Investigation of QSPR-Based Machine Learning Models in Pyrimidine Corrosion Inhibitors

Muhamad Akrom, Totok Sutojo


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


machine learning; QSPR; corrosion; inhibition efficiency; pyrimidine


Akrom, M. (2022). Investigation Of Natural Extracts As Green Corrosion Inhibitors In Steel Using Density Functional Theory. In Jurnal Teori dan Aplikasi Fisika (Vol. 10, Issue 01).

Akrom, M., Saputro, A. G., Maulana, A. L., Ramelan, A., Nuruddin, A., Rustad, S., & Dipojono, H. K. (2023). DFT and microkinetic investigation of oxygen reduction reaction on corrosion inhibition mechanism of iron surface by Syzygium Aromaticum extract. Applied Surface Science, 615.

Alamri, A. H., & Alhazmi, N. (2022). Development of data driven machine learning models for the prediction and design of pyrimidine corrosion inhibitors. Journal of Saudi Chemical Society, 26(6).

Anadebe, V. C., Nnaji, P. C., Onukwuli, O. D., Okafor, N. A., Abeng, F. E., Chukwuike, V. I., Okoye, C. C., Udoh, I. I., Chidiebere, M. A., Guo, L., & Barik, R. C. (2022). Multidimensional insight into the corrosion inhibition of salbutamol drug molecule on mild steel in oilfield acidizing fluid: Experimental and computer aided modeling approach. Journal of Molecular Liquids, 349.

Belghiti, M. E., Echihi, S., Dafali, A., Karzazi, Y., Bakasse, M., Elalaoui-Elabdallaoui, H., Olasunkanmi, L. O., Ebenso, E. E., & Tabyaoui, M. (2019). Computational simulation and statistical analysis on the relationship between corrosion inhibition efficiency and molecular structure of some hydrazine derivatives in phosphoric acid on mild steel surface. Applied Surface Science, 491, 707–722.

Beltran-Perez, C., Serrano, A. A. A., Solís-Rosas, G., Martínez-Jiménez, A., Orozco-Cruz, R., Espinoza-Vázquez, A., & Miralrio, A. (2022). A General Use QSAR-ARX Model to Predict the Corrosion Inhibition Efficiency of Drugs in Terms of Quantum Mechanical Descriptors and Experimental Comparison for Lidocaine. International Journal of Molecular Sciences, 23(9).

Camacho-Mendoza, R. L., Feria, L., Zárate-Hernández, L. Á., Alvarado-Rodríguez, J. G., & Cruz-Borbolla, J. (2022). New QSPR model for prediction of corrosion inhibition using conceptual density functional theory. Journal of Molecular Modeling, 28(8).

El Assiri, E. H., Driouch, M., Lazrak, J., Bensouda, Z., Elhaloui, A., Sfaira, M., Saffaj, T., & Taleb, M. (2020). Development and validation of QSPR models for corrosion inhibition of carbon steel by some pyridazine derivatives in acidic medium. Heliyon, 6(10).

Haladu, S. A., Dalhat Mu’azu, N., Ali, S. A., Elsharif, A. M., Odewunmi, N. A., & Abd El-Lateef, H. M. (2022). Inhibition of mild steel corrosion in 1 M H2SO4 by a gemini surfactant 1,6-hexyldiyl-bis-(dimethyldodecylammonium bromide): ANN, RSM predictive modeling, quantum chemical and MD simulation studies. Journal of Molecular Liquids, 350, 118533.

Kokalj, A. (2022). Corrosion inhibitors: physisorbed or chemisorbed? Corrosion Science, 196, 109939.

Kozlica, D. K., Kokalj, A., & Milošev, I. (2021). Synergistic effect of 2-mercaptobenzimidazole and octylphosphonic acid as corrosion inhibitors for copper and aluminium – An electrochemical, XPS, FTIR and DFT study. Corrosion Science, 182, 109082.

Kumar, D., Jain, V., & Rai, B. (2022). Capturing the synergistic effects between corrosion inhibitor molecules using density functional theory and ReaxFF simulations - A case for benzyl azide and butyn-1-ol on Cu surface. Corrosion Science, 195.

Li, L., Zhang, X., Gong, S., Zhao, H., Bai, Y., Li, Q., & Ji, L. (2015). The discussion of descriptors for the QSAR model and molecular dynamics simulation of benzimidazole derivatives as corrosion inhibitors. Corrosion Science, 99, 76–88.

Quadri, T. W., Olasunkanmi, L. O., Akpan, E. D., Fayemi, O. E., Lee, H. S., Lgaz, H., Verma, C., Guo, L., Kaya, S., & Ebenso, E. E. (2022). Development of QSAR-based (MLR/ANN) predictive models for effective design of pyridazine corrosion inhibitors. Materials Today Communications, 30.

Quadri, T. W., Olasunkanmi, L. O., Fayemi, O. E., Akpan, E. D., Lee, H. S., Lgaz, H., Verma, C., Guo, L., Kaya, S., & Ebenso, E. E. (2022). Multilayer perceptron neural network-based QSAR models for the assessment and prediction of corrosion inhibition performances of ionic liquids. Computational Materials Science, 214.

Quadri, T. W., Olasunkanmi, L. O., Fayemi, O. E., Lgaz, H., Dagdag, O., Sherif, E. S. M., Akpan, E. D., Lee, H. S., & Ebenso, E. E. (2022). Predicting protection capacities of pyrimidine-based corrosion inhibitors for mild steel/HCl interface using linear and nonlinear QSPR models. Journal of Molecular Modeling, 28(9).

Quadri, T. W., Olasunkanmi, L. O., Fayemi, O. E., Lgaz, H., Dagdag, O., Sherif, E. S. M., Alrashdi, A. A., Akpan, E. D., Lee, H. S., & Ebenso, E. E. (2022). Computational insights into quinoxaline-based corrosion inhibitors of steel in HCl: Quantum chemical analysis and QSPR-ANN studies. Arabian Journal of Chemistry, 15(7).

Ser, C. T., Žuvela, P., & Wong, M. W. (2020). Prediction of corrosion inhibition efficiency of pyridines and quinolines on an iron surface using machine learning-powered quantitative structure-property relationships. Applied Surface Science, 512, 145612.

Sutojo, T., Rustad, S., Akrom, M., Syukur, A., Shidik, G. F., & Dipojono, H. K. (2023). A machine learning approach for corrosion small datasets. Npj Materials Degradation, 7(1).

Thakur, A., Kaya, S., Abousalem, A. S., & Kumar, A. (2022). Experimental, DFT and MC simulation analysis of Vicia Sativa weed aerial extract as sustainable and eco-benign corrosion inhibitor for mild steel in acidic environment. Sustainable Chemistry and Pharmacy, 29.



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