Gábor Cśanyi
Impact in
- Materials Chemistry top 0.1%
- Machine Learning in Materials Science
- X-ray Diffraction in Crystallography
- Graphene research and applications
- Carbon Nanotubes in Composites
- Computational Theory and Mathematics top 0.05%
- Computational Drug Discovery Methods
Papers in
- Co-authors
- Albert P. BartókRisi KondorVolker L. DeringerM. C. PayneMichele CeriottiNoam BernsteinA. MiguelSandip De
- Journals
- Physical Review Letters (16 papers)The Journal of Chemical Physics (15 papers)npj Computational Materials (10 papers)Journal of Chemical Theory and Computation (10 papers)Physical review. B. (7 papers)
- Partner nations
- United KingdomUnited StatesGermany
In The Last Decade
Gábor Cśanyi
171 papers receiving 18.0k citations
Hit Papers
Peers
Comparison fields: 5 of 155
- Materials Chemistry 14.8k
- Computational Theory and Mathematics 3.7k
- Structural Biology 253
- Metals and Alloys 435
- Catalysis 794
Countries citing papers authored by Gábor Cśanyi
This map shows the geographic impact of Gábor Cśanyi'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 Gábor Cśanyi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gábor Cśanyi more than expected).
Fields of papers citing papers by Gábor Cśanyi
This network shows the impact of papers produced by Gábor Cśanyi. 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 Gábor Cśanyi. The network helps show where Gábor Cśanyi may publish in the future.
Co-authors
The 25 scholars most cited alongside Gábor Cśanyi, 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 | 2025 | 0 | |
| 2 | 2025 | 4 | |
| 3 | 2025 | 5 | |
| 4 | 2024 | 8 | |
| 5 | 2024 | 19 | |
| 6 | 2024 | 15 | |
| 7 | 2024 | 10 | |
| 8 | 2023 | 38 | |
| 9 | 2023 | 40 | |
| 10 | 2023 | 28 | |
| 11 | 2023 | 51 | |
| 12 | 2021 | 12 | |
| 13 | 2020 | 79 | |
| 14 | 2020 | 207 | |
| 15 | 2020 | 32 | |
| 16 | 2018 | 5 | |
| 17 | 2018 | 89 | |
| 18 | 2017 | 84 | |
| 19 | DNest3: Diffusive Nested Sampling | 2010 | 3 |
| 20 | Multiscale modeling of defects in semiconductors : a novel molecular-dynamics scheme | 2007 | 1 |
About Gábor Cśanyi
Gábor Cśanyi is a scholar working on Structural Biology, Metals and Alloys, Materials Chemistry, Computational Theory and Mathematics and Atomic and Molecular Physics, and Optics, having authored 175 papers that have together received 18.3k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (102 papers), Computational Drug Discovery Methods (33 papers), X-ray Diffraction in Crystallography (27 papers), Advanced Chemical Physics Studies (25 papers), Protein Structure and Dynamics (19 papers), Graphene research and applications (14 papers), Spectroscopy and Quantum Chemical Studies (11 papers) and Crystallography and molecular interactions (10 papers). The work is most often cited by research in Materials Chemistry (14.8k citations), Computational Theory and Mathematics (3.7k citations), Structural Biology (253 citations), Metals and Alloys (435 citations) and Catalysis (794 citations). Gábor Cśanyi has collaborated with scholars based in United Kingdom, United States and Germany. Frequent co-authors include Albert P. Bartók, Risi Kondor, Volker L. Deringer, M. C. Payne, Michele Ceriotti, Noam Bernstein, A. Miguel, Sandip De, Andrea C. Ferrari and James R. Kermode. Their work appears in journals such as Physical Review Letters, The Journal of Chemical Physics, npj Computational Materials, Journal of Chemical Theory and Computation and Physical review. B..
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.