Katja Hansen
- Materials Chemistry top 2%
- Computational Theory and Mathematics top 0.2%
- Molecular Biology
- Atomic and Molecular Physics, and Optics top 5%
- Physical and Theoretical Chemistry top 2%
- Co-authors
- Klaus‐Robert MüllerMatthias RuppAlexandre TkatchenkoO. Anatole von LilienfeldFranziska BieglerKieron BurkeJohn SnyderGrégoire Montavon
- Topics
- Computational Drug Discovery Methods (10 papers)Machine Learning in Materials Science (9 papers)Protein Structure and Dynamics (2 papers)
- Journals
- Physical Review LettersThe Journal of Chemical PhysicsThe Journal of Physical Chemistry Letters
- Partner nations
- GermanySwitzerlandUnited States
In The Last Decade
Katja Hansen
16 papers receiving 2.4k citations
Hit Papers
Peers
Comparison fields: 5 of 122
- Materials Chemistry 1.8k
- Computational Theory and Mathematics 1.2k
- Molecular Biology 576
- Atomic and Molecular Physics, and Optics 415
- Physical and Theoretical Chemistry 209
Countries citing papers authored by Katja Hansen
This map shows the geographic impact of Katja Hansen'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 Katja Hansen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Katja Hansen more than expected).
Fields of papers citing papers by Katja Hansen
This network shows the impact of papers produced by Katja Hansen. 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 Katja Hansen. The network helps show where Katja Hansen may publish in the future.
Co-authorship network of co-authors of Katja Hansen
This figure shows the co-authorship network connecting the top 25 collaborators of Katja Hansen. A scholar is included among the top collaborators of Katja Hansen 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 Katja Hansen. Katja Hansen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Spacebreakdown → | 585 |
| 2 | Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energiesbreakdown → | 443 |
| 3 | 285 | |
| 4 | 86 | |
| 5 | Finding Density Functionals with Machine Learningbreakdown → | 414 |
| 6 | 6 | |
| 7 | 83 | |
| 8 | Learning Invariant Representations of Molecules for Atomization Energy Prediction | 49 |
| 9 | 36 | |
| 10 | 11 | |
| 11 | 25 | |
| 12 | 45 | |
| 13 | 257 | |
| 14 | 22 | |
| 15 | 21 | |
| 16 | 28 |
About Katja Hansen
Katja Hansen is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Molecular Biology, having authored 16 papers that have together received 2.4k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (10 papers), Machine Learning in Materials Science (9 papers) and Protein Structure and Dynamics (2 papers). The work is most often cited by research in Computational Theory and Mathematics (1.2k citations), Materials Chemistry (1.8k citations) and Physical and Theoretical Chemistry (209 citations). Katja Hansen has collaborated with scholars based in Germany, Switzerland and United States. Frequent co-authors include Klaus‐Robert Müller, Matthias Rupp, Alexandre Tkatchenko, O. Anatole von Lilienfeld, Franziska Biegler, Kieron Burke, John Snyder, Grégoire Montavon, Raghunathan Ramakrishnan and Siamac Fazli. Their work appears in journals such as Physical Review Letters, The Journal of Chemical Physics and The Journal of Physical Chemistry Letters.
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.