Martin K. Scherer

1.4k citations
3 papers · 856 indexed · 1 hit paper · h-index 3
Topics
Mass Spectrometry Techniques and Applications (2 papers)Protein Structure and Dynamics (1 paper)Machine Learning in Materials Science (1 paper)
Partner nations
GermanyUnited States

In The Last Decade

Martin K. Scherer

3 papers receiving 853 citations

Hit Papers

PyEMMA 2: A Software Package for Estimation, Validation, ...20152026201820222015250500750

Peers

Martin K. Scherer
Comparison fields: 5 of 102
  • Molecular Biology 716
  • Materials Chemistry 192
  • Computational Theory and Mathematics 118
  • Spectroscopy 108
  • Atomic and Molecular Physics, and Optics 66
Replace Yu‐Chu Chang with:
Yu‐Chu Chang United States
Marharyta Petukh United States
Jozef Hritz Czechia
Servaas Michielssens Belgium
Jaewoon Jung Japan
Kota Kasahara Japan
Weihua Zheng United States
Chuan Li United States
Robert G. Smock United States
Gabriela Barreiro United States
Martin K. Scherer relative to Yu‐Chu Chang United States Yu‐Chu Chang's profile →
Citations per field
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Yu‐Chu Chang · 1×
Citations per year

Countries citing papers authored by Martin K. Scherer

Since Specialization
Citations

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

Fields of papers citing papers by Martin K. Scherer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Martin K. Scherer. 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 Martin K. Scherer. The network helps show where Martin K. Scherer may publish in the future.

Co-authorship network of co-authors of Martin K. Scherer

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

All Works

3 of 3 papers shown
#WorkIndexed citations
1 40
2
Introduction to Markov state modeling with the PyEMMA software — v1.0
3
3
PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Modelsbreakdown →
813

About Martin K. Scherer

Martin K. Scherer is a scholar working on Spectroscopy, Physical and Theoretical Chemistry and Materials Chemistry, having authored 3 papers that have together received 856 indexed citations. Recurring topics across this work include Mass Spectrometry Techniques and Applications (2 papers), Protein Structure and Dynamics (1 paper) and Machine Learning in Materials Science (1 paper). The work is most often cited by research in Molecular Biology (716 citations), Computational Theory and Mathematics (118 citations) and Spectroscopy (108 citations). Martin K. Scherer has collaborated with scholars based in Germany and United States. Frequent co-authors include Christoph Wehmeyer, Frank Noé, Fabian Paul, Jan-Hendrik Prinz, Nuria Plattner, Guillermo Pérez‐Hernández, Moritz Hoffmann, Simon Olsson, Brooke E. Husic and Tim Hempel. Their work appears in journals such as 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.

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