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[A Machine Learning Model for Evaluation of the Corrosion Inhibition Capacity of Quinoxaline Compounds ]]>
<![CDATA[Setiyanto, Noor Ageng]]>;
<![CDATA[Azies, Harun Al]]>;
<![CDATA[Sudibyo, Usman]]>;
<![CDATA[Pertiwi, Ayu]]>;
<![CDATA[Budi, Setyo]]>;
<![CDATA[Akrom, Muhamad]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 1 No. 1 (2024): April ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v1i1.10429
<![CDATA[Investigating potential corrosion inhibitors via empirical research is a labor- and resource-intensive process. In this work, we evaluated various linear and non-linear algorithms as predictive models for corrosion inhibition efficiency (CIE) values using a machine learning (ML) paradigm based on the quantitative structure-property relationship (QSPR) model. In the quinoxaline compound dataset, our analysis showed that the XGBoost model performed the best predictor of other ensemble-based models. The coefficient of determination (R2), mean absolute percentage error (MAPE), and root mean squared error (RMSE) metrics were used to objectively assess this superiority. To sum up, our study offers a fresh viewpoint on the effectiveness of machine learning algorithms in determining the ability of organic compounds like quinoxaline to suppress corrosion on iron surfaces.]]>
<![CDATA[Investigation of Corrosion Inhibition Efficiency of Pyridine-Quinoline Compounds through Machine Learning ]]>
<![CDATA[Herowati, Wise]]>;
<![CDATA[Akrom, Muhamad]]>;
<![CDATA[Hidayat, Novianto Nur]]>;
<![CDATA[Sutojo, Totok]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 1 No. 1 (2024): April ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v1i1.10448
<![CDATA[Corrosion in materials is a significant concern for the industrial and academic fields because corrosion causes enormous losses in various fields such as the economy, environment, society, industry, security, safety, and others. Currently, material damage control using organic compounds has become a popular field of study. Pyridine and quinoline stand out as corrosion inhibitors among a myriad of organic compounds because they are non-toxic, inexpensive, and effective in a variety of corrosive environments. Experimental investigations in developing various candidate potential inhibitor compounds are time and resource-intensive. In this work, we use a quantitative structure-property relationship (QSPR)-based machine learning (ML) approach to investigate support vector machine (SVR), random forest (RF), and k-nearest neighbors (KNN) algorithms as predictive models of inhibition performance. (Inhibition efficiency) corrosion of pyridine-quinoline derivative compounds as corrosion inhibitors on iron. We found that the RF model showed the best predictive ability based on the coefficient of determination (R2) and root mean squared error (RMSE) metrics. Overall, our study provides new insights regarding the ML model in predicting corrosion inhibition on iron surfaces.]]>
<![CDATA[Development of a Machine Learning Model to Predict the Corrosion Inhibition Ability of Benzimidazole Compounds ]]>
<![CDATA[Safitri, Aprilyani Nur]]>;
<![CDATA[Trisnapradika, Gustina Alfa]]>;
<![CDATA[Kurniawan, Achmad Wahid]]>;
<![CDATA[Prabowo, Wahyu AJi Eko]]>;
<![CDATA[Akrom, Muhamad]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 1 No. 1 (2024): April ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v1i1.10464
<![CDATA[The purpose of this study is to use quantitative structure-property relationship (QSPR)-based machine learning (ML) to examine the corrosion inhibition capabilities of benzimidazole compounds. The primary difficulty in ML development is creating a model with a high degree of precision so that the predictions are correct and pertinent to the material's actual attributes. We assess the comparison between the extra trees regressor (EXT) as an ensemble model and the decision tree regressor (DT) as a basic model. It was discovered that the EXT model had better predictive performance in predicting the corrosion inhibition performance of benzimidazole compounds based on the coefficient of determination (R2) and root mean square error (RMSE) metrics compared DT model. This method provides a fresh viewpoint on the capacity of ML models to forecast potent corrosion inhibitors.]]>
<![CDATA[Green Corrosion Inhibitors for Iron Alloys: A Comprehensive Review of Integrating Data-Driven Forecasting, Density Functional Theory Simulations, and Experimental Investigation ]]>
<![CDATA[Akrom, Muhamad]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 1 No. 1 (2024): April ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v1i1.10495
<![CDATA[This comprehensive review delves into the realm of green corrosion inhibitors for iron alloys, focusing on a thorough exploration guided by data-driven investigation, density functional theory (DFT) simulations, and experimental validation. Harnessing the potential of plant extracts, this study scrutinizes their effectiveness in mitigating corrosion in iron alloys through a multi-faceted approach. By integrating computational modeling with empirical experimentation, a deeper understanding of the inhibitive mechanisms is achieved, offering insights into their practical application. The review synthesizes findings from diverse studies, elucidating the pivotal role of DFT in predicting inhibitor behavior and optimizing their performance. Furthermore, experimental validation provides crucial validation of theoretical predictions, highlighting the synergistic relationship between simulation and real-world application. Through this journey of exploration, the review underscores the promise of green corrosion inhibitors derived from natural sources, paving the way for sustainable corrosion control practices in the realm of iron alloys.]]>
<![CDATA[Ensemble Learning Model in Predicting Corrosion Inhibition Capability of Pyridazine Compounds ]]>
<![CDATA[Rachman, Dian Arif]]>;
<![CDATA[Akrom, Muhamad]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 1 No. 1 (2024): April ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v1i1.10502
<![CDATA[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.]]>
<![CDATA[Comparison of Ridge and Kernel Ridge Models in Predicting Thermal Stability of Zn-MOF Catalysts ]]>
<![CDATA[Trisnapradika, Gustina Alfa]]>;
<![CDATA[Akrom, Muhamad]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 1 No. 1 (2024): April ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v1i1.10542
<![CDATA[This study investigates machine learning-based quantitative structure-property relationship (QSPR) models for predicting the thermal stability of zinc metal-organic frameworks (Zn-MOF). Utilizing a dataset comprising 151 Zn-MOF compounds with relevant molecular descriptors, ridge (R) and kernel ridge (KR) regression models were developed and evaluated. The results demonstrate that the R model outperforms the KR model in terms of prediction accuracy, with the R model exhibiting exceptional performance (R² = 0.999, RMSE = 0.0022). While achieving high accuracy, opportunities for further improvement exist through hyperparameter optimization and exploration of polynomial functions. This research underscores the potential of ML-based QSPR models in predicting the thermal stability of Zn-MOF compounds and highlights avenues for future investigation to enhance model accuracy and applicability in materials science.]]>
<![CDATA[Quantum Support Vector Machine for Classification Task: A Review ]]>
<![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.v1i2.10965
<![CDATA[Quantum computing has emerged as a promising technology capable of solving complex computational problems more efficiently than classical computers. Among the various quantum algorithms developed, the Quantum Support Vector Machine (QSVM) has gained significant attention for its potential to enhance machine learning tasks, particularly classification. This review paper explores the theoretical foundations, methodologies, and potential advantages of QSVM for classification tasks. We discuss the quantum computing principles underpinning QSVM, compare them with classical support vector machines, and review recent advancements and applications. Finally, we highlight the challenges and prospects of QSVM in the context of quantum machine learning.]]>
<![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.]]>
