Andreas Mardt
Impact in
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- Model Reduction and Neural Networks
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- Computational Drug Discovery Methods
Papers in
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- Protein Structure and Dynamics 3
- DNA Repair Mechanisms 1
- DNA and Nucleic Acid Chemistry 1
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- Gaussian Processes and Bayesian Inference 2
- Adversarial Robustness in Machine Learning 1
- Co-authors
- Frank Noé (4 shared papers)Hao Wu (3 shared papers)Tim Hempel (1 shared paper)Cecilia Clementi (1 shared paper)Maï Zahran (1 shared paper)Petra Imhof (1 shared paper)
- Journals
- Biophysical Chemistry (1 paper)Nature Communications (1 paper)arXiv (Cornell University) (1 paper)Neural Information Processing Systems (1 paper)RePEc: Research Papers in Economics (1 paper)
- Partner nations
- GermanyUnited StatesChina
In The Last Decade
Andreas Mardt
6 papers receiving 366 citations
Andreas Mardt's Hit Papers
Peers
Comparison fields: 5 of 82
- Statistical and Nonlinear Physics 93
- Computational Theory and Mathematics 77
- Molecular Biology 219
- Materials Chemistry 149
- Statistics, Probability and Uncertainty 22
Countries citing papers authored by Andreas Mardt
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
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-authors
The 6 scholars most cited alongside Andreas Mardt, 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 | VAMPnets for deep learning of molecular kinetics Hit paper breakdown → | 2018 | 341 |
| 2 | 2022 | 15 | |
| 3 | 2021 | 8 | |
| 4 | Deep Generative Markov State Models | 2018 | 6 |
| 5 | 2022 | 4 | |
| 6 | Deep learning Markov and Koopman models with physical constraints | 2019 | 2 |
About Andreas Mardt
Andreas Mardt is a scholar working on Molecular Biology, Artificial Intelligence, Materials Chemistry, Statistical and Nonlinear Physics and Computer Vision and Pattern Recognition, having authored 6 papers that have together received 376 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (3 papers), Protein Structure and Dynamics (3 papers), Model Reduction and Neural Networks (2 papers), Gaussian Processes and Bayesian Inference (2 papers), DNA Repair Mechanisms (1 paper), Computational Drug Discovery Methods (1 paper), Adversarial Robustness in Machine Learning (1 paper) and DNA and Nucleic Acid Chemistry (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (93 citations), Computational Theory and Mathematics (77 citations), Molecular Biology (219 citations), Materials Chemistry (149 citations) and Statistics, Probability and Uncertainty (22 citations). Andreas Mardt has collaborated with scholars based in Germany, United States and China. Frequent co-authors include Frank Noé, Hao Wu, Tim Hempel, Cecilia Clementi, Maï Zahran and Petra Imhof. Their work appears in journals such as Biophysical Chemistry, Nature Communications, arXiv (Cornell University), Neural Information Processing Systems and RePEc: Research Papers in Economics.
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.