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
18 Documents
<![CDATA[Variational quantum algorithm for forecasting drugs for corrosion inhibitor ]]>
<![CDATA[Rosyid, Muhammad Reesa]]>;
<![CDATA[Akrom, Muhamad]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 1 No. 2 (2024): October ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v1i2.11425
<![CDATA[This study explores the development and evaluation of a Variational Quantum Algorithm (VQA) for predicting a drug as a corrosion inhibitor, highlighting its advantages over traditional regression models. The VQA leverages quantum-enhanced feature mapping and optimization techniques to capture complex, non-linear relationships within the data. Comparative analysis with AutoRegressive with exogenous inputs (ARX) and Gradient Boosting (GB) models demonstrate the superior performance of VQA across key metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Deviation (MAD). The VQA achieved the lowest RMSE (4.40), MAE (3.33), and MAD (3.17) values, indicating enhanced predictive accuracy and stability. These results underscore the potential of quantum machine learning techniques in advancing predictive modeling capabilities, offering significant improvements in accuracy and consistency over classical methods. The findings suggest that VQA is a promising approach for applications requiring high precision and reliability, paving the way for broader adoption of quantum-enhanced models in material science and beyond.]]>
<![CDATA[Quantum support vector regression for predicting corrosion inhibition of drugs ]]>
<![CDATA[Santosa, Akbar Priyo]]>;
<![CDATA[Akrom, Muhamad]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 1 No. 2 (2024): October ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v1i2.11427
<![CDATA[This study evaluates the performance of Quantum Support Vector Regression (QSVR) in predicting material properties using limited data. Experimental results show that the QSVR model consistently produces superior prediction accuracy compared to previous conventional regression models. This improvement is especially evident in the prediction accuracy for small and complex datasets, where QSVR can better capture non-linear patterns. The superiority of QSVR in processing data with a quantum approach provides great potential in developing predictive models in materials science and computational chemistry.]]>
<![CDATA[Comparative Study of Classical, Quantum, and Hybrid Stacking Models for Predicting Corrosion Inhibition Efficiency Using QSAR Descriptors ]]>
<![CDATA[Herowati, Wise]]>;
<![CDATA[Akrom, Muhamad]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 2 No. 1 (2025): April ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v2i1.12217
<![CDATA[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.]]>
<![CDATA[Towards intelligent post-quantum security: a machine learning approach to FrodoKEM, Falcon, and SIKE ]]>
<![CDATA[Akrom, Muhamad]]>;
<![CDATA[Setiadi, De Rosal Ignatius Moses]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 2 No. 1 (2025): April ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v2i1.12865
<![CDATA[The rapid advancement of quantum computing poses a substantial threat to classical cryptographic systems, accelerating the global shift toward post-quantum cryptography (PQC). Despite their theoretical robustness, practical deployment of PQC algorithms remains hindered by challenges such as computational overhead, side-channel vulnerabilities, and poor adaptability to dynamic environments. This study integrates machine learning (ML) techniques to enhance three representative PQC algorithms: FrodoKEM, Falcon, and Supersingular Isogeny Key Encapsulation (SIKE). ML is employed for four key purposes: performance optimization through Bayesian and evolutionary parameter tuning; real-time side-channel leakage detection using deep learning models; dynamic algorithm switching based on runtime conditions using reinforcement learning; and cryptographic forensics through anomaly detection on vulnerable implementations. Experimental results demonstrate up to 23.6% reduction in key generation time, over 96% accuracy in side-channel detection, and significant gains in adaptability and leakage resilience. ML models also identified predictive patterns of cryptographic fragility in the now-broken SIKE protocol. These findings confirm that machine learning augments performance and security and enables intelligent and adaptive cryptographic infrastructures for the post-quantum era.]]>
<![CDATA[Synergizing Quantum Computing and Machine Learning: A Pathway Toward Quantum-Enhanced Intelligence ]]>
<![CDATA[Trisnapradika, Gustina Alfa]]>;
<![CDATA[Akrom, Muhamad]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 2 No. 1 (2025): April ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v2i1.12947
<![CDATA[The convergence of quantum computing and artificial intelligence has introduced a new paradigm in computational science known as Quantum Artificial Intelligence (QAI). By leveraging quantum mechanical principles such as superposition, entanglement, and quantum parallelism, QAI aims to overcome the limitations of classical machine learning, particularly in handling high-dimensional data, complex optimization, and scalability issues. This paper presents a comprehensive review of foundational concepts in both classical machine learning and quantum computing, followed by an in-depth discussion of emerging quantum algorithms tailored for AI applications, such as quantum neural networks, quantum support vector machines, and variational quantum classifiers. We explore the practical implications of these approaches across key sectors, including healthcare, finance, cybersecurity, and logistics. Furthermore, we identify critical challenges related to hardware limitations, algorithmic stability, data encoding, and ethical considerations. Finally, we outline research directions necessary to advance the field, highlighting the transformative potential of QAI in shaping the next generation of intelligent technologies]]>
<![CDATA[Layerwise Quantum Training: A Progressive Strategy for Mitigating Barren Plateaus in Quantum Neural Networks ]]>
<![CDATA[Al Azies, Harun]]>;
<![CDATA[Akrom, Muhamad]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 2 No. 1 (2025): April ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v2i1.12948
<![CDATA[Barren plateaus (BP) remain a core challenge in training quantum neural networks (QNN), where gradient vanishing hinders convergence. This paper proposes a layerwise quantum training (LQT) strategy, which trains parameterized quantum circuits (PQC) incrementally by optimizing each layer separately. Our approach avoids deep circuit initialization by gradually constructing the QNN. Experimental results demonstrate that LQT mitigates the onset of barren plateaus and enhances convergence rates compared to conventional and residual-based QNN, rendering it a scalable alternative for Noisy Intermediate-Scale Quantum (NISQ)-era quantum devices.]]>
<![CDATA[Tree Tensor Network Quantum-Classical Hybrid Neural Architecture for Efficient Data Classification ]]>
<![CDATA[Hidayat, Novianto Nur]]>;
<![CDATA[Akrom, Muhamad]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 2 No. 1 (2025): April ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v2i1.12949
<![CDATA[We introduce the Tree Tensor Network-enhanced Quantum-Classical Neural Network (TTN-QNet), a hybrid architecture that leverages the hierarchical structure of Tree Tensor Networks for efficient parameter representation and Variational Quantum Circuits (VQC) for expressive modeling. Unlike Tensor Ring Networks, TTNs reduce parameter redundancy through a tree-based topology, enabling scalable and interpretable computation. The proposed TTN-QNet is evaluated on the Iris, MNIST, and CIFAR-10 datasets, achieving classification accuracies of 93.2%, 85.24%, and 81.67%, respectively, on binary classification tasks. TTN-QNet demonstrates rapid convergence and robustness against barren plateaus, offering a promising direction for deep quantum learning.]]>
<![CDATA[Evaluating Gate-Based Quantum Machine Learning Models on Quantum Chemistry Datasets ]]>
<![CDATA[Prabowo, Wahyu Aji Eko]]>;
<![CDATA[Akrom, Muhamad]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 2 No. 1 (2025): April ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v2i1.12950
<![CDATA[This study evaluates gate-based quantum machine learning (QML) models, including the Variational Quantum Classifier (VQC) and Quantum k-Nearest Neighbors (QkNN), on the QM9 quantum chemistry dataset for binary classification of molecular electronic properties. Using IBM Qiskit, both models were tested on simulators and real quantum hardware. Classical models (LightGBM, SVM, MLP) served as benchmarks. Results show classical models outperform quantum ones, with LightGBM achieving the highest AUC-ROC (0.901). However, VQC on simulators achieved a competitive AUC of 0.781, and real hardware still yielded performance above that of chance. Despite hardware constraints, quantum models demonstrated learning capability. The findings support hybrid quantum-classical systems as a promising near-term approach while quantum hardware continues to evolve]]>
