Journal of Multiscale Materials Informatics

About the Journal

1. Journal Title : Journal of Multiscale Materials Informatics (JIMAT)
2. Online ISSN : 3047-5724 
3. Frequency : Twice (April and October) 
4. DOI Prefix : 10.62411/jimat
5. Publisher : Universitas Dian Nuswantoro

Journal of Multiscale Materials Informatics (JIMAT) is a peer-reviewed, open-access, APC-free journal, published 2 times (April and October) in one year, and has ISSN: 3047-5724. 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 analyzing and predicting 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:

• Interdisciplinary research integrating physics, chemistry, biology, mathematics, mechanics, engineering, materials science, and computer science.
• Materials informatics, physics informatics, bioinformatics, chemoinformatics, medical informatics, agri informatics, geoinformatics, astroinformatics, etc.
• Quantum computing, quantum information, quantum simulation, quantum error correction, and quantum sensors and metrology.
• Artificial intelligence, machine learning, and statistical learning to analyze materials data.
• Data mining, big data, and database construction of materials data.
• Data-driven discovery, design, and development of materials.
• Development of software, codes, and algorithms for materials computation and simulation.
• Synergistic approaches combining theory, experiment, computation, and artificial intelligence in materials research.
• Theoretical modeling, numerical analysis, and domain knowledge approaches of materials structure-activity-property relationship.