Kevin P. Greenman

894 citations
9 papers · 488 indexed · 1 hit paper · h-index 5

Kevin P. Greenman

7 papers receiving 479 citations

Hit Papers

Chemprop: A Machine Learning Package for Chemical Propert...250202320262024202550100150200250

Peers

Kevin P. Greenman
Comparison fields: 5 of 92
  • Computational Theory and Mathematics 228
  • Materials Chemistry 292
  • Catalysis 21
  • Physical and Theoretical Chemistry 27
  • Spectroscopy 43
Replace Shih‐Cheng Li with:
Shih‐Cheng Li Taiwan
Charles J. McGill United States
Camille Bilodeau United States
Mojtaba Haghighatlari United States
Dylan M. Anstine United States
Justin Gilmer United States
Fuchun Ge China
Adam C. Mater Australia
Riccardo Petraglia Switzerland
Jinxiao Zhang China
Kevin P. Greenman relative to Shih‐Cheng Li Taiwan Shih‐Cheng Li's profile →
Citations per field
00.5×1.5×
Shih‐Cheng Li · 1×
Citations per year

Countries citing papers authored by Kevin P. Greenman

Since Specialization
Citations

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

Fields of papers citing papers by Kevin P. Greenman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kevin P. Greenman. 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 Kevin P. Greenman. The network helps show where Kevin P. Greenman may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Kevin P. Greenman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Kevin P. Greenman Line = papers co-authored together Kevin P. Greenman links everyone, so they are left out of the graph.

All Works

9 of 9 papers shown
#Work
1 20250
2 20250
3 20253
4
Chemprop: A Machine Learning Package for Chemical Property Predictionbreakdown →
2023250
5 20237
6 202389
7 202280
8 202255
9 20194

About Kevin P. Greenman

Kevin P. Greenman is a scholar working on Computational Theory and Mathematics, Biophysics and Materials Chemistry, having authored 9 papers that have together received 488 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (8 papers), Computational Drug Discovery Methods (5 papers), Protein Structure and Dynamics (2 papers), Electronic and Structural Properties of Oxides (1 paper), Acoustic Wave Resonator Technologies (1 paper), Machine Learning and Algorithms (1 paper), GaN-based semiconductor devices and materials (1 paper) and Metal and Thin Film Mechanics (1 paper). The work is most often cited by research in Computational Theory and Mathematics (228 citations), Materials Chemistry (292 citations) and Catalysis (21 citations). Kevin P. Greenman has collaborated with scholars based in United States, Taiwan and Austria. Frequent co-authors include William H. Green, Rafael Gómez‐Bombarelli, Florence H. Vermeire, Haoyang Wu, Shih‐Cheng Li, Charles J. McGill, Yunsie Chung, David Graff, Esther Heid and Daniel Schwalbe‐Koda. Their work appears in journals such as Chemical Science, npj Computational Materials, PLoS Computational Biology, Science and Journal of Applied Physics.

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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