Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments
- Journal
- Science Advances
In The Last Decade
doi.org/10.1126/sciadv.adn4397 →Countries where authors are citing Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments
This map shows the geographic impact of Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments. 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 Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments more than expected).
Fields of papers citing Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments
This network shows the impact of Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments.
About Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments
This paper, published in 2024, received 67 indexed citations . Written by Oliver T. Unke, Martin Stöhr, Stefan Ganscha, Thomas Unterthiner, Sergii Kashubin, Michael Gastegger, Leonardo Medrano Sandonas, Joshua T. Berryman, Alexandre Tkatchenko and Klaus‐Robert Müller covering the research area of Molecular Biology, Materials Chemistry and Structural Biology. It is primarily cited by scholars working on Materials Chemistry (49 citations), Computational Theory and Mathematics (30 citations) and Molecular Biology (28 citations). Published in Science Advances.
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.
This paper is also available at doi.org/10.1126/sciadv.adn4397.