Modelling and Simulation in Materials Science and Engineering

SJR Rank #12,196H-index: 100SJR: 0.486Best quartile: Q2APC: US$3,325
Journal website: aims, scope and author guidelines
Publisher

IOP Publishing Ltd.

Country

United Kingdom

Type

journal

SJR by Edition

2022202500.150.30.450.6

Subject Categories

  • Computer Science ApplicationsQ2
  • Condensed Matter PhysicsQ2
  • Materials Science (miscellaneous)Q2
  • Mechanics of MaterialsQ2
  • Modeling and SimulationQ2

Areas: Computer Science · Engineering · Materials Science · Mathematics · Physics and Astronomy

Most published research topics

Microstructure and mechanical propertiesAluminum Alloy Microstructure PropertiesSolidification and crystal growth phenomenaMetallurgy and Material FormingMetal and Thin Film MechanicsHigh Temperature Alloys and Creepnanoparticles nucleation surface interactionsMicrostructure and Mechanical Properties of SteelsMetal Forming Simulation TechniquesAluminum Alloys Composites Properties

Source: OpenAlex

Publication Statistics

2025 Docs3-Year DocsCitable Docs095190285380

Documents per Year

2022202503570105140

Detailed Information

ISSN

0965-0393, 1361-651X

SJR Best Quartile

Q2

Region

Western Europe

Citations per Document (2 years)

2.30

References per Document

51.51

Coverage

1992-2025

Scope

Modelling and Simulation in Materials Science and Engineering™ (MSMSE) is an international journal serving the multidisciplinary materials science community, publishing new research on novel modelling strategies and/or simulation tools to establish the processing-structure-properties link in materials from atomic to macroscale. In particular, the journal welcomes submissions that: -Address innovative theoretical or computational studies of material structure and behaviour, examining materials evolution at the electronic-, atomic- and meso-scale, in relation to mechanical, chemical, electronic, optical, biological or biomedical properties. -Develop innovative multiscale modelling strategies (atomistic-continuum, transition state theory, homogenization, etc). -Apply machine learning, learning systems, and data-driven tools for microstructural analysis, processing and properties simulation and materials discovery. -Uses new software-enabled methods, analysis tools, cyberinfrastructure, high-throughput computational strategies for materials characterization, discovery and design.

Metrics from SCImago Journal Rank 2025. View on SCImago