Jonathan Barnoud
- Pollution top 5%
- Molecular Biology top 5%
- Lipid Membrane Structure and Behavior 16
- Protein Structure and Dynamics 10
- Biomaterials top 5%
- Materials Chemistry top 10%
- Machine Learning in Materials Science 3
- Carbon Nanotubes in Composites 3
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- Nanopore and Nanochannel Transport Studies 8
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- Scientific Computing and Data Management 4
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- Spectroscopy and Quantum Chemical Studies 3
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- Fullerene Chemistry and Applications 3
- Co-authors
- Luca MonticelliGiulia RossiManuel N. MeloTyler ReddyRichard GowersOliver BecksteinSébastien BuchouxDavid Dotson
- Journals
- Biophysical Journal (6 papers)Journal of Chemical Theory and Computation (3 papers)The Journal of Physical Chemistry B (2 papers)
- Partner nations
- NetherlandsFranceUnited Kingdom
In The Last Decade
Jonathan Barnoud
33 papers receiving 2.4k citations
Hit Papers
Peers
Comparison fields: 5 of 136
- Pollution 267
- Molecular Biology 1.4k
- Biomaterials 190
- Industrial and Manufacturing Engineering 101
- Materials Chemistry 480
Countries citing papers authored by Jonathan Barnoud
This map shows the geographic impact of Jonathan Barnoud'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 Jonathan Barnoud with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan Barnoud more than expected).
Fields of papers citing papers by Jonathan Barnoud
This network shows the impact of papers produced by Jonathan Barnoud. 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 Jonathan Barnoud. The network helps show where Jonathan Barnoud may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jonathan Barnoud, 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 | 2023 | 46 | |
| 2 | 2023 | 2 | |
| 3 | 2023 | 4 | |
| 4 | 2022 | 32 | |
| 5 | 2021 | 116 | |
| 6 | 2020 | 70 | |
| 7 | 2020 | 30 | |
| 8 | 2020 | 37 | |
| 9 | 2019 | 11 | |
| 10 | 2019 | 12 | |
| 11 | 2019 | 1 | |
| 12 | 2018 | 1 | |
| 13 | 2017 | 6 | |
| 14 | 2017 | 33 | |
| 15 | MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulationsbreakdown → | 2016 | 1133 |
| 16 | 2014 | 19 | |
| 17 | 2014 | 56 | |
| 18 | 2013 | 110 | |
| 19 | 2012 | 17 | |
| 20 | 2012 | 95 |
About Jonathan Barnoud
Jonathan Barnoud is a scholar working on Information Systems and Management, Biophysics and Molecular Biology, having authored 34 papers that have together received 2.5k indexed citations. Recurring topics across this work include Lipid Membrane Structure and Behavior (16 papers), Protein Structure and Dynamics (10 papers), Nanopore and Nanochannel Transport Studies (8 papers), Scientific Computing and Data Management (4 papers), Spectroscopy and Quantum Chemical Studies (3 papers), Machine Learning in Materials Science (3 papers), Fullerene Chemistry and Applications (3 papers) and Carbon Nanotubes in Composites (3 papers). The work is most often cited by research in Pollution (267 citations), Molecular Biology (1.4k citations) and Biomaterials (190 citations). Jonathan Barnoud has collaborated with scholars based in Netherlands, France and United Kingdom. Frequent co-authors include Luca Monticelli, Giulia Rossi, Manuel N. Melo, Tyler Reddy, Richard Gowers, Oliver Beckstein, Sébastien Buchoux, David Dotson, Ian M. Kenney and Max Linke. Their work appears in journals such as Biophysical Journal, Journal of Chemical Theory and Computation, The Journal of Physical Chemistry B, Scientific Reports and Nanoscale.
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.