Sina Stocker
Impact in
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- Computational Drug Discovery Methods
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- Catalysis and Oxidation Reactions
Papers in
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- Machine Learning in Materials Science 5
- Catalytic Processes in Materials Science 1
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- Computational Drug Discovery Methods 3
- Co-authors
- Johannes T. Margraf (5 shared papers)Karsten Reuter (4 shared papers)Gábor Cśanyi (2 shared papers)Hyunwook Jung (3 shared papers)Johannes Gasteiger (1 shared paper)Stephan Günnemann (1 shared paper)Florian Becker (1 shared paper)C. Franklin Goldsmith (1 shared paper)
- Journals
- Nature Communications (1 paper)Machine Learning Science and Technology (1 paper)Journal of Chemical Theory and Computation (1 paper)npj Computational Materials (1 paper)ChemSystemsChem (1 paper)
- Partner nations
- GermanyUnited KingdomSouth Korea
In The Last Decade
Sina Stocker
5 papers receiving 277 citations
Peers
Comparison fields: 5 of 47
- Computational Theory and Mathematics 106
- Catalysis 38
- Materials Chemistry 233
- Structural Biology 3
- Renewable Energy, Sustainability and the Environment 27
Countries citing papers authored by Sina Stocker
This map shows the geographic impact of Sina Stocker'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 Sina Stocker with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sina Stocker more than expected).
Fields of papers citing papers by Sina Stocker
This network shows the impact of papers produced by Sina Stocker. 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 Sina Stocker. The network helps show where Sina Stocker may publish in the future.
Co-authors
The 11 scholars most cited alongside Sina Stocker, 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 | 140 | |
| 2 | 2022 | 48 | |
| 3 | 2023 | 43 | |
| 4 | 2023 | 28 | |
| 5 | 2020 | 25 |
About Sina Stocker
Sina Stocker is a scholar working on Materials Chemistry, Computational Theory and Mathematics, Molecular Biology, Atomic and Molecular Physics, and Optics and Artificial Intelligence, having authored 5 papers that have together received 284 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (5 papers), Computational Drug Discovery Methods (3 papers), Protein Structure and Dynamics (2 papers), Catalytic Processes in Materials Science (1 paper), Topic Modeling (1 paper), Innovative Microfluidic and Catalytic Techniques Innovation (1 paper), Advanced Chemical Physics Studies (1 paper) and Catalysis and Hydrodesulfurization Studies (1 paper). The work is most often cited by research in Computational Theory and Mathematics (106 citations), Catalysis (38 citations), Materials Chemistry (233 citations), Structural Biology (3 citations) and Renewable Energy, Sustainability and the Environment (27 citations). Sina Stocker has collaborated with scholars based in Germany, United Kingdom and South Korea. Frequent co-authors include Johannes T. Margraf, Karsten Reuter, Gábor Cśanyi, Hyunwook Jung, Johannes Gasteiger, Stephan Günnemann, Florian Becker, C. Franklin Goldsmith, Harald Oberhofer and Byungchan Han. Their work appears in journals such as Nature Communications, Machine Learning Science and Technology, Journal of Chemical Theory and Computation, npj Computational Materials and ChemSystemsChem.
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.