Computational Materials Science
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In The Last Decade
Computational Materials Science
11.8k papers receiving 320.3k citations
Fields of papers published in Computational Materials Science
This network shows the impact of papers published in Computational Materials Science. 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 Computational Materials Science.
Countries where authors publish in Computational Materials Science
This map shows the geographic impact of research published in Computational Materials Science. 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 Computational Materials Science with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Computational Materials Science more than expected).
- Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set (1996)
- A fast and robust algorithm for Bader decomposition of charge density (2005)
- Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis (2012)
- First-principles computation of material properties: the ABINIT software project (2002)
- MOLCAS: a program package for computational chemistry (2003)
- Computer graphics and graphical user interfaces as tools in simulations of matter at the atomic scale (2003)
- Solid state calculations using WIEN2k (2003)
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.