Scott LeGrand
Impact in
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- Computational Drug Discovery Methods
- Hardware and Architecture top 10%
- Parallel Computing and Optimization Techniques
Papers in ⓘ
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- Computational Drug Discovery Methods 2
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- Machine Learning in Materials Science 3
- Enzyme Structure and Function 1
- Co-authors
- Mike Houston (1 shared paper)Mark S. Friedrichs (1 shared paper)Christopher M. Bruns (1 shared paper)Daniel L. Ensign (1 shared paper)Vijay S. Pande (1 shared paper)Peter Eastman (1 shared paper)Ross C. Walker (1 shared paper)David A. Case (1 shared paper)
- Journals
- Journal of Computational Chemistry (1 paper)The International Journal of High Performance Computing Applications (1 paper)Journal of Chemical Information and Modeling (1 paper)Parallel Computing (1 paper)
- Partner nations
- United StatesGermany
In The Last Decade
Scott LeGrand
5 papers receiving 791 citations
Hit Papers
Peers
Comparison fields: 5 of 123
- Computational Theory and Mathematics 131
- Hardware and Architecture 52
- Molecular Biology 497
- Spectroscopy 86
- Atomic and Molecular Physics, and Optics 122
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 | 424 | |
| 2 | GPU-Accelerated Molecular Dynamics and Free Energy Methods in Amber18: Performance Enhancements and New Features Hit paper breakdown → | 2018 | 330 |
| 3 | 2021 | 26 | |
| 4 | 2021 | 21 | |
| 5 | THE EFFECTIVENESS OF A TWO-LAYER NEURAL NETWORK FOR RECOMMENDATIONS | 2018 | 2 |
About Scott LeGrand
Scott LeGrand is a scholar working on Computational Theory and Mathematics, Materials Chemistry, Management Science and Operations Research, Artificial Intelligence and Information Systems, having authored 5 papers that have together received 803 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (3 papers), Computational Drug Discovery Methods (2 papers), Protein Structure and Dynamics (2 papers), Advanced Text Analysis Techniques (1 paper), Advanced Chemical Physics Studies (1 paper), Metabolomics and Mass Spectrometry Studies (1 paper), Enzyme Structure and Function (1 paper) and Quantum, superfluid, helium dynamics (1 paper). The work is most often cited by research in Computational Theory and Mathematics (131 citations), Hardware and Architecture (52 citations), Molecular Biology (497 citations), Spectroscopy (86 citations) and Atomic and Molecular Physics, and Optics (122 citations). Scott LeGrand has collaborated with scholars based in United States and Germany. Frequent co-authors include Mike Houston, Mark S. Friedrichs, Christopher M. Bruns, Daniel L. Ensign, Vijay S. Pande, Peter Eastman, Ross C. Walker, David A. Case, Adrián E. Roitberg and Charles Lin. Their work appears in journals such as Journal of Computational Chemistry, The International Journal of High Performance Computing Applications, Journal of Chemical Information and Modeling and Parallel Computing.
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.