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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Adimas : Jurnal Pengabdian Kepada Masyarakat JE (Journal of Empowerment) International Journal of Advances in Data and Information Systems Abdimasku : Jurnal Pengabdian Masyarakat Journal of Computing Theories and Applications Journal of Multiscale Materials Informatics Journal of Future Artificial Intelligence and Technologies
Herowati, Wise
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

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Journal : Journal of Multiscale Materials Informatics

 1 
Investigation of Corrosion Inhibition Efficiency of Pyridine-Quinoline Compounds through Machine Learning Herowati, Wise; Akrom, Muhamad; Hidayat, Novianto Nur; Sutojo, Totok
Journal of Multiscale Materials Informatics Vol. 1 No. 1 (2024): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v1i1.10448

Abstract

Corrosion in materials is a significant concern for the industrial and academic fields because corrosion causes enormous losses in various fields such as the economy, environment, society, industry, security, safety, and others. Currently, material damage control using organic compounds has become a popular field of study. Pyridine and quinoline stand out as corrosion inhibitors among a myriad of organic compounds because they are non-toxic, inexpensive, and effective in a variety of corrosive environments. Experimental investigations in developing various candidate potential inhibitor compounds are time and resource-intensive. In this work, we use a quantitative structure-property relationship (QSPR)-based machine learning (ML) approach to investigate support vector machine (SVR), random forest (RF), and k-nearest neighbors (KNN) algorithms as predictive models of inhibition performance. (Inhibition efficiency) corrosion of pyridine-quinoline derivative compounds as corrosion inhibitors on iron. We found that the RF model showed the best predictive ability based on the coefficient of determination (R2) and root mean squared error (RMSE) metrics. Overall, our study provides new insights regarding the ML model in predicting corrosion inhibition on iron surfaces.
Comparative Study of Classical, Quantum, and Hybrid Stacking Models for Predicting Corrosion Inhibition Efficiency Using QSAR Descriptors Herowati, Wise; Akrom, Muhamad
Journal of Multiscale Materials Informatics Vol. 2 No. 1 (2025): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v2i1.12217

Abstract

This study investigates the performance of classical, quantum, and hybrid classical-quantum stacking models in predicting Corrosion Inhibition Efficiency (IE%) using 14 QSAR descriptors. The hybrid model combines a Gradient Boosting Regressor (GBR) and a Quantum Support Vector Regressor (QSVR) through a meta-learner (Ridge Regression). Results show a significant improvement over traditional models. The hybrid stacking model achieved an R² of 0.834, an MSE of 8.123, an MAE of 2.371, and an RMSE of 2.850, outperforming both individual classical and quantum models. These results confirm the strength of hybrid models in capturing both complex nonlinear and quantum-interaction patterns in QSAR-based molecular prediction.
Hybrid Quantum Neural Network for Predicting Corrosion Inhibition Efficiency of Organic Molecules Herowati, Wise; Akrom, Muhamad
Journal of Multiscale Materials Informatics Vol. 2 No. 2 (2025): Oktober
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v2i2.15132

Abstract

Corrosion inhibition efficiency (IE%) prediction plays a central role in the computational discovery of high-performance organic inhibitors. Classical machine learning has shown promising results; however, its performance often deteriorates when learning non-linear interactions between quantum chemical descriptors. Meanwhile, quantum machine learning (QML) provides enhanced expressivity through quantum feature mapping but remains limited by NISQ-era hardware. In this study, we propose a Hybrid Quantum Neural Network (HQNN) integrating classical dense layers with variational quantum circuits (VQC) to predict the inhibition efficiency of organic corrosion inhibitors. Using a curated dataset of 660 molecules with DFT descriptors, the HQNN achieves an RMSE of 3.41 and R² of 0.958, outperforming classical regressors and pure VQC. The results demonstrate that hybrid quantum models offer a balanced trade-off between quantum advantage and practical feasibility in materials informatics.
Co-Authors Achmad Wahid Kurniawan Akrom, Muhamad Aprilah, Thania Ayu Pertiwi Budi, Setyo De Rosal Ignatius Moses Setiadi Dutta, Pushan Kumar Hanny Haryanto Harisa, Ardiawan Bagus Harun Al Azies Hidayat, Novianto Nur Ignatius Moses Setiadi, De Rosal Indra Gamayanto Islam, Hussain Md Mehedul Kusuma, Muh Galuh Surya Putra Minh T. Nguyen, Minh T. Moch Anjas Aprihartha Muhamad Fadil Muljono Muljono Prabowo, Wahyu Aji Eko Prajanto Wahyu Adi, Prajanto Wahyu Safitri, Aprilyani Nur Sasono Wibowo Sendi Novianto Sidauruk, Vanesa Estetika Supriadi Rustad T. Sutojo Trisnapradika, Gustina Alfa Wibawa, Tangkas Surya Zami, Farrikh Al