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.
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<![CDATA[XGBoost performance in predicting corrosion inhibition efficiency of Benzimidazole Compounds ]]>
<![CDATA[Ningtias, Diah Rahayu]]>;
<![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.v1i2.11021
<![CDATA[In this study, we compare the performance of the XGBoost model with a Support Vector Machine (SVM) model from the literature in predicting a given task. Performance metrics such as the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) were utilized to evaluate and compare the models. The XGBoost model achieved an R² of 0.99, an RMSE of 2.54, and an MAE of 1.96, significantly outperforming the SVM model, which recorded an R² of 0.96 and an RMSE of 6.79. The scatter plot for the XGBoost model further illustrated its superior performance, showing a tight clustering of points around the ideal line (y = x), indicating high accuracy and low prediction errors. These findings suggest that the XGBoost model is highly effective for the given prediction task, likely due to its ability to capture complex patterns and interactions within the data.]]>
<![CDATA[Investigation of an amino acid compound as a corrosion inhibitor via ensemble learning ]]>
<![CDATA[Lingga Dewi, Adhe]]>;
<![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.v1i2.11053
<![CDATA[In this study, we evaluate the performance of various machine learning models, including Random Forest (RF), Bagging (BAG), AdaBoost (ADA), Artificial Neural Network (ANN), and Support Vector Machine (SVM), using metrics such as R², Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The results indicate that AdaBoost (ADA) achieves the highest performance with an R² of 0.999, RMSE of 2.32, and MAE of 2.24, making it the most accurate model with the smallest prediction errors. Bagging (BAG) also performs exceptionally well, with an R2 of 0.996, RMSE of 3.09, and MAE of 2.92. The Artificial Neural Network (ANN) exhibits a high R2 of 0.999, though RMSE and MAE values are not provided. Random Forest (RF) and Support Vector Machine (SVM) show good performance with R² values of 0.982 and 0.970, respectively, but are outperformed by the ensemble methods. The findings underscore the superiority of ensemble techniques, particularly AdaBoost, in achieving high predictive accuracy and minimal errors in this context.]]>
<![CDATA[Enhancing School Waste Management with EcoViber Using the Waterfall Approach ]]>
<![CDATA[Dliyaul Haq, Muh]]>;
<![CDATA[Hanif, Nayudin]]>;
<![CDATA[Rokhman, Ayla Yuli]]>;
<![CDATA[Sukinawan, Kiki]]>
<![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.v1i2.11108
<![CDATA[The research focuses on the development and implementation of the EcoViber application to address plastic waste management in school environments, specifically at SDN Perdopo 02. The study aims to tackle the issue of plastic waste accumulation through technological solutions, enhancing environmental awareness and operational efficiency. The application employs the waterfall method for development and features data collection, visualization, and educational tools to promote transparency and accountability. Comprehensive observations and interviews with seven teachers revealed significant improvements in waste management, with approximately 54.95 kg of plastic waste collected from January to April 2024. The findings highlight the practical implications of EcoViber in fostering a culture of environmental stewardship among school stakeholders. Despite its success, the study acknowledges potential biases in data reporting and the limited generalizability of results due to the focus on a single school. Future research should explore diverse settings and refine methodologies to enhance the scalability and long-term impact of EcoViber. Overall, the study demonstrates EcoViber's value as an innovative and effective solution for sustainable plastic waste management in educational settings.]]>
<![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.]]>
