Scott LeGrand
Impact in
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- Computational Drug Discovery Methods
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- Protein Structure and Dynamics
- RNA and protein synthesis mechanisms
- DNA and Nucleic Acid Chemistry
Papers in
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- Computational Drug Discovery Methods 3
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- Machine Learning in Materials Science 3
- Enzyme Structure and Function 1
- Co-authors
- Vijay S. Pande (1 shared paper)Daniel L. Ensign (1 shared paper)Peter K. Eastman (1 shared paper)Mark S. Friedrichs (1 shared paper)Mike Houston (1 shared paper)Christopher M. Bruns (1 shared paper)Tai‐Sung Lee (1 shared paper)Adrián E. Roitberg (1 shared paper)
- Journals
- Journal of Chemical Information and Modeling (1 paper)Journal of Computational Chemistry (1 paper)The International Journal of High Performance Computing Applications (1 paper)Parallel Computing (1 paper)Open Repository and Bibliography (University of Luxembourg) (1 paper)
- Partner nations
- United StatesGermany
In The Last Decade
Scott LeGrand
6 papers receiving 1.0k citations
Scott LeGrand's Hit Papers
Peers
Comparison fields: 5 of 123
- Computational Theory and Mathematics 196
- Molecular Biology 606
- Hardware and Architecture 59
- Spectroscopy 93
- Materials Chemistry 217
Countries citing papers authored by Scott LeGrand
This map shows the geographic impact of Scott LeGrand'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 Scott LeGrand with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Scott LeGrand more than expected).
Fields of papers citing papers by Scott LeGrand
This network shows the impact of papers produced by Scott LeGrand. 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 Scott LeGrand. The network helps show where Scott LeGrand may publish in the future.
Co-authors
The 25 scholars most cited alongside Scott LeGrand, 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 | 2009 | 448 | |
| 2 | GPU-Accelerated Molecular Dynamics and Free Energy Methods in Amber18: Performance Enhancements and New Features Hit paper breakdown → | 2018 | 389 |
| 3 | Amber 2021: Reference Manual | 2021 | 83 |
| 4 | 2020 | 43 | |
| 5 | 2021 | 29 | |
| 6 | 2021 | 27 |
About Scott LeGrand
Scott LeGrand is a scholar working on Computational Theory and Mathematics, Materials Chemistry, Molecular Biology, Atomic and Molecular Physics, and Optics and Artificial Intelligence, having authored 6 papers that have together received 1.0k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (3 papers), Machine Learning in Materials Science (3 papers), Protein Structure and Dynamics (3 papers), Protein Degradation and Inhibitors (1 paper), Advanced Chemical Physics Studies (1 paper), Quantum, superfluid, helium dynamics (1 paper), Metabolomics and Mass Spectrometry Studies (1 paper) and Enzyme Structure and Function (1 paper). The work is most often cited by research in Computational Theory and Mathematics (196 citations), Molecular Biology (606 citations), Hardware and Architecture (59 citations), Spectroscopy (93 citations) and Materials Chemistry (217 citations). Scott LeGrand has collaborated with scholars based in United States and Germany. Frequent co-authors include Vijay S. Pande, Daniel L. Ensign, Peter K. Eastman, Mark S. Friedrichs, Mike Houston, Christopher M. Bruns, Tai‐Sung Lee, Adrián E. Roitberg, David Andrew Case and Timothy J. Giese. Their work appears in journals such as Journal of Chemical Information and Modeling, Journal of Computational Chemistry, The International Journal of High Performance Computing Applications, Parallel Computing and Open Repository and Bibliography (University of Luxembourg).
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.