npj Computational Materials

1.6k papers and 54.3k indexed citations i.

About

The 1.6k papers published in npj Computational Materials in the last decades have received a total of 54.3k indexed citations. Papers published in npj Computational Materials usually cover Materials Chemistry (1.2k papers), Electrical and Electronic Engineering (346 papers) and Atomic and Molecular Physics, and Optics (306 papers) specifically the topics of Machine Learning in Materials Science (625 papers), X-ray Diffraction in Crystallography (169 papers) and 2D Materials and Applications (156 papers). The most active scholars publishing in npj Computational Materials are Miguel A. L. Marques, Silvana Botti, Jonathan Schmidt, Mário R. G. Marques, Chen Ling, Rampi Ramprasad, Rohit Batra, Chiho Kim, Gerbrand Ceder and Chris Wolverton.

In The Last Decade

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.

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).

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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