Franziska Biegler
- Materials Chemistry top 5%
- Computational Theory and Mathematics top 1%
- Molecular Biology
- Atomic and Molecular Physics, and Optics
- Physical and Theoretical Chemistry top 5%
- Co-authors
- Klaus‐Robert MüllerAlexandre TkatchenkoKatja HansenO. Anatole von LilienfeldRaghunathan RamakrishnanMatthias RuppGrégoire MontavonSiamac Fazli
- Topics
- semigroups and automata theory (5 papers)Machine Learning in Materials Science (4 papers)DNA and Biological Computing (4 papers)
- Journals
- PLoS ONEThe Journal of Physical Chemistry LettersJournal of Chemical Theory and Computation
- Partner nations
- GermanySouth KoreaCanada
In The Last Decade
Franziska Biegler
10 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 84
- Materials Chemistry 958
- Computational Theory and Mathematics 585
- Molecular Biology 283
- Atomic and Molecular Physics, and Optics 158
- Physical and Theoretical Chemistry 110
Countries citing papers authored by Franziska Biegler
This map shows the geographic impact of Franziska Biegler'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 Franziska Biegler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Franziska Biegler more than expected).
Fields of papers citing papers by Franziska Biegler
This network shows the impact of papers produced by Franziska Biegler. 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 Franziska Biegler. The network helps show where Franziska Biegler may publish in the future.
Co-authorship network of co-authors of Franziska Biegler
This figure shows the co-authorship network connecting the top 25 collaborators of Franziska Biegler. A scholar is included among the top collaborators of Franziska Biegler 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 Franziska Biegler. Franziska Biegler is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 40 | |
| 2 | Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Spacebreakdown → | 585 |
| 3 | Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energiesbreakdown → | 443 |
| 4 | 11 | |
| 5 | 2 | |
| 6 | Learning Invariant Representations of Molecules for Atomization Energy Prediction | 49 |
| 7 | 5 | |
| 8 | 4 | |
| 9 | 0 | |
| 10 | 16 | |
| 11 | 1 |
About Franziska Biegler
Franziska Biegler is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Management of Technology and Innovation, having authored 11 papers that have together received 1.2k indexed citations. Recurring topics across this work include semigroups and automata theory (5 papers), Machine Learning in Materials Science (4 papers) and DNA and Biological Computing (4 papers). The work is most often cited by research in Computational Theory and Mathematics (585 citations), Materials Chemistry (958 citations) and Physical and Theoretical Chemistry (110 citations). Franziska Biegler has collaborated with scholars based in Germany, South Korea and Canada. Frequent co-authors include Klaus‐Robert Müller, Alexandre Tkatchenko, Katja Hansen, O. Anatole von Lilienfeld, Raghunathan Ramakrishnan, Matthias Rupp, Grégoire Montavon, Siamac Fazli, Matthias Scheffler and Mark Daley. Their work appears in journals such as PLoS ONE, The Journal of Physical Chemistry Letters 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.