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All Journal BERKALA FISIKA Techno.Com: Jurnal Teknologi Informasi Jurnal Fisika Unand Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Journal of Multiscale Materials Informatics
Dian Arif Rachman
Program Studi Magister Ilmu Fisika, Universitas Diponegoro Semarang
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Public Health

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Journal : Journal of Multiscale Materials Informatics

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Ensemble Learning Model in Predicting Corrosion Inhibition Capability of Pyridazine Compounds Rachman, Dian Arif; 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.v1i1.10502

Abstract

Empirical studies of possible compound corrosion inhibitors require a lot of money, time, and resources. Therefore, we used a machine learning (ML) paradigm based on quantitative structure-property relationship (QSPR) models to evaluate ensemble algorithms as predictors of corrosion inhibition efficiency (CIE) values. Our investigation reveals that the gradient boosting (GB) regressor model outperforms other ensemble-based models. This advantage is evaluated objectively using the metrics root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). In summary, our research provides a new perspective on how well machine learning algorithms in particular ensembles work to identify organic molecules such as pyridazine that have the potential to prevent corrosion on the surfaces of metals such as iron and its alloys.
Variational Quantum Circuits Design Principles, Applications, and Challenges Toward Practical: A Review Akrom, Muhamad; Rachman, Dian Arif
Journal of Multiscale Materials Informatics Vol. 2 No. 2 (2025): Oktober (in progress)
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v2i2.14935

Abstract

Variational Quantum Circuits (VQCs) have emerged as a cornerstone of hybrid quantum–classical algorithms designed to harness the computational potential of near-term quantum devices. By combining parameterized quantum gates with classical optimization, VQCs provide a flexible framework for tackling machine learning, chemistry, and optimization problems intractable for classical methods. This review comprehensively overviews VQC design principles, ansatz structures, optimization strategies, and real-world applications. Furthermore, we discuss fundamental challenges such as barren plateaus, the expressibility–trainability trade-off, and current noisy intermediate-scale quantum (NISQ) hardware limitations. Finally, we highlight emerging directions that could enable scalable, noise-resilient, and physically interpretable variational quantum models for future quantum computing applications
Co-Authors Abdul Hakim Prima Yuniarto Agustina, Elvinda Bendra Akrom, Muhamad Artamevira, Jessika Elvinda Bendra Agustina Endang Kusdiyantini Feriawan, Feriawan Fitri, Lia Ikhlasia Karimah, Imroatul Lestiyanti, Yuni Muhammad Nur Nofillah, Recha Sari, Atika Windra Tachriri, Yusril Ihza