Richard A. Messerly
- Materials Chemistry top 10%
- Biomedical Engineering top 10%
- Fluid Flow and Transfer Processes top 5%
- Computational Theory and Mathematics top 5%
- Atomic and Molecular Physics, and Optics
- Co-authors
- Daniel R. RoeEdward J. MaginnSergei TretiakNicholas LubbersKipton BarrosJustin S. SmithBenjamin NebgenYing Wai Li
- Topics
- Machine Learning in Materials Science (15 papers)Phase Equilibria and Thermodynamics (15 papers)Computational Drug Discovery Methods (9 papers)
- Journals
- Chemical ReviewsThe Journal of Chemical PhysicsSHILAP Revista de lepidopterología
- Partner nations
- United StatesCyprusGermany
In The Last Decade
Richard A. Messerly
40 papers receiving 951 citations
Hit Papers
Peers
Comparison fields: 5 of 80
- Materials Chemistry 515
- Biomedical Engineering 338
- Fluid Flow and Transfer Processes 168
- Computational Theory and Mathematics 154
- Atomic and Molecular Physics, and Optics 152
Countries citing papers authored by Richard A. Messerly
This map shows the geographic impact of Richard A. Messerly'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 Richard A. Messerly with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard A. Messerly more than expected).
Fields of papers citing papers by Richard A. Messerly
This network shows the impact of papers produced by Richard A. Messerly. 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 Richard A. Messerly. The network helps show where Richard A. Messerly may publish in the future.
Co-authorship network of co-authors of Richard A. Messerly
This figure shows the co-authorship network connecting the top 25 collaborators of Richard A. Messerly. A scholar is included among the top collaborators of Richard A. Messerly 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 Richard A. Messerly. Richard A. Messerly is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potentialbreakdown → | 74 |
| 6 | 18 | |
| 7 | 12 | |
| 8 | 8 | |
| 9 | 87 | |
| 10 | 11 | |
| 11 | 94 | |
| 12 | 18 | |
| 13 | 14 | |
| 14 | 12 | |
| 15 | 15 | |
| 16 | 9 | |
| 17 | 211 | |
| 18 | 7 | |
| 19 | 20 | |
| 20 | 10 |
About Richard A. Messerly
Richard A. Messerly is a scholar working on Fluid Flow and Transfer Processes, Catalysis and Computational Theory and Mathematics, having authored 41 papers that have together received 969 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (15 papers), Phase Equilibria and Thermodynamics (15 papers) and Computational Drug Discovery Methods (9 papers). The work is most often cited by research in Fluid Flow and Transfer Processes (168 citations), Catalysis (88 citations) and Materials Chemistry (515 citations). Richard A. Messerly has collaborated with scholars based in United States, Cyprus and Germany. Frequent co-authors include Daniel R. Roe, Edward J. Maginn, Sergei Tretiak, Nicholas Lubbers, Kipton Barros, Justin S. Smith, Benjamin Nebgen, Ying Wai Li, Maksim Kulichenko and Monika Thol. Their work appears in journals such as Chemical Reviews, The Journal of Chemical Physics and SHILAP Revista de lepidopterología.
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.