Caitlin C. Bannan
- Molecular Biology
- Computational Theory and Mathematics top 2%
- Materials Chemistry
- Atomic and Molecular Physics, and Optics top 10%
- Organic Chemistry
- Co-authors
- David L. MobleyDaisy Y. KyuMichael R. ShirtsGaetano CalabròMichael K. GilsonChristopher I. BaylyJohn D. ChoderaVictoria T. Lim
- Topics
- Protein Structure and Dynamics (6 papers)Machine Learning in Materials Science (5 papers)Computational Drug Discovery Methods (5 papers)
- Cited by
- Computational Theory and MathematicsFiltration and SeparationPhysical and Theoretical Chemistry
- Journals
- The Journal of Chemical PhysicsThe Journal of Physical Chemistry AJournal of Chemical Theory and Computation
- Partner nations
- United StatesSwitzerlandCzechia
In The Last Decade
Caitlin C. Bannan
15 papers receiving 610 citations
Peers
Comparison fields: 5 of 77
- Molecular Biology 307
- Computational Theory and Mathematics 220
- Materials Chemistry 208
- Atomic and Molecular Physics, and Optics 167
- Organic Chemistry 103
Countries citing papers authored by Caitlin C. Bannan
This map shows the geographic impact of Caitlin C. Bannan'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 Caitlin C. Bannan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Caitlin C. Bannan more than expected).
Fields of papers citing papers by Caitlin C. Bannan
This network shows the impact of papers produced by Caitlin C. Bannan. 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 Caitlin C. Bannan. The network helps show where Caitlin C. Bannan may publish in the future.
Co-authorship network of co-authors of Caitlin C. Bannan
This figure shows the co-authorship network connecting the top 25 collaborators of Caitlin C. Bannan. A scholar is included among the top collaborators of Caitlin C. Bannan 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 Caitlin C. Bannan. Caitlin C. Bannan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 2 | |
| 3 | 12 | |
| 4 | 113 | |
| 5 | 1 | |
| 6 | 4 | |
| 7 | 1 | |
| 8 | 121 | |
| 9 | 28 | |
| 10 | 21 | |
| 11 | 90 | |
| 12 | 9 | |
| 13 | 138 | |
| 14 | 19 | |
| 15 | 56 |
About Caitlin C. Bannan
Caitlin C. Bannan is a scholar working on Physical and Theoretical Chemistry, Computational Theory and Mathematics and Biophysics, having authored 15 papers that have together received 617 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (6 papers), Machine Learning in Materials Science (5 papers) and Computational Drug Discovery Methods (5 papers). The work is most often cited by research in Computational Theory and Mathematics (220 citations), Filtration and Separation (21 citations) and Physical and Theoretical Chemistry (79 citations). Caitlin C. Bannan has collaborated with scholars based in United States, Switzerland and Czechia. Frequent co-authors include David L. Mobley, Daisy Y. Kyu, Michael R. Shirts, Gaetano Calabrò, Michael K. Gilson, Christopher I. Bayly, John D. Chodera, Victoria T. Lim, Andrea Rizzi and Michael Chiu. Their work appears in journals such as The Journal of Chemical Physics, The Journal of Physical Chemistry A and Journal of Chemical Theory and Computation.
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.