Philipp Benner

935 citations
18 papers · 229 · 1 hit paper · h-index 8

Impact in

Papers in

Philipp Benner

17 papers receiving 222 citations

Philipp Benner's Hit Papers

A framework to evaluate machine learning crystal stability predictions 2025 · 48 citations
480Years since publication10203040

Peers

Philipp Benner
Comparison fields: 5 of 61
  • Statistical and Nonlinear Physics 78
  • Computer Networks and Communications 74
  • Materials Chemistry 53
  • Mathematical Physics 7
  • Molecular Biology 46
Replace Nagi Khalil with:
Nagi Khalil Spain
С. И. Фадеев Russia
Małgorzata M. Kłosek United States
Endre Csóka Hungary
Dan T. Gillespie United States
Yun Tian China
Stefan Hellander Sweden
Suyu Liu China
Alexander Slepoy United States
Je-Chiang Tsai Taiwan
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Citations per field
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Citations per year

Countries citing papers authored by Philipp Benner

Since Specialization
Citations

This map shows the geographic impact of Philipp Benner'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 Philipp Benner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philipp Benner more than expected).

Fields of papers citing papers by Philipp Benner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Philipp Benner. 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 Philipp Benner. The network helps show where Philipp Benner may publish in the future.

Co-authors

The 25 scholars most cited alongside Philipp Benner, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Philipp Benner Line = papers co-authored together Philipp Benner links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1 201080
2
A framework to evaluate machine learning crystal stability predictions
Hit paper breakdown →
202548
3 201219
4 201817
5 202316
6 20148
7 20198
8 20257
9
[From beginner to expert].
19967
10 20236
11 20195
12 20122
13 20251
14 20251
15 20251
16 20211
17 20211
18
Computing the posterior expectation of phylogenetic trees
20131

About Philipp Benner

Philipp Benner is a scholar working on Molecular Biology, Computational Theory and Mathematics, Materials Chemistry, Statistical and Nonlinear Physics and Spectroscopy, having authored 18 papers that have together received 229 indexed citations. Recurring topics across this work include Genomics and Chromatin Dynamics (4 papers), Computational Drug Discovery Methods (3 papers), Machine Learning in Materials Science (3 papers), Advanced Proteomics Techniques and Applications (2 papers), Genomics and Phylogenetic Studies (2 papers), Chaos control and synchronization (2 papers), Crystallization and Solubility Studies (2 papers) and Genetic diversity and population structure (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (78 citations), Computer Networks and Communications (74 citations), Materials Chemistry (53 citations), Mathematical Physics (7 citations) and Molecular Biology (46 citations). Philipp Benner has collaborated with scholars based in Germany, United States and Switzerland. Frequent co-authors include Yoko Uwate, Ruedi Stoop, Martin Vingron, Janosh Riebesell, Mark Asta, Yuan Chiang, Gerbrand Ceder, Bowen Deng, Kristin A. Persson and Anubhav Jain. Their work appears in journals such as Journal of Computational Biology, Nature Machine Intelligence, Bioinformatics, Nature Communications and International Journal of Bifurcation and Chaos.

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.

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