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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Voice Of Informatics Abdimasku : Jurnal Pengabdian Masyarakat BERNAS: Jurnal Pengabdian Kepada Masyarakat Journal of Multiscale Materials Informatics Journal of Future Artificial Intelligence and Technologies
T. Sutojo
Program Studi Teknik Informatika, Fakultas Ilmu Komputer Universitas Dian Nuswantoro
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Neuroscience Nursing Public Health Social Sciences Veterinary Other

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Journal : Journal of Multiscale Materials Informatics

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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.
Machine Learning-Assisted Prediction of Oxygen Evolution Reaction (OER) Activity for Catalyst Discovery: A Review Herowati, Wise; Akrom, Muhamad; Sutojo, Totok; Kurniawan, Achmad Wahid
Journal of Multiscale Materials Informatics Vol. 3 No. 1 (2026): April (In Progress)
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v3i1.15917

Abstract

The Oxygen Evolution Reaction (OER) is a fundamental process in electrochemical water splitting, playing a crucial role in sustainable hydrogen production. However, its intrinsically sluggish kinetics, involving complex four-electron transfer steps, remain a major bottleneck for efficient energy conversion. In recent years, Machine Learning (ML) has emerged as a powerful approach to accelerate catalyst discovery by enabling data-driven prediction of OER activity and reducing reliance on costly experimental and density functional theory (DFT) calculations. This review systematically summarizes recent advances in ML-assisted OER research, focusing on key aspects including dataset construction, descriptor engineering, model development, and performance evaluation. Various ML techniques, ranging from traditional algorithms such as Random Forest and Support Vector Machines to advanced deep learning approaches, are critically discussed in the context of catalyst screening and activity prediction. Particular attention is given to the role of physicochemical descriptors, including adsorption energies and electronic structure parameters, in governing model performance and interpretability. Furthermore, this review highlights current challenges, such as data scarcity, lack of standardization, and limited model generalization, while discussing emerging trends including active learning, explainable AI, and integration with high-throughput simulations. By providing a comprehensive overview, this work aims to guide future research toward the development of robust, interpretable, and scalable ML frameworks for accelerating the discovery of efficient OER catalysts.
Co-Authors Achmad Wahid Kurniawan Adhitya Nugraha Akrom, Muhamad Andreas Widiyantoro Astuti, Yani Parti Ayu Pertiwi Budi, Setyo Catur Supriyanto De Rosal Ignatius Moses Setiadi Dewi Agustini Santoso Egia Rosi Subhiyakto, Egia Rosi Erlin Dolphina Erna Zuni Astuti Etika Kartikadarma Fauzi Adi Rafrastara Hanny Haryanto Harun Al Azies Heribertus Himawan Herowati, Wise Hidayat, Novianto Nur Ifan Rizqa Ignatius Moses Setiadi, De Rosal Irnanda, Muhammad Diva Liya Umaroh, Liya Moch Anjas Aprihartha Muljono Muljono Mulyanto, Edy Noor Ageng Setiyanto, Noor Ageng Oktaviansyah, Brenendra Putra Pratama, Ananta Surya Rahmadianto, Rizky Setiawan, Marcell Adi Sudaryanto Sudaryanto Sugiyanto - Supriadi Rustad Suria Sandi Winarto Trisnapradika, Gustina Alfa Umam, Taufiqul Wisinggya, Kadhana Reya