npj Computational Materials

SJR Rank #738H-index: 113SJR: 2.943Best quartile: Q1Open AccessAPC: US$3,490Listed in DOAJ
Journal website: aims, scope and author guidelines
Publisher

Nature Publishing Group

Country

United Kingdom

Type

journal

SJR by Edition

2022202500.751.52.253

Subject Categories

  • Computer Science ApplicationsQ1
  • Materials Science (miscellaneous)Q1
  • Mechanics of MaterialsQ1
  • Modeling and SimulationQ1

Areas: Computer Science · Engineering · Materials Science · Mathematics

Most published research topics

Machine Learning in Materials Science2D Materials and ApplicationsX-ray Diffraction in CrystallographyElectronic and Structural Properties of OxidesComputational Drug Discovery MethodsTopological Materials and PhenomenaGraphene research and applicationsAdvanced Electron Microscopy Techniques and ApplicationsMultiferroics and related materialsThermal properties of materials

Source: OpenAlex

Publication Statistics

2025 Docs3-Year DocsCitable Docs0200400600800

Documents per Year

20222025095190285380

Detailed Information

ISSN

2057-3960

SJR Best Quartile

Q1

Region

Western Europe

Citations per Document (2 years)

11.81

References per Document

67.27

Coverage

2015-2026

Scope

npj Computational Materials publishes high-quality research papers that apply computational approaches for the design of new materials, and for enhancing our understanding of existing ones. New computational techniques – and the refinement of current approaches – that facilitate these aims are also welcome, as are experimental papers that complement computational findings. Representative journal scope includes: application and development of existing and emerging theoretical and simulation approaches for the study of materials in their entirety (organic and inorganic). structure and property prediction of new materials, such as by the ‘materials by design’ paradigm. significant new understanding of materials fundamentals, behavior and properties. integrative/complementary experimental works that serve to validate, support and extend computational findings. high-throughput techniques – both computational and experimental – for large data set generation, and materials data mining.

Metrics from SCImago Journal Rank 2025. View on SCImago