Scott LeGrand

1.4k total citations · 1 hit paper
5 papers, 803 citations indexed

About

Scott LeGrand is a scholar working on Materials Chemistry, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, Scott LeGrand has authored 5 papers receiving a total of 803 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Materials Chemistry, 3 papers in Molecular Biology and 2 papers in Artificial Intelligence. Recurrent topics in Scott LeGrand's work include Machine Learning in Materials Science (3 papers), Protein Structure and Dynamics (2 papers) and Computational Drug Discovery Methods (2 papers). Scott LeGrand is often cited by papers focused on Machine Learning in Materials Science (3 papers), Protein Structure and Dynamics (2 papers) and Computational Drug Discovery Methods (2 papers). Scott LeGrand collaborates with scholars based in United States and Germany. Scott LeGrand's co-authors include Vijay S. Pande, Daniel L. Ensign, Mike Houston, Peter Eastman, Christopher M. Bruns, Mark S. Friedrichs, Charles Lin, David S. Cerutti, Ross C. Walker and David A. Case and has published in prestigious journals such as Journal of Computational Chemistry, Journal of Chemical Information and Modeling and Parallel Computing.

In The Last Decade

Scott LeGrand

5 papers receiving 791 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

GPU-Accelerated Molecular Dynamics and Free Energy Methods in Amber18: Performance Enhancements and New Features

2018 330 citations Tai‐Sung Lee, David S. Cerutti et al. Journal of Chemical Information and Modeling profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Scott LeGrand United States	4	497	179	131	122	86	5	803
Kim Branson United States	10	525 1.1×	145 0.8×	260 2.0×	112 0.9×	60 0.7×	21	1.1k
G. Giupponi United Kingdom	11	635 1.3×	200 1.1×	98 0.7×	71 0.6×	99 1.2×	17	1.0k
Andrew T. Fenley United States	18	759 1.5×	229 1.3×	182 1.4×	272 2.2×	158 1.8×	27	1.2k
Andrew Emerson Italy	14	288 0.6×	332 1.9×	197 1.5×	124 1.0×	122 1.4×	21	867
Jaewoon Jung Japan	19	937 1.9×	351 2.0×	107 0.8×	259 2.1×	160 1.9×	45	1.3k
Guha Jayachandran United States	9	643 1.3×	294 1.6×	118 0.9×	146 1.2×	109 1.3×	14	856
Chigusa Kobayashi Japan	22	741 1.5×	298 1.7×	92 0.7×	289 2.4×	142 1.7×	37	1.1k
Moritz Hoffmann Switzerland	9	1.0k 2.1×	291 1.6×	362 2.8×	69 0.6×	113 1.3×	16	1.5k
Trung Hai Nguyen Vietnam	18	552 1.1×	132 0.7×	382 2.9×	119 1.0×	39 0.5×	54	1.2k
J.P. Grossman United States	15	729 1.5×	219 1.2×	123 0.9×	139 1.1×	131 1.5×	42	1.4k

Countries citing papers authored by Scott LeGrand

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Scott LeGrand

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

All Works

Sort: Min cites: Since: Top N: Style:

5 of 5 papers shown

1.

Solis-Vasquez, Leonardo, Andreas F. Tillack, Diogo Santos‐Martins, et al.. (2021). Benchmarking the performance of irregular computations in AutoDock-GPU molecular docking. Parallel Computing. 109. 102861–102861. 21 indexed citations

2.

Gläser, Jens, Josh V. Vermaas, David Rogers, et al.. (2021). High-throughput virtual laboratory for drug discovery using massive datasets. The International Journal of High Performance Computing Applications. 35(5). 452–468. 26 indexed citations

3.

Lee, Tai‐Sung, David S. Cerutti, Charles Lin, et al.. (2018). GPU-Accelerated Molecular Dynamics and Free Energy Methods in Amber18: Performance Enhancements and New Features. Journal of Chemical Information and Modeling. 58(10). 2043–2050. 330 indexed citations breakdown →

4.

LeGrand, Scott, et al.. (2018). THE EFFECTIVENESS OF A TWO-LAYER NEURAL NETWORK FOR RECOMMENDATIONS. 2 indexed citations

5.

Friedrichs, Mark S., Peter Eastman, Mike Houston, et al.. (2009). Accelerating molecular dynamic simulation on graphics processing units. Journal of Computational Chemistry. 30(6). 864–872. 424 indexed citations

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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