Viktor Zaverkin
Impact in
-
- Computational Drug Discovery Methods
Papers in
-
- Machine Learning in Materials Science 10
-
- Advanced Chemical Physics Studies 5
- Spectroscopy and Quantum Chemical Studies 3
- Quantum, superfluid, helium dynamics 2
- Co-authors
- Johannes Kästner (14 shared papers)Germán Molpeceres (3 shared papers)Ingo Steinwart (2 shared papers)Federico Errica (2 shared papers)Mathias Niepert (1 shared paper)Francesco Alesiani (2 shared papers)Kenji Furuya (1 shared paper)Fabian Zills (1 shared paper)
- Journals
- Journal of Chemical Theory and Computation (3 papers)Astronomy and Astrophysics (2 papers)The Journal of Chemical Physics (2 papers)npj Computational Materials (2 papers)Physical Chemistry Chemical Physics (1 paper)
- Partner nations
- GermanyJapanNetherlands
In The Last Decade
Viktor Zaverkin
15 papers receiving 341 citations
Peers
Comparison fields: 5 of 43
- Computational Theory and Mathematics 122
- Structural Biology 8
- Materials Chemistry 253
- Spectroscopy 70
- Catalysis 23
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-authors
The 15 scholars most cited alongside Viktor Zaverkin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 80 | |
| 2 | 2023 | 35 | |
| 3 | 2024 | 32 | |
| 4 | 2021 | 32 | |
| 5 | 2022 | 27 | |
| 6 | 2022 | 26 | |
| 7 | 2020 | 25 | |
| 8 | 2023 | 19 | |
| 9 | 2021 | 18 | |
| 10 | 2023 | 18 | |
| 11 | 2021 | 15 | |
| 12 | 2021 | 11 | |
| 13 | 2018 | 9 | |
| 14 | 2025 | 1 | |
| 15 | 2020 | 1 |
About Viktor Zaverkin
Viktor Zaverkin is a scholar working on Materials Chemistry, Atomic and Molecular Physics, and Optics, Computational Theory and Mathematics, Physical and Theoretical Chemistry and Spectroscopy, having authored 15 papers that have together received 349 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (10 papers), Computational Drug Discovery Methods (6 papers), Advanced Chemical Physics Studies (5 papers), Spectroscopy and Quantum Chemical Studies (3 papers), Molecular Spectroscopy and Structure (3 papers), Astrophysics and Star Formation Studies (2 papers), Various Chemistry Research Topics (2 papers) and Quantum, superfluid, helium dynamics (2 papers). The work is most often cited by research in Computational Theory and Mathematics (122 citations), Structural Biology (8 citations), Materials Chemistry (253 citations), Spectroscopy (70 citations) and Catalysis (23 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, Mathias Niepert, Francesco Alesiani, Kenji Furuya, Fabian Zills, Makoto Takamoto and Naoki Watanabe. Their work appears in journals such as Journal of Chemical Theory and Computation, Astronomy and Astrophysics, The Journal of Chemical Physics, npj Computational Materials 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.