Charles J. McGill

889 citations
10 papers · 477 indexed · 1 hit paper · h-index 7
Topics
Machine Learning in Materials Science (4 papers)Computational Drug Discovery Methods (4 papers)Catalytic Processes in Materials Science (2 papers)

In The Last Decade

Charles J. McGill

9 papers receiving 466 citations

Hit Papers

Chemprop: A Machine Learning Package for Chemical Propert...2023202620242025202350100150200250

Peers

Charles J. McGill
Comparison fields: 5 of 86
  • Materials Chemistry 261
  • Computational Theory and Mathematics 230
  • Molecular Biology 136
  • Biomedical Engineering 80
  • Spectroscopy 60
Replace Kevin P. Greenman with:
Kevin P. Greenman United States
Shih‐Cheng Li Taiwan
Camille Bilodeau United States
Jakob B. Wolf Germany
Jinxiao Zhang China
Adam C. Mater Australia
Yunsie Chung United States
Sebastian Steiner Austria
Natalia Kireeva Russia
Jesús G. Estrada United States
Charles J. McGill relative to Kevin P. Greenman United States Kevin P. Greenman's profile →
Citations per field
00.5×1.5×
Kevin P. Greenman · 1×
Citations per year

Countries citing papers authored by Charles J. McGill

Since Specialization
Citations

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

Fields of papers citing papers by Charles J. McGill

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Charles J. McGill. 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 Charles J. McGill. The network helps show where Charles J. McGill may publish in the future.

Co-authorship network of co-authors of Charles J. McGill

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

All Works

10 of 10 papers shown
#WorkIndexed citations
1 1
2
Chemprop: A Machine Learning Package for Chemical Property Predictionbreakdown →
250
3 89
4 47
5 56
6 0
7 6
8 4
9 8
10 16

About Charles J. McGill

Charles J. McGill is a scholar working on Process Chemistry and Technology, Computational Theory and Mathematics and Biophysics, having authored 10 papers that have together received 477 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (4 papers), Computational Drug Discovery Methods (4 papers) and Catalytic Processes in Materials Science (2 papers). The work is most often cited by research in Computational Theory and Mathematics (230 citations), Materials Chemistry (261 citations) and Catalysis (26 citations). Charles J. McGill has collaborated with scholars based in United States, Austria and Belgium. Frequent co-authors include William H. Green, Florence H. Vermeire, Esther Heid, Kevin P. Greenman, Haoyang Wu, Shih‐Cheng Li, Yunsie Chung, David Graff, Yanfei Guan and Phillip R. Westmoreland. Their work appears in journals such as Science, Chemical Communications and Industrial & Engineering Chemistry Research.

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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