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
20 Documents
<![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]]>
<![CDATA[Systematic Review of AppSheet-Based Attendance Systems: Outcomes, Features, and Sectoral Adoption ]]>
<![CDATA[Dliyaul Haq, Muh]]>;
<![CDATA[Prasetiyo, Nova Tri]]>;
<![CDATA[Setianto, Aris]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 2 No. 2 (2025): Oktober (in progress) ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v2i2.14636
<![CDATA[The digitalization of organizational processes has accelerated the adoption of efficient attendance management systems. This study systematically reviews AppSheet-based digital attendance implementations, focusing on outcomes, features, and sectoral adoption. Following a PRISMA-guided systematic literature review, 23 studies published between 2020 and 2025 were analyzed. Findings indicate that AppSheet enhances efficiency, accuracy, transparency, and user satisfaction, primarily through mobile access, GPS-based geolocation, QR code verification, and cloud-based reporting. Selfie photo with GPS emerged as the most common feature, followed by QR code–based attendance, while additional functionalities such as digital signatures and form-based attendance provide complementary solutions. Adoption is concentrated in the education sector, particularly primary and secondary schools, with limited uptake in government and corporate contexts. Challenges include scalability limits in free versions, proxy attendance risks, internet dependency, and storage constraints. The study underscores AppSheet’s flexibility as a no-code platform, offering practical, cost-effective, and adaptive solutions for digital attendance management across diverse organizational settings.]]>
<![CDATA[Quantum Neural Network in Architectures, Learning Mechanisms, and Emerging Applications Across Domains: A Review ]]>
<![CDATA[Akrom, Muhamad]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 2 No. 2 (2025): Oktober (in progress) ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v2i2.14929
<![CDATA[Quantum Neural Networks (QNNs) represent a novel computational paradigm that merges the principles of quantum computing with the architecture of artificial neural networks. Through the quantum phenomena of superposition, entanglement, and interference, QNNs enable parallel computation in high-dimensional Hilbert spaces, offering the potential to surpass the representational limits of classical models. This review provides a comprehensive overview of the theoretical foundations and architectures of QNNs, including Quantum Perceptrons, Variational Quantum Circuits (VQCs), Quantum Convolutional Neural Networks (QCNNs), and Quantum Recurrent Neural Networks (QRNNs). Furthermore, it discusses hybrid quantum–classical training mechanisms and key challenges such as barren plateaus, decoherence, and sampling complexity. The review also highlights recent applications of QNNs in medical diagnostics, materials science, and financial forecasting, demonstrating their potential to accelerate computation and improve predictive accuracy. Finally, future research directions are discussed in relation to computational efficiency, model interpretability, and integration with next-generation quantum hardware.]]>
<![CDATA[Variational Quantum Circuits Design Principles, Applications, and Challenges Toward Practical: A Review ]]>
<![CDATA[Akrom, Muhamad]]>;
<![CDATA[Rachman, Dian Arif]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 2 No. 2 (2025): Oktober (in progress) ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v2i2.14935
<![CDATA[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]]>
