Simone Sieg
Impact in
- Catalysis top 10%
- Catalysis and Oxidation Reactions
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- Machine Learning in Materials Science
- Catalytic Processes in Materials Science
- Electronic and Structural Properties of Oxides
- X-ray Diffraction in Crystallography
Papers in
-
- Machine Learning in Materials Science 5
- Catalytic Processes in Materials Science 2
- Electronic and Structural Properties of Oxides 1
-
- Catalysis and Oxidation Reactions 3
- Co-authors
- Wilhelm F. Maier (7 shared papers)Klaus Stöwe (2 shared papers)Krishna Rajan (2 shared papers)Changwon Suh (2 shared papers)Timm Schmidt (2 shared papers)Fred A. Hamprecht (1 shared paper)James H. Oliver (1 shared paper)Klaus Stoewe (1 shared paper)
- Journals
- Angewandte Chemie International Edition (1 paper)Journal of Molecular Modeling (1 paper)QSAR & Combinatorial Science (2 papers)Angewandte Chemie (1 paper)ChemInform (1 paper)
- Partner nations
- GermanyUnited States
In The Last Decade
Simone Sieg
7 papers receiving 382 citations
Peers
Comparison fields: 5 of 59
- Catalysis 81
- Materials Chemistry 259
- Renewable Energy, Sustainability and the Environment 50
- Electrochemistry 16
- Inorganic Chemistry 34
Countries citing papers authored by Simone Sieg
This map shows the geographic impact of Simone Sieg'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 Simone Sieg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Simone Sieg more than expected).
Fields of papers citing papers by Simone Sieg
This network shows the impact of papers produced by Simone Sieg. 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 Simone Sieg. The network helps show where Simone Sieg may publish in the future.
Co-authors
The 8 scholars most cited alongside Simone Sieg, 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 | 2007 | 317 | |
| 2 | 2007 | 27 | |
| 3 | 2006 | 25 | |
| 4 | 2009 | 10 | |
| 5 | 2006 | 9 | |
| 6 | 2007 | 4 | |
| 7 | 2007 | 4 |
About Simone Sieg
Simone Sieg is a scholar working on Materials Chemistry, Catalysis, Computational Theory and Mathematics, Inorganic Chemistry and Computer Vision and Pattern Recognition, having authored 7 papers that have together received 396 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (5 papers), Catalysis and Oxidation Reactions (3 papers), Computational Drug Discovery Methods (2 papers), Catalytic Processes in Materials Science (2 papers), Complex Network Analysis Techniques (1 paper), Graph Theory and Algorithms (1 paper), Electronic and Structural Properties of Oxides (1 paper) and Data Visualization and Analytics (1 paper). The work is most often cited by research in Catalysis (81 citations), Materials Chemistry (259 citations), Renewable Energy, Sustainability and the Environment (50 citations), Electrochemistry (16 citations) and Inorganic Chemistry (34 citations). Simone Sieg has collaborated with scholars based in Germany and United States. Frequent co-authors include Wilhelm F. Maier, Klaus Stöwe, Krishna Rajan, Changwon Suh, Timm Schmidt, Fred A. Hamprecht, James H. Oliver and Klaus Stoewe. Their work appears in journals such as Angewandte Chemie International Edition, Journal of Molecular Modeling, QSAR & Combinatorial Science, Angewandte Chemie and ChemInform.
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.