Viktor Zaverkin
- Materials Chemistry
- Computational Theory and Mathematics top 5%
- Atomic and Molecular Physics, and Optics
- Spectroscopy
- Molecular Biology
- Co-authors
- Johannes KästnerGermán MolpeceresIngo SteinwartFederico ErricaPrashanth SrinivasanBlazej GrabowskiNaoki WatanabeMathias Niepert
- Topics
- Machine Learning in Materials Science (10 papers)Computational Drug Discovery Methods (6 papers)Advanced Chemical Physics Studies (5 papers)
- Journals
- The Journal of Chemical PhysicsMonthly Notices of the Royal Astronomical SocietyPhysical Chemistry Chemical Physics
- Partner nations
- GermanyJapanNetherlands
In The Last Decade
Viktor Zaverkin
13 papers receiving 304 citations
Peers
Comparison fields: 5 of 42
- Materials Chemistry 237
- Computational Theory and Mathematics 120
- Atomic and Molecular Physics, and Optics 82
- Spectroscopy 65
- Molecular Biology 63
Countries citing papers authored by Viktor Zaverkin
This map shows the geographic impact of Viktor Zaverkin's research. 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 Viktor Zaverkin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Viktor Zaverkin more than expected).
Fields of papers citing papers by Viktor Zaverkin
This network shows the impact of papers produced by Viktor Zaverkin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Viktor Zaverkin. The network helps show where Viktor Zaverkin may publish in the future.
Co-authorship network of co-authors of Viktor Zaverkin
This figure shows the co-authorship network connecting the top 25 collaborators of Viktor Zaverkin. A scholar is included among the top collaborators of Viktor Zaverkin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Viktor Zaverkin. Viktor Zaverkin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 25 | |
| 3 | 18 | |
| 4 | 16 | |
| 5 | 32 | |
| 6 | 23 | |
| 7 | 26 | |
| 8 | 16 | |
| 9 | 13 | |
| 10 | 28 | |
| 11 | 11 | |
| 12 | 24 | |
| 13 | 72 | |
| 14 | 9 |
About Viktor Zaverkin
Viktor Zaverkin is a scholar working on Computational Theory and Mathematics, Physical and Theoretical Chemistry and Atomic and Molecular Physics, and Optics, having authored 14 papers that have together received 313 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (10 papers), Computational Drug Discovery Methods (6 papers) and Advanced Chemical Physics Studies (5 papers). The work is most often cited by research in Computational Theory and Mathematics (120 citations), Structural Biology (8 citations) and Materials Chemistry (237 citations). Viktor Zaverkin has collaborated with scholars based in Germany, Japan and Netherlands. Frequent co-authors include Johannes Kästner, Germán Molpeceres, Ingo Steinwart, Federico Errica, Prashanth Srinivasan, Blazej Grabowski, Naoki Watanabe, Mathias Niepert, Francesco Alesiani and Andrew Ian Duff. Their work appears in journals such as The Journal of Chemical Physics, Monthly Notices of the Royal Astronomical Society and Physical Chemistry Chemical Physics.
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.