Predicting protection capacities of pyrimidine-based corrosion inhibitors for mild steel/HCl interface using linear and nonlinear QSPR models

Quadri, Taiwo W.; Olasunkanmi, Lukman O.; Fayemi, Omolola E.; Lgaz, Hassane; Dagdag, Omar; Sherif, El‐Sayed M.; Akpan, Ekemini D.; Lee, Han-Seung; Ebenso, Eno E.

doi:10.1007/s00894-022-05245-1

Cited by 19 publications

(7 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Untuk mengevaluasi kinerja inhibitor, banyak teknik ML digunakan secara luas dan digunakan dalam pembuatan model QSPR. Quadri et al (Quadri et al, 2022c) Dilaporkan bahwa model RF menunjukkan hasil yang lebih baik dibandingkan model PLS. Karena senyawa yang digunakan pada kedua penelitian tersebut sama, kemi berhipotesis seharusnya ada model spesifik yang dapat digunakan pada kedua dataset dari penelitian diatas.…”

Section: Pendahuluanunclassified

“…Dalam penelitian ini, kami menggunakan dataset senyawa pirimidin yang terpublikasi pada literatur Quadri et al (Quadri et al, 2022c) dan Alamri et al (Alamri & Alhazmi, 2022). Model QSPR dibangun menggunakan berbagai deskriptor kimia kuantum yang dihitung DFT dari bahan kimia inhibitor untuk membantu desain penghambatan korosi.…”

Section: Metode Penelitian 21 Dataset Dan Deskriptorunclassified

See 1 more Smart Citation

Investigasi Model Machine Learning Berbasis QSPR pada Inhibitor Korosi Pirimidin

Akrom

Sutojo²

2023

Eksergi

View full text Add to dashboard Cite

Since corrosion causes considerable losses in many fields, including the economy, environment, society, industry, security, and safety, it is a major concern for the industrial and academic sectors. Damage control of materials based on organic compounds is currently a field of great interest. Because it is non-toxic, affordable, and effective in a variety of corrosive situations, pyrimidine has potential as a corrosion inhibitor. It takes a lot of time and resources to carry out experimental investigations in the exploration of potential corrosion inhibitor candidates. In this study, we evaluate the gradient boosting regressor (GBR), support vector regression (SVR), and k-nearest neighbor (KNN) algorithms as predictive models for corrosion inhibition efficiency using a machine learning (ML) approach based on the quantitative structure-property relationship model (QSPR). Based on the metric coefficient of determination (R2) and root mean square error (RMSE), we found that the GBR model had the best predictive performance compared to the SVR and KNN models as well as models from the literature for pyrimidine compound datasets. Overall, our study offers a new perspective on the ability of ML models to predict corrosion inhibition of iron surfaces

show abstract

Section: Pendahuluanunclassified

Section: Metode Penelitian 21 Dataset Dan Deskriptorunclassified

Investigasi Model Machine Learning Berbasis QSPR pada Inhibitor Korosi Pirimidin

Akrom

Sutojo²

2023

Eksergi

View full text Add to dashboard Cite

show abstract

“…Ser et al 13 employed an artificial neural network (ANN) algorithm to construct an IE predictive model for pyridine and quinoline compounds on steel using a dataset of 40 compounds from the input data, including the inhibitor molecular descriptors and the adsorption energies on the Fe surface. Recently, Quadri et al used an ANN algorithm to build an IE predictive model for pyridazines, 14 ionic liquids, 15 and pyrimidines 16 on steel. In these studies, the input data includes five main descriptors selected from many quantum chemically-derived and cheminformatics-derived descriptors.…”

Section: Introductionmentioning

confidence: 99%

A data-driven QSPR model for screening organic corrosion inhibitors for carbon steel using machine learning techniques

Pham,

Le,

Son

2024

RSC Adv.

View full text Add to dashboard Cite

show abstract

“…Machine learning (ML) techniques that utilize quantitative structure-activity relationships (QSAR) or quantitative structure-property relationships (QSPR) have shown promise in the field of corrosion inhibitors, both in terms of efficiency and effectiveness [8][9][10][11]. Prior studies have examined different algorithmic models, including Partial Least Squares (PLS), Multiple Linear Regression (MLR), Random Forest (RF), Autoregressive with Exogenous Inputs (ARX), Support Vector Machine (SVM), and similar models [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Corrosion Inhibition Efficiency Based on Machine Learning for Pyrimidine Compounds: A Comparative Study of Linear and Non-linear Algorithms

Herowati,

Aji Eko Prabowo,

Akrom

et al. 2024

KEG

View full text Add to dashboard Cite

The corrosion of materials poses a significant challenge in various industries, leading to substantial economic impacts. In this context, pyrimidine compounds emerge as promising, non-toxic, cost-effective, and versatile corrosion inhibitors. However, conventional methods for identifying such inhibitors are typically time-consuming, expensive, and labor-intensive. Addressing this challenge, our study leverages machine learning (ML) to predict pyrimidine compounds corrosion inhibition efficiency (CIE). Using a quantitative structure-property relationship (QSPR) model, we compared 14 linear and 12 non-linear ML algorithms to identify the most accurate predictor of CIE. The bagging regressor model demonstrated superior performance, achieving a root mean square error (RMSE) of 5.38, a mean square error (MSE) of 28.93, a mean absolute error (MAE) of 4.23, and a mean absolute percentage error (MAPE) of 0.05 in predicting the CIE values for pyrimidine compounds. This research marks a significant advancement in corrosion science, offering a novel and efficient ML-based approach as an alternative to traditional experimental methods. It shows that machine learning can quickly and accurately determine how well organic chemical inhibitors like pyrimidine stop material corrosion. This method gives the industry a new perspective and a workable solution to a problem that has existed for a long time. Keywords: machine learning, corrosion inhibition, pyrimidine, QSPR model, predictive analysis

show abstract

Predicting protection capacities of pyrimidine-based corrosion inhibitors for mild steel/HCl interface using linear and nonlinear QSPR models

Cited by 19 publications

References 54 publications

Investigasi Model Machine Learning Berbasis QSPR pada Inhibitor Korosi Pirimidin

Investigasi Model Machine Learning Berbasis QSPR pada Inhibitor Korosi Pirimidin

A data-driven QSPR model for screening organic corrosion inhibitors for carbon steel using machine learning techniques

Prediction of Corrosion Inhibition Efficiency Based on Machine Learning for Pyrimidine Compounds: A Comparative Study of Linear and Non-linear Algorithms

Contact Info

Product

Resources

About