David Lonie
- Organic Chemistry top 0.5%
- Physical and Theoretical Chemistry top 0.5%
- Toxicology top 1%
- Computational Theory and Mathematics top 0.5%
- Computational Drug Discovery Methods 4
- Materials Chemistry top 2%
- Machine Learning in Materials Science 4
- Carbon Nanotubes in Composites 1
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- Protein Structure and Dynamics 3
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- Scientific Computing and Data Management 2
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- Analytical Chemistry and Chromatography 1
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- Analytical chemistry methods development 1
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- Parallel Computing and Optimization Techniques 1
David Lonie
11 papers receiving 8.2k citations
Hit Papers
Peers
Comparison fields: 5 of 169
- Organic Chemistry 2.1k
- Physical and Theoretical Chemistry 637
- Toxicology 191
- Computational Theory and Mathematics 796
- Materials Chemistry 2.1k
Countries citing papers authored by David Lonie
This map shows the geographic impact of David Lonie'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 David Lonie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Lonie more than expected).
Fields of papers citing papers by David Lonie
This network shows the impact of papers produced by David Lonie. 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 David Lonie. The network helps show where David Lonie may publish in the future.
Co-authorship network
The 25 scholars most cited alongside David Lonie, 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 | 2017 | 10 | |
| 2 | 2016 | 31 | |
| 3 | 2015 | 25 | |
| 4 | 2014 | 1 | |
| 5 | 2013 | 7 | |
| 6 | 2013 | 18 | |
| 7 | 2013 | 113 | |
| 8 | Avogadro: an advanced semantic chemical editor, visualization, and analysis platformbreakdown → | 2012 | 7715 |
| 9 | 2011 | 36 | |
| 10 | 2011 | 67 | |
| 11 | 2010 | 267 |
About David Lonie
David Lonie is a scholar working on Information Systems and Management, Computer Graphics and Computer-Aided Design, Computational Theory and Mathematics, Physical and Theoretical Chemistry and Hardware and Architecture, having authored 11 papers that have together received 8.3k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (4 papers), Computational Drug Discovery Methods (4 papers), Protein Structure and Dynamics (3 papers), Scientific Computing and Data Management (2 papers), Analytical Chemistry and Chromatography (1 paper), Analytical chemistry methods development (1 paper), Carbon Nanotubes in Composites (1 paper) and Parallel Computing and Optimization Techniques (1 paper). The work is most often cited by research in Organic Chemistry (2.1k citations), Physical and Theoretical Chemistry (637 citations), Toxicology (191 citations), Computational Theory and Mathematics (796 citations) and Materials Chemistry (2.1k citations). David Lonie has collaborated with scholars based in United States, India and Türkiye. Frequent co-authors include Eva Zurek, Marcus D. Hanwell, Donald Curtis, Geoffrey Hutchison, James Hooper, Zackary Falls, Andrew Shamp, Scott Simpson, Patrick Avery and Berk Geveci. Their work appears in journals such as Computer Physics Communications, Journal of Cheminformatics, Analytical Chemistry, Physical Review B and Journal of Chemical Education.
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.