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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- Machine Learning in Materials Science 3
- Enzyme Structure and Function 1
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- Protein Structure and Dynamics 2
- Metabolomics and Mass Spectrometry Studies 1
- Co-authors
- Mark S. Friedrichs (1 shared paper)Christopher M. Bruns (1 shared paper)Vijay S. Pande (1 shared paper)Peter Eastman (1 shared paper)Daniel L. Ensign (1 shared paper)Mike Houston (1 shared paper)Tai‐Sung Lee (1 shared paper)Adrián E. Roitberg (1 shared paper)
- Journals
- Journal of Computational Chemistry (1 paper)The International Journal of High Performance Computing Applications (1 paper)Parallel Computing (1 paper)Journal of Chemical Information and Modeling (1 paper)
- Partner nations
- United StatesGermany
In The Last Decade
Scott LeGrand
5 papers receiving 810 citations
Scott LeGrand's Hit Papers
Peers
Comparison fields: 5 of 120
- Computational Theory and Mathematics 133
- Hardware and Architecture 52
- Molecular Biology 491
- Spectroscopy 88
- 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 | 425 | |
| 2 | GPU-Accelerated Molecular Dynamics and Free Energy Methods in Amber18: Performance Enhancements and New Features Hit paper breakdown → | 2018 | 346 |
| 3 | 2021 | 27 | |
| 4 | 2021 | 22 | |
| 5 | THE EFFECTIVENESS OF A TWO-LAYER NEURAL NETWORK FOR RECOMMENDATIONS | 2018 | 2 |
About Scott LeGrand
Scott LeGrand is a scholar working on Materials Chemistry, Molecular Biology, Artificial Intelligence, Computational Theory and Mathematics and Information Systems, having authored 5 papers that have together received 822 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), Stock Market Forecasting Methods (1 paper), Metabolomics and Mass Spectrometry Studies (1 paper), Enzyme Structure and Function (1 paper), Advanced Text Analysis Techniques (1 paper) and Recommender Systems and Techniques (1 paper). The work is most often cited by research in Computational Theory and Mathematics (133 citations), Hardware and Architecture (52 citations), Molecular Biology (491 citations), Spectroscopy (88 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 Mark S. Friedrichs, Christopher M. Bruns, Vijay S. Pande, Peter Eastman, Daniel L. Ensign, Mike Houston, Tai‐Sung Lee, Adrián E. Roitberg, David A. Case and Darrin M. York. Their work appears in journals such as Journal of Computational Chemistry, The International Journal of High Performance Computing Applications, Parallel Computing and Journal of Chemical Information and Modeling.
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.