Pan Kessel
- Nuclear and High Energy Physics top 10%
- Black Holes and Theoretical Physics 4
- Quantum Chromodynamics and Particle Interactions 3
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- Model Reduction and Neural Networks 4
- Statistical Mechanics and Entropy 3
- Condensed Matter Physics top 10%
- Theoretical and Computational Physics 5
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- Cosmology and Gravitation Theories 3
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- Explainable Artificial Intelligence (XAI) 3
- Adversarial Robustness in Machine Learning 3
- Co-authors
- Kim A. NicoliAlexandre TkatchenkoMichael GasteggerK. MüllerKristof T. SchüttShinichi NakajimaEvgeny SkvortsovMassimo Taronna
- Cited by
- Nuclear and High Energy PhysicsStatistical and Nonlinear PhysicsComputational Theory and Mathematics
- Journals
- Physical Review Letters (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)Geophysical Journal International (1 paper)
- Partner nations
- GermanyJapanSouth Korea
In The Last Decade
Pan Kessel
19 papers receiving 668 citations
Hit Papers
Peers
Comparison fields: 5 of 66
- Nuclear and High Energy Physics 153
- Statistical and Nonlinear Physics 124
- Computational Theory and Mathematics 139
- Condensed Matter Physics 84
- Materials Chemistry 295
Countries citing papers authored by Pan Kessel
This map shows the geographic impact of Pan Kessel'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 Pan Kessel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pan Kessel more than expected).
Fields of papers citing papers by Pan Kessel
This network shows the impact of papers produced by Pan Kessel. 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 Pan Kessel. The network helps show where Pan Kessel may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Pan Kessel, 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 | 2025 | 0 | |
| 2 | 2023 | 21 | |
| 3 | 2023 | 15 | |
| 4 | 2023 | 1 | |
| 5 | 2023 | 5 | |
| 6 | 2022 | 9 | |
| 7 | 2022 | 10 | |
| 8 | Diffeomorphic Explanations with Normalizing Flows | 2021 | 2 |
| 9 | 2021 | 71 | |
| 10 | 2021 | 28 | |
| 11 | 2021 | 7 | |
| 12 | 2021 | 8 | |
| 13 | 2020 | 7 | |
| 14 | 2020 | 63 | |
| 15 | Explanations can be manipulated and geometry is to blame | 2019 | 10 |
| 16 | 2018 | 18 | |
| 17 | SchNetPack: A Deep Learning Toolbox For Atomistic Systemsbreakdown → | 2018 | 305 |
| 18 | 2017 | 6 | |
| 19 | 2016 | 63 | |
| 20 | 2015 | 24 |
About Pan Kessel
Pan Kessel is a scholar working on Statistical and Nonlinear Physics, Condensed Matter Physics and Nuclear and High Energy Physics, having authored 20 papers that have together received 673 indexed citations. Recurring topics across this work include Theoretical and Computational Physics (5 papers), Model Reduction and Neural Networks (4 papers), Black Holes and Theoretical Physics (4 papers), Cosmology and Gravitation Theories (3 papers), Explainable Artificial Intelligence (XAI) (3 papers), Adversarial Robustness in Machine Learning (3 papers), Statistical Mechanics and Entropy (3 papers) and Quantum Chromodynamics and Particle Interactions (3 papers). The work is most often cited by research in Nuclear and High Energy Physics (153 citations), Statistical and Nonlinear Physics (124 citations) and Computational Theory and Mathematics (139 citations). Pan Kessel has collaborated with scholars based in Germany, Japan and South Korea. Frequent co-authors include Kim A. Nicoli, Alexandre Tkatchenko, Michael Gastegger, K. Müller, Kristof T. Schütt, Shinichi Nakajima, Evgeny Skvortsov, Massimo Taronna, Christopher J. Anders and Klaus‐Robert Müller. Their work appears in journals such as Physical Review Letters, IEEE Transactions on Pattern Analysis and Machine Intelligence and Geophysical Journal International.
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.