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All Journal Jurnal Simetris Jurnal Transformatika Scientific Journal of Informatics Jurnal Informatika Universitas Pamulang International Journal of Advances in Data and Information Systems Abdimasku : Jurnal Pengabdian Masyarakat JoMMiT : Jurnal Multi Media dan IT MANGGALI Journal of Multiscale Materials Informatics
Achmad Wahid Kurniawan
Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

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Journal : Journal of Multiscale Materials Informatics

 1 
Development of a Machine Learning Model to Predict the Corrosion Inhibition Ability of Benzimidazole Compounds Safitri, Aprilyani Nur; Trisnapradika, Gustina Alfa; Kurniawan, Achmad Wahid; Prabowo, Wahyu AJi Eko; Akrom, Muhamad
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.v1i1.10464

Abstract

The purpose of this study is to use quantitative structure-property relationship (QSPR)-based machine learning (ML) to examine the corrosion inhibition capabilities of benzimidazole compounds. The primary difficulty in ML development is creating a model with a high degree of precision so that the predictions are correct and pertinent to the material's actual attributes. We assess the comparison between the extra trees regressor (EXT) as an ensemble model and the decision tree regressor (DT) as a basic model. It was discovered that the EXT model had better predictive performance in predicting the corrosion inhibition performance of benzimidazole compounds based on the coefficient of determination (R2) and root mean square error (RMSE) metrics compared DT model. This method provides a fresh viewpoint on the capacity of ML models to forecast potent corrosion inhibitors.
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 Astuti, Yani Parti Astuti, Yani Parti Astuti, Yani Parti Ayu Pertiwi Budi Widjajanto Budi, Setyo Harun Al Azies Herawati, Wise Herowati, Wise Herowati, Wise Herowati Hidayat, Novianto Ida Farida Ignatius Moses Setiadi, De Rosal Kurniawan, Setyo Budi Moch Anjas Aprihartha Muljono Muljono Noor Ageng Setiyanto, Noor Ageng Prabowo, Wahyu Aji Eko Safitri, Aprilyani Nur Setiawan, Aries Sudibyo, Usman Sudibyo, Usman Sudibyo, Usman Supriadi Rustad T. Sutojo Trisnapradika, Gustina Alfa Yuniarsi Rahayu