Andreas Mardt

700 citations
6 papers · 374 indexed · 1 hit paper · h-index 5
Topics
Protein Structure and Dynamics (3 papers)Machine Learning in Materials Science (3 papers)Model Reduction and Neural Networks (2 papers)
Journals
Nature CommunicationsBiophysical ChemistryarXiv (Cornell University)

In The Last Decade

Andreas Mardt

6 papers receiving 364 citations

Hit Papers

VAMPnets for deep learning of molecular kinetics20182026202020232018100200300

Peers

Andreas Mardt
Comparison fields: 5 of 81
  • Molecular Biology 219
  • Materials Chemistry 149
  • Statistical and Nonlinear Physics 92
  • Computational Theory and Mathematics 77
  • Artificial Intelligence 48
Replace Jonas Köhler with:
Jonas Köhler Germany
Chulan Kwon South Korea
Izaak Neri United Kingdom
Yukito Iba Japan
Elena Facco Italy
Katherine Klymko United States
T. J. Christopher Ward United States
Shinji Takesue Japan
Chao‐Kun Cheng United States
Peter R. Cromwell United Kingdom
Andreas Mardt relative to Jonas Köhler Germany Jonas Köhler's profile →
Citations per field
00.5×1.5×2.1×
Jonas Köhler · 1×
Citations per year

Countries citing papers authored by Andreas Mardt

Since Specialization
Citations

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

Fields of papers citing papers by Andreas Mardt

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Andreas Mardt

This figure shows the co-authorship network connecting the top 25 collaborators of Andreas Mardt. A scholar is included among the top collaborators of Andreas Mardt 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 Andreas Mardt. Andreas Mardt is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

6 of 6 papers shown
#WorkIndexed citations
1 15
2 4
3 8
4
Deep learning Markov and Koopman models with physical constraints
2
5
Deep Generative Markov State Models
6
6
VAMPnets for deep learning of molecular kineticsbreakdown →
339

About Andreas Mardt

Andreas Mardt is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Materials Chemistry, having authored 6 papers that have together received 374 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (3 papers), Machine Learning in Materials Science (3 papers) and Model Reduction and Neural Networks (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (92 citations), Computational Theory and Mathematics (77 citations) and Materials Chemistry (149 citations). Andreas Mardt has collaborated with scholars based in Germany, United States and China. Frequent co-authors include Frank Noé, Hao Wu, Cecilia Clementi, Tim Hempel, Maï Zahran and Petra Imhof. Their work appears in journals such as Nature Communications, Biophysical Chemistry and arXiv (Cornell University).

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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026