Utilization of Machine Learning for Predicting Corrosion Inhibition by Quinoxaline Compounds

Muhamad Fadil; Muhamad Akrom; Wise Herowati

doi:10.30871/jaic.v9i1.8894

Muhamad Fadil Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Muhamad Akrom Research Center for Quantum Computing and Materials Informatics, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Wise Herowati Research Center for Quantum Computing and Materials Informatics, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

DOI: https://doi.org/10.30871/jaic.v9i1.8894

Abstract

Corrosion is a significant issue in both industrial and academic sectors, with widespread negative impacts on various aspects, including economics and safety. To address this problem, the use of corrosion inhibitors has proven effective. This study explores the application of Machine Learning (ML) methods based on Quantitative Structure-Properties Relationship (QSPR) to develop a predictive model for the efficiency of quinoxaline compounds as corrosion inhibitors. By conducting a comparative analysis among three algorithms: AdaBoost Regressor (ADB), Gradient Boosting Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR), and optimizing parameters through hyperparameter tuning using Grid Search and Random Search, this research demonstrates that the XGBR model yields the most superior prediction results. The XGBR optimized with hyperparameter tuning using Grid Search achieved the highest R² value of 0.970 and showed the lowest RMSE, MSE, MAD, and MAPE values of 0.368, 0.135, 0.119, and 0.273, respectively, indicating high predictive accuracy. These results are expected to contribute to the development of more effective methods for identifying corrosion inhibitor candidates.

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References

T. Sutojo, S. Rustad, M. Akrom, A. Syukur, G. F. Shidik, and H. K. Dipojono, “A machine learning approach for corrosion small datasets,” Npj Mater Degrad, vol. 7, no. 1, Dec. 2023, doi: 10.1038/s41529-023-00336-7.

M. Akrom et al., “DFT and microkinetic investigation of oxygen reduction reaction on corrosion inhibition mechanism of iron surface by Syzygium Aromaticum extract,” Appl Surf Sci, vol. 615, Apr. 2023, doi: 10.1016/j.apsusc.2022.156319.

S. Budi et al., “Implementation of Polynomial Functions to Improve the Accuracy of Machine Learning Models in Predicting the Corrosion Inhibition Efficiency of Pyridine-Quinoline Compounds as Corrosion Inhibitors,” KnE Engineering, Mar. 2024, doi: 10.18502/keg.v6i1.15351.

M. Akrom, “Seminar Nasional Sains Dan Teknik Fst Undana (Sainstek) Ekstrak Bahan Alam Sebagai Inhibitor Hijau Pada Korosi Baja Natural Products as a Green Inhibitor of Corrosion on Steel.”

M. Akrom, “Investigasi Ekstrak Bahan Alam Sebagai Inhibitor Korosi Hijau Pada Baja Menggunakan Teori Fungsional Kerapatan,” 2021, doi: 10.46984/sebatik.v00i0.0000.

M. Akrom, S. Rustad, A. G. Saputro, A. Ramelan, F. Fathurrahman, and H. K. Dipojono, “A combination of machine learning model and density functional theory method to predict corrosion inhibition performance of new diazine derivative compounds,” Mater Today Commun, vol. 35, Jun. 2023, doi: 10.1016/j.mtcomm.2023.106402.

M. Akrom, “Experimental Investigation Of Natural Plant Extracts As A Green Corrosion Inhibitor In Steel (Investigasi Eksperimental Ekstrak Tanaman Alam Sebagai Inhibitor Korosi Pada Baja),” Journal Renewable Energy & Mechanics, no. 01, 2022, doi: 10.25299/rem.2022.vol5(01).8887.

M. Akrom, W. Aji, E. Prabowo, and K. Kunci, “Investigation of Natural Product Extracts as Green Corrosion Inhibitors on Steels and Alloys: Experimental and Theoretical Approach.” [Online]. Available: http://esec.upnvjt.com/

M. Akrom, T. Sutojo, A. Pertiwi, S. Rustad, and H. K. Dipojono, “Investigation of Best QSPR-Based Machine Learning Model to Predict Corrosion Inhibition Performance of Pyridine-Quinoline Compounds,” in Journal of Physics: Conference Series, Institute of Physics, 2023. doi: 10.1088/1742-6596/2673/1/012014.

A. Nur, A. Thohari, A. Karima, K. Santoso, and R. Rahmawati, “Crack Detection in Building Through Deep Learning Feature Extraction and Machine Learning Approach,” 2024. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC

R. I. D. Putra, A. L. Maulana, and A. G. Saputro, “Study on building machine learning model to predict biodegradable-ready materials,” in AIP Conference Proceedings, American Institute of Physics Inc., Mar. 2019. doi: 10.1063/1.5095351.

M. Akrom, S. Rustad, and H. K. Dipojono, “A machine learning approach to predict the efficiency of corrosion inhibition by natural product-based organic inhibitors,” Phys Scr, vol. 99, no. 3, Mar. 2024, doi: 10.1088/1402-4896/ad28a9.

T. W. Quadri et al., “Computational insights into quinoxaline-based corrosion inhibitors of steel in HCl: Quantum chemical analysis and QSPR-ANN studies,” Arabian Journal of Chemistry, vol. 15, no. 7, Jul. 2022, doi: 10.1016/j.arabjc.2022.103870.

M. Kemampuan Penghambatan Korosi oleh Senyawa Benzimidazole, C. Adryan Putra Sumarjono, M. Akrom, and G. Alfa Trisnapradika, “Perbandingan Model Machine Learning Terbaik untuk Comparison of the Best Machine Learning Model to Predict Corrosion Inhibition Capability of Benzimidazole Compounds,” 2023.

N. V. Putranto, M. Akrom, G. A. Trinapradika, and A. History, “Jurnal Teknologi dan Manajemen Informatika Implementasi Fungsi Polinomial pada Algoritma Gradient Boosting Regressor: Studi Regresi pada Dataset Obat-Obatan Kadaluarsa sebagai Material Antikorosi Article Info ABSTRACT,” vol. 9, no. 2, pp. 172–182, 2023, [Online]. Available: http://http://jurnal.unmer.ac.id/index.php/jtmi

T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, Aug. 2016, pp. 785–794. doi: 10.1145/2939672.2939785.

M. Akrom, S. Rustad, and H. Kresno Dipojono, “Prediction of Anti-Corrosion performance of new triazole derivatives via Machine learning,” Comput Theor Chem, vol. 1236, Jun. 2024, doi: 10.1016/j.comptc.2024.114599.

M. Fajri and A. Primajaya, “Komparasi Teknik Hyperparameter Optimization pada SVM untuk Permasalahan Klasifikasi dengan Menggunakan Grid Search dan Random Search,” 2023. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC

S. Himawan, R. Sohiburoyyan, and I. Iryanto, “Hyperparameter Tuning on Graph Neural Network for the Classification of SARS-CoV-2 Inhibitors”, JAIC, vol. 7, no. 2, pp. 186-191, Nov. 2023.

D. S. Chauhan, P. Singh, and M. A. Quraishi, “Quinoxaline derivatives as efficient corrosion inhibitors: Current status, challenges and future perspectives,” Dec. 15, 2020, Elsevier B.V. doi: 10.1016/j.molliq.2020.114387.

J. Yang, S. Rahardja, and P. Fränti, “Outlier detection: How to threshold outlier scores?,” in ACM International Conference Proceeding Series, Association for Computing Machinery, Dec. 2019. doi: 10.1145/3371425.3371427.

M. Akrom, S. Rustad, and H. Kresno Dipojono, “Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors,” Results Chem, vol. 6, Dec. 2023, doi: 10.1016/j.rechem.2023.101126.

W. Herowati et al., “Prediction of Corrosion Inhibition Efficiency Based on Machine Learning for Pyrimidine Compounds: A Comparative Study of Linear and Non-linear Algorithms,” KnE Engineering, Mar. 2024, doi: 10.18502/keg.v6i1.15350.

M. Akrom et al., “Artificial Intelligence Berbasis QSPR Dalam Kajian Inhibitor Korosi,” vol. 07, no. 01, pp. 15–20, [Online]. Available: https://doi.org/10.

A. B. Prasetyo and T. G. Laksana, “Optimasi Algoritma K-Nearest Neighbors dengan Teknik Cross Validation Dengan Streamlit (Studi Data: Penyakit Diabetes),” 2022. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC

S. Budi, M. Akrom, G. A. Trisnapradika, T. Sutojo, and W. A. E. Prabowo, “Optimization of Polynomial Functions on the NuSVR Algorithm Based on Machine Learning: Case Studies on Regression Datasets,” Scientific Journal of Informatics, vol. 10, no. 2, pp. 151–158, May 2023, doi: 10.15294/sji.v10i2.43929.

M. Akrom, S. Rustad, and H. K. Dipojono, “Development of quantum machine learning to evaluate the corrosion inhibition capability of pyrimidine compounds,” Mater Today Commun, vol. 39, Jun. 2024, doi: 10.1016/j.mtcomm.2024.108758.

M. Akrom, S. Rustad, A. G. Saputro, and H. K. Dipojono, “Data-driven investigation to model the corrosion inhibition efficiency of Pyrimidine-Pyrazole hybrid corrosion inhibitors,” Comput Theor Chem, vol. 1229, Nov. 2023, doi: 10.1016/j.comptc.2023.114307.

M. Akrom and T. Sutojo, “Investigasi Model Machine Learning Berbasis QSPR pada Inhibitor Korosi Pirimidin Investigation of QSPR-Based Machine Learning Models in Pyrimidine Corrosion Inhibitors,” 2023.

V. Frendyatha Adiprasetya, M. Akrom, and G. Alfa Trisnapradika, “Investigasi Efisiensi Penghambatan Korosi Senyawa Quinoxaline Berbasis Machine Learning A Study on the Corrosion Inhibition Efficiency of Quinoxaline Compounds Utilizing Machine Learning,” 2024.

F. M. Haikal, M. Akrom, and G. A. Trisnapradika, “Perbandingan Algoritma Multilinear Regression dan Decision Tree Regressor dalam Memprediksi Efisiensi Penghambatan Korosi Piridazin,” Edumatic: Jurnal Pendidikan Informatika, vol. 7, no. 2, pp. 307–315, Dec. 2023, doi: 10.29408/edumatic.v7i2.22127.