npj Computational Materials

66.7k citations
1.8k papers · · active since 1950

Impact in

    • Machine Learning in Materials Science
    • 2D Materials and Applications
    • X-ray Diffraction in Crystallography
    • Advanced Thermoelectric Materials and Devices

Papers in

    • Machine Learning in Materials Science 682
    • 2D Materials and Applications 162
    • X-ray Diffraction in Crystallography 161
    • Electronic and Structural Properties of Oxides 129
    • Graphene research and applications 102

npj Computational Materials

1.6k papers receiving 65.7k citations

Peers

npj Computational Materials
Comparison fields: 5 of 213
  • Materials Chemistry 41.6k
  • Structural Biology 643
  • Metals and Alloys 1.1k
  • Electronic, Optical and Magnetic Materials 6.6k
  • Mechanical Engineering 12.0k
Replace Current Opinion in Solid State and Materials Science with:
Current Opinion in Solid State and Materials Science United States
Science and Technology of Advanced Materials Japan
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Modelling and Simulation in Materials Science and Engineering United States
International Materials Reviews United States
Applied Physics Reviews China
Topics in applied physics United States
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Materials Today China
npj Computational Materials relative to Current Opinion in Solid State and Materials Science United States Current Opinion in Solid State and Materials Science's profile →
Citations per field
00.5×2×3×4×4.6×
Current Opinion in Solid State and Materials Science · 1×
Citations per year

Countries where authors publish in npj Computational Materials

Since Specialization
Citations

This map shows the geographic impact of research published in npj Computational Materials. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by papers published in npj Computational Materials with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites npj Computational Materials more than expected).

Fields of papers published in npj Computational Materials

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in npj Computational Materials. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in npj Computational Materials.

About npj Computational Materials

The 1.8k papers published in npj Computational Materials in the last decades have received a total of 66.7k indexed citations . Papers published in npj Computational Materials usually cover Structural Biology (52 papers), Materials Chemistry (1.3k papers), Metals and Alloys (32 papers), Condensed Matter Physics (139 papers) and Electronic, Optical and Magnetic Materials (207 papers) specifically the topics of Machine Learning in Materials Science (682 papers), 2D Materials and Applications (162 papers), X-ray Diffraction in Crystallography (161 papers), Electronic and Structural Properties of Oxides (129 papers), Computational Drug Discovery Methods (104 papers), Graphene research and applications (102 papers), Perovskite Materials and Applications (87 papers) and Topological Materials and Phenomena (81 papers). The most active scholars publishing in npj Computational Materials are Miguel A. L. Marques, Silvana Botti, Jonathan Schmidt, Mário R. G. Marques, Rampi Ramprasad, Alok Choudhary, Ankit Agrawal, Kamal Choudhary, Rohit Batra and Christopher Wolverton.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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