning (ML) for a pyrimidine-pyrazole hybrid as a corrosion inhibitor. Based on …
A combination of machine learning model and density functional theory method to predict corrosion inhibition performance of new diazine derivative compounds
This study proposes a novel approach that combines machine learning (ML) and density
functional theory (DFT) methods to construct a quantitative structure-properties relationship …
functional theory (DFT) methods to construct a quantitative structure-properties relationship …
A machine learning approach to predict the efficiency of corrosion inhibition by natural product-based organic inhibitors
This paper presents a quantitative structure–property relationship (QSPR)-based machine
learning (ML) framework designed for predicting corrosion inhibition efficiency (CIE) values …
learning (ML) framework designed for predicting corrosion inhibition efficiency (CIE) values …
Prediction of Anti-Corrosion performance of new triazole derivatives via Machine learning
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 …
efficiency (CIE) of novel triazole derivatives serving as corrosion inhibitors. Among the array …
[HTML][HTML] Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds
This work used a variational quantum circuit (VQC) in conjunction with a quantitative
structure-property relationship (QSPR) model to completely investigate the corrosion …
structure-property relationship (QSPR) model to completely investigate the corrosion …
Applications of conceptual density functional theory in reference to quantitative structure–activity/property relationship
To predict the biological effects of chemical compounds based on mathematical and
statistical relationships, quantitative structure–activity relationship (QSAR) approach is used …
statistical relationships, quantitative structure–activity relationship (QSAR) approach is used …
Multilayer perceptron neural network-based QSAR models for the assessment and prediction of corrosion inhibition performances of ionic liquids
The present study reports the quantum chemical studies and quantitative structure activity
relationship (QSAR) modeling of thirty ionic liquids utilized as chemical additives to repress …
relationship (QSAR) modeling of thirty ionic liquids utilized as chemical additives to repress …