Gregor N. C. Simm
- Materials Chemistry top 10%
- Computational Theory and Mathematics top 2%
- Molecular Biology
- Atomic and Molecular Physics, and Optics
- Catalysis top 10%
- Co-authors
- Markus ReiherJonny ProppeAlain C. VaucherTamara HuschEdward O. Pyzer‐KnappJohannes HachmannKewei LiSteven A. Lopez
- Topics
- Machine Learning in Materials Science (10 papers)Computational Drug Discovery Methods (7 papers)Protein Structure and Dynamics (2 papers)
- Journals
- The Journal of Physical Chemistry AJournal of Chemical Theory and ComputationJournal of Chemical Information and Modeling
- Partner nations
- SwitzerlandUnited KingdomUnited States
In The Last Decade
Gregor N. C. Simm
10 papers receiving 791 citations
Hit Papers
Peers
Comparison fields: 5 of 87
- Materials Chemistry 522
- Computational Theory and Mathematics 302
- Molecular Biology 204
- Atomic and Molecular Physics, and Optics 152
- Catalysis 109
Countries citing papers authored by Gregor N. C. Simm
This map shows the geographic impact of Gregor N. C. Simm'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 Gregor N. C. Simm with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gregor N. C. Simm more than expected).
Fields of papers citing papers by Gregor N. C. Simm
This network shows the impact of papers produced by Gregor N. C. Simm. 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 Gregor N. C. Simm. The network helps show where Gregor N. C. Simm may publish in the future.
Co-authorship network of co-authors of Gregor N. C. Simm
This figure shows the co-authorship network connecting the top 25 collaborators of Gregor N. C. Simm. A scholar is included among the top collaborators of Gregor N. C. Simm based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Gregor N. C. Simm. Gregor N. C. Simm is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | The design space of E(3)-equivariant atom-centred interatomic potentialsbreakdown → | 61 |
| 2 | 60 | |
| 3 | 57 | |
| 4 | 162 | |
| 5 | 94 | |
| 6 | 26 | |
| 7 | 99 | |
| 8 | 57 | |
| 9 | 59 | |
| 10 | 126 |
About Gregor N. C. Simm
Gregor N. C. Simm is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Electrochemistry, having authored 10 papers that have together received 801 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (10 papers), Computational Drug Discovery Methods (7 papers) and Protein Structure and Dynamics (2 papers). The work is most often cited by research in Computational Theory and Mathematics (302 citations), Catalysis (109 citations) and Materials Chemistry (522 citations). Gregor N. C. Simm has collaborated with scholars based in Switzerland, United Kingdom and United States. Frequent co-authors include Markus Reiher, Jonny Proppe, Alain C. Vaucher, Tamara Husch, Edward O. Pyzer‐Knapp, Johannes Hachmann, Kewei Li, Steven A. Lopez, Alán Aspuru‐Guzik and Andreas Bender. Their work appears in journals such as The Journal of Physical Chemistry A, Journal of Chemical Theory and Computation and Journal of Chemical Information and Modeling.
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.