Nicholas E. Charron

1.6k total citations · 1 hit paper
18 papers, 1.1k citations indexed

About

Nicholas E. Charron is a scholar working on Molecular Biology, Materials Chemistry and Civil and Structural Engineering. According to data from OpenAlex, Nicholas E. Charron has authored 18 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 8 papers in Materials Chemistry and 3 papers in Civil and Structural Engineering. Recurrent topics in Nicholas E. Charron's work include Protein Structure and Dynamics (8 papers), Machine Learning in Materials Science (8 papers) and Lipid Membrane Structure and Behavior (4 papers). Nicholas E. Charron is often cited by papers focused on Protein Structure and Dynamics (8 papers), Machine Learning in Materials Science (8 papers) and Lipid Membrane Structure and Behavior (4 papers). Nicholas E. Charron collaborates with scholars based in United States, Germany and Canada. Nicholas E. Charron's co-authors include Frank Noé, Cecilia Clementi, Simon Olsson, Jiang Wang, Adrià Pérez, Gianni De Fabritiis, Steven L. Waslander, Stephen Phillips, Christoph Wehmeyer and Huey W. Huang and has published in prestigious journals such as Nature Communications, The Journal of Chemical Physics and Biochemistry.

In The Last Decade

Nicholas E. Charron

17 papers receiving 1.1k citations

Hit Papers

Machine Learning of Coarse-Grained Molecular Dynamics For... 2019 2026 2021 2023 2019 100 200 300
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.

  1. Machine Learning of Coarse-Grained Molecular Dynamics Force Fields
    2019 343 citations Jiang Wang, Simon Olsson et al. ACS Central Science profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nicholas E. Charron United States 14 512 501 155 101 88 18 1.1k
Huan Zhang China 23 286 0.6× 418 0.8× 16 0.1× 14 0.1× 23 0.3× 99 2.0k
Jae-Hun Jung South Korea 16 203 0.4× 137 0.3× 60 0.4× 26 0.3× 16 0.2× 88 921
Bo Chen China 21 85 0.2× 456 0.9× 30 0.2× 39 0.4× 10 0.1× 198 2.0k
Junqi Yin United States 18 226 0.4× 305 0.6× 72 0.5× 4 0.0× 15 0.2× 59 1.2k
Guangyuan Li China 25 145 0.3× 274 0.5× 20 0.1× 83 0.8× 18 0.2× 163 2.2k
Pengyun Chen China 19 185 0.4× 448 0.9× 11 0.1× 9 0.1× 17 0.2× 122 1.4k
Juan Meng China 20 243 0.5× 103 0.2× 47 0.3× 9 0.1× 9 0.1× 63 1.1k
Thomas Grosges France 18 69 0.1× 72 0.1× 84 0.5× 31 0.3× 12 0.1× 57 921
Rui Gao China 22 413 0.8× 485 1.0× 107 0.7× 7 0.1× 2 0.0× 91 1.5k
Arash Bahrami Iran 18 586 1.1× 253 0.5× 28 0.2× 88 0.9× 2 0.0× 49 1.2k

Countries citing papers authored by Nicholas E. Charron

Since Specialization
Citations

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

Fields of papers citing papers by Nicholas E. Charron

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nicholas E. Charron. 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 Nicholas E. Charron. The network helps show where Nicholas E. Charron may publish in the future.

Co-authorship network of co-authors of Nicholas E. Charron

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

All Works

18 of 18 papers shown
1.
2.
Durumeric, Aleksander E. P., Nicholas E. Charron, Félix Musil, et al.. (2023). Machine learned coarse-grained protein force-fields: Are we there yet?. Current Opinion in Structural Biology. 79. 102533–102533. 47 indexed citations
3.
Majewski, Maciej, Adrià Pérez, Stefan H. Doerr, et al.. (2023). Machine learning coarse-grained potentials of protein thermodynamics. Nature Communications. 14(1). 5739–5739. 62 indexed citations
4.
Pan, Xiaqing, Nicholas E. Charron, Scott J. Peters, et al.. (2023). Aria Digital Twin: A New Benchmark Dataset for Egocentric 3D Machine Perception. 20076–20086. 20 indexed citations
5.
Krämer, Andreas, Aleksander E. P. Durumeric, Nicholas E. Charron, et al.. (2023). Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics. The Journal of Physical Chemistry Letters. 14(17). 3970–3979. 19 indexed citations
6.
Chen, Yaoyi, Andreas Krämer, Nicholas E. Charron, et al.. (2022). Machine learning implicit solvation for molecular dynamics. Refubium (Universitätsbibliothek der Freien Universität Berlin). 58 indexed citations
7.
Shmilovich, Kirill, et al.. (2022). Temporally Coherent Backmapping of Molecular Trajectories From Coarse-Grained to Atomistic Resolution. The Journal of Physical Chemistry A. 126(48). 9124–9139. 13 indexed citations
8.
Wang, Jiang, Nicholas E. Charron, Brooke E. Husic, et al.. (2021). Multi-body effects in a coarse-grained protein force field. The Journal of Chemical Physics. 154(16). 164113–164113. 41 indexed citations
9.
Husic, Brooke E., Nicholas E. Charron, Dominik Lemm, et al.. (2020). Coarse graining molecular dynamics with graph neural networks. Refubium (Universitätsbibliothek der Freien Universität Berlin). 121 indexed citations
10.
Charron, Nicholas E., et al.. (2020). Automated Defect Quantification in Concrete Bridges Using Robotics and Deep Learning. Journal of Computing in Civil Engineering. 34(5). 63 indexed citations
11.
Lee, Ming-Tao, et al.. (2019). Comparison of the Effects of Daptomycin on Bacterial and Model Membranes. Biophysical Journal. 116(3). 44a–45a. 1 indexed citations
12.
Charron, Nicholas E., et al.. (2019). Combining Deep Learning and Robotics for Automated Concrete Delamination Assessment. Proceedings of the ... ISARC. 10 indexed citations
13.
Charron, Nicholas E., et al.. (2019). Automated Bridge Inspection Using Mobile Ground Robotics. Journal of Structural Engineering. 145(11). 4019137–4019137. 41 indexed citations
14.
Wang, Jiang, Simon Olsson, Christoph Wehmeyer, et al.. (2019). Machine Learning of Coarse-Grained Molecular Dynamics Force Fields. ACS Central Science. 5(5). 755–767. 343 indexed citations breakdown →
15.
Lee, Ming-Tao, et al.. (2018). Comparison of the Effects of Daptomycin on Bacterial and Model Membranes. Biochemistry. 57(38). 5629–5639. 34 indexed citations
16.
Charron, Nicholas E., Stephen Phillips, & Steven L. Waslander. (2018). De-noising of Lidar Point Clouds Corrupted by Snowfall. 254–261. 122 indexed citations
17.
Huang, Huey W. & Nicholas E. Charron. (2017). Understanding membrane-active antimicrobial peptides. Quarterly Reviews of Biophysics. 50. e10–e10. 53 indexed citations
18.
Hung, Wei‐Chin, et al.. (2016). Comparative Study of the Condensing Effects of Ergosterol and Cholesterol. Biophysical Journal. 110(9). 2026–2033. 31 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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