Philipp Eiden
Impact in
- Computational Theory and Mathematics top 0.5%
- Computational Drug Discovery Methods
- Materials Chemistry top 5%
- Machine Learning in Materials Science
- Corrosion Behavior and Inhibition
Papers in
-
- Corrosion Behavior and Inhibition 5
- Machine Learning in Materials Science 4
-
- Concrete Corrosion and Durability 4
- Co-authors
- Miriam Mathea (3 shared papers)Volker Settels (3 shared papers)Klavs F. Jensen (2 shared papers)Regina Barzilay (2 shared papers)Connor W. Coley (2 shared papers)Andrew Palmer (2 shared papers)Kevin Yang (2 shared papers)Kyle Swanson (2 shared papers)
- Journals
- Journal of Chemical Information and Modeling (3 papers)Corrosion Science (3 papers)The Journal of Physical Chemistry B (2 papers)Molecular Systems Design & Engineering (2 papers)
- Partner nations
- GermanyAustraliaUnited States
In The Last Decade
Philipp Eiden
9 papers receiving 1.2k citations
Philipp Eiden's Hit Papers
Peers
Comparison fields: 5 of 108
- Computational Theory and Mathematics 902
- Materials Chemistry 787
- Metals and Alloys 24
- Molecular Biology 513
- Physical and Theoretical Chemistry 62
Countries citing papers authored by Philipp Eiden
This map shows the geographic impact of Philipp Eiden'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 Philipp Eiden with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philipp Eiden more than expected).
Fields of papers citing papers by Philipp Eiden
This network shows the impact of papers produced by Philipp Eiden. 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 Philipp Eiden. The network helps show where Philipp Eiden may publish in the future.
Co-authors
The 25 scholars most cited alongside Philipp Eiden, 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 | Analyzing Learned Molecular Representations for Property Prediction Hit paper breakdown → | 2019 | 1142 |
| 2 | 2022 | 30 | |
| 3 | 2019 | 27 | |
| 4 | 2024 | 16 | |
| 5 | 2023 | 13 | |
| 6 | 2023 | 13 | |
| 7 | 2023 | 11 | |
| 8 | 2023 | 9 | |
| 9 | 2024 | 2 | |
| 10 | 2025 | 0 |
About Philipp Eiden
Philipp Eiden is a scholar working on Materials Chemistry, Civil and Structural Engineering, Computational Theory and Mathematics, Metals and Alloys and Molecular Biology, having authored 10 papers that have together received 1.3k indexed citations. Recurring topics across this work include Corrosion Behavior and Inhibition (5 papers), Computational Drug Discovery Methods (4 papers), Machine Learning in Materials Science (4 papers), Concrete Corrosion and Durability (4 papers), Hydrogen embrittlement and corrosion behaviors in metals (4 papers), Machine Learning and Data Classification (1 paper), Protein Structure and Dynamics (1 paper) and Various Chemistry Research Topics (1 paper). The work is most often cited by research in Computational Theory and Mathematics (902 citations), Materials Chemistry (787 citations), Metals and Alloys (24 citations), Molecular Biology (513 citations) and Physical and Theoretical Chemistry (62 citations). Philipp Eiden has collaborated with scholars based in Germany, Australia and United States. Frequent co-authors include Miriam Mathea, Volker Settels, Klavs F. Jensen, Regina Barzilay, Connor W. Coley, Andrew Palmer, Kevin Yang, Kyle Swanson, Hua Gao and Wengong Jin. Their work appears in journals such as Journal of Chemical Information and Modeling, Corrosion Science, The Journal of Physical Chemistry B and Molecular Systems Design & Engineering.
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.