Journal of Multiscale Materials Informatics
Journal of Multiscale Materials Informatics (JIMAT) is a peer-reviewed, open-access, free of APC (until December 2025), and published 2 times (April and October) in one year. JIMAT is an interdisciplinary journal emphasis on cutting-edge research situated at the intersection of materials science and engineering with data science. The journal aims to establish a unified platform catering to researchers utilizing and advancing data-driven methodologies, machine learning (ML), and artificial intelligence (AI) techniques for the analysis and prediction of material properties, behavior, and performance. Our overarching mission is to propel and distribute innovative research that expedites the progress of materials research and discovery through the utilization of data-centric approaches. The journal publishes papers in the areas of, but not limited to: a. Interdisciplinary research integrating physics, chemistry, biology, mathematics, mechanics, engineering, materials science, and computer science. b. Materials informatics, physics informatics, bioinformatics, chemoinformatics, medical informatics, agri informatics, geoinformatics, astroinformatics, etc. c. Quantum computing, quantum information, quantum simulation, quantum error correction, and quantum sensors and metrology. d. Artificial intelligence, machine learning, and statistical learning to analyze materials data. e. Data mining, big data, and database construction of materials data. f. Data-driven discovery, design, and development of materials. g. Development of software, codes, and algorithms for materials computation and simulation. h. Synergistic approaches combining theory, experiment, computation, and artificial intelligence in materials research. i. Theoretical modeling, numerical analysis, and domain knowledge approaches of materials structure-activity-property relationship.
Articles
23 Documents
<![CDATA[Framework for Early Prediction of Lithium-Ion Battery Lifetime: A Hybrid Quantum-Classical Approach ]]>
<![CDATA[Anggita, Sheilla Rully]]>;
<![CDATA[Akrom, Muhamad]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 2 No. 2 (2025): Oktober ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v2i2.15055
<![CDATA[Accurately predicting the lifetime of lithium-ion batteries during early charge–discharge cycles remains a significant challenge due to the nonlinear and weakly expressed degradation dynamics in the initial stages of operation. Classical machine learning (ML) models—although effective in pattern recognition—often face limitations in modeling complex correlations within small, high-dimensional datasets. To address these challenges, this study proposes a Hybrid Quantum–Classical Machine Learning (HQML) framework that integrates a Variational Quantum Circuit (VQC) as a quantum feature encoder with a Gradient Boosting Regressor (GBR) as the classical learner. The proposed approach is implemented using the Qiskit Aer simulator on the MIT Battery Degradation Dataset (124 cells, 42 engineered features). By encoding multi-source degradation descriptors (voltage, capacity, temperature, internal resistance) into Hilbert space via amplitude and angle encoding, the HQML model captures intricate nonlinear feature interactions that are inaccessible to conventional kernels. Experimental results demonstrate that the hybrid model achieves an RMSE of 93 cycles and an R² of 0.94, outperforming the best classical baseline (SVM + Wrapper selection, RMSE = 115, R² = 0.90). Furthermore, quantum observables analysis reveals interpretable correlations between entanglement strengths and physical degradation indicators. These results highlight the potential of quantum machine learning as a powerful paradigm for high-fidelity battery prognostics in the early-life regime.]]>
<![CDATA[Hybrid Quantum Neural Network for Predicting Corrosion Inhibition Efficiency of Organic Molecules ]]>
<![CDATA[Herowati, Wise]]>;
<![CDATA[Akrom, Muhamad]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 2 No. 2 (2025): Oktober ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v2i2.15132
<![CDATA[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.]]>
<![CDATA[Quantum Convolutional Neural Networks: Architectures, Applications, and Future Directions: A Review ]]>
<![CDATA[Trisnapradika, Gustina Alfa]]>;
<![CDATA[Safitri, Aprilyani Nur]]>;
<![CDATA[Hidayat, Novianto Nur]]>;
<![CDATA[Akrom, Muhamad]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 2 No. 2 (2025): Oktober ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v2i2.15154
<![CDATA[Quantum Convolutional Neural Networks (QCNNs) have emerged as one of the most promising architectures in Quantum Machine Learning (QML), enabling hierarchical quantum feature extraction and offering potential advantages over classical CNNs in expressivity and scalability. This study presents a Systematic Literature Review (SLR) on QCNN development from 2019 to 2025, covering theoretical foundations, model architectures, noise resilience, benchmark performance, and applications in materials informatics, chemistry, image recognition, quantum phase classification, and cybersecurity. The SLR followed PRISMA guidelines, screening 214 publications and selecting 47 primary studies. The review finds that QCNNs consistently outperform classical baselines in small-data and high-dimensional regimes due to quantum feature maps and entanglement-driven locality. Significant limitations include noise sensitivity, limited qubit availability, and a lack of standardized datasets for benchmarking. The novelty of this work lies in providing the first comprehensive synthesis of QCNN research across theory, simulations, and real-hardware deployment, offering a roadmap for research gaps and future directions. The findings confirm that QCNNs are strong candidates for NISQ-era applications, especially in physics-informed learning.]]>
