Machine learning of accurate energy-conserving molecular force fields
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doi.org/10.1126/sciadv.1603015 →Countries where authors are citing Machine learning of accurate energy-conserving molecular force fields
This map shows the geographic impact of Machine learning of accurate energy-conserving molecular force fields. 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 Machine learning of accurate energy-conserving molecular force fields with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Machine learning of accurate energy-conserving molecular force fields more than expected).
Fields of papers citing Machine learning of accurate energy-conserving molecular force fields
This network shows the impact of Machine learning of accurate energy-conserving molecular force fields. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Machine learning of accurate energy-conserving molecular force fields.
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.1603015.