Richard Van
Impact in
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- Computational Drug Discovery Methods
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- Machine Learning in Materials Science
- Luminescence and Fluorescent Materials
Papers in
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- Protein Structure and Dynamics 5
- Receptor Mechanisms and Signaling 2
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- Neuroscience and Neuropharmacology Research 4
- Co-authors
- Yihan Shao (15 shared papers)Jingzhi Pu (5 shared papers)Xiaoliang Pan (6 shared papers)Kwangho Nam (3 shared papers)Ye Mei (3 shared papers)Evgeny Epifanovsky (2 shared papers)Junjie Yang (1 shared paper)Jing Huang (1 shared paper)
- Journals
- Journal of Medicinal Chemistry (2 papers)Proceedings of the National Academy of Sciences (2 papers)Journal of Chemical Theory and Computation (2 papers)ACS Pharmacology & Translational Science (1 paper)Molecular Pharmaceutics (1 paper)
- Partner nations
- United StatesChinaJapan
In The Last Decade
Richard Van
13 papers receiving 268 citations
Peers
Comparison fields: 5 of 73
- Computational Theory and Mathematics 65
- Materials Chemistry 133
- Molecular Biology 144
- Spectroscopy 26
- Physical and Theoretical Chemistry 14
Countries citing papers authored by Richard Van
This map shows the geographic impact of Richard Van'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 Van with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard Van more than expected).
Fields of papers citing papers by Richard Van
This network shows the impact of papers produced by Richard Van. 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 Van. The network helps show where Richard Van may publish in the future.
Co-authors
The 25 scholars most cited alongside Richard Van, 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 | 2020 | 94 | |
| 2 | 2021 | 85 | |
| 3 | 2023 | 23 | |
| 4 | 2023 | 19 | |
| 5 | 2022 | 13 | |
| 6 | 2023 | 7 | |
| 7 | 2023 | 7 | |
| 8 | 2023 | 4 | |
| 9 | 2021 | 4 | |
| 10 | 2020 | 4 | |
| 11 | 2020 | 4 | |
| 12 | 2024 | 3 | |
| 13 | 2021 | 2 | |
| 14 | 2025 | 0 | |
| 15 | 2026 | 0 | |
| 16 | 2024 | 0 |
About Richard Van
Richard Van is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience, Materials Chemistry, Computational Theory and Mathematics and Radiology, Nuclear Medicine and Imaging, having authored 16 papers that have together received 269 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (5 papers), Neuroscience and Neuropharmacology Research (4 papers), Machine Learning in Materials Science (3 papers), Nanoplatforms for cancer theranostics (2 papers), Computational Drug Discovery Methods (2 papers), Molecular Sensors and Ion Detection (2 papers), Receptor Mechanisms and Signaling (2 papers) and Optical Imaging and Spectroscopy Techniques (2 papers). The work is most often cited by research in Computational Theory and Mathematics (65 citations), Materials Chemistry (133 citations), Molecular Biology (144 citations), Spectroscopy (26 citations) and Physical and Theoretical Chemistry (14 citations). Richard Van has collaborated with scholars based in United States, China and Japan. Frequent co-authors include Yihan Shao, Jingzhi Pu, Xiaoliang Pan, Kwangho Nam, Ye Mei, Evgeny Epifanovsky, Junjie Yang, Jing Huang, Can Zhang and Biyue Zhu. Their work appears in journals such as Journal of Medicinal Chemistry, Proceedings of the National Academy of Sciences, Journal of Chemical Theory and Computation, ACS Pharmacology & Translational Science and Molecular Pharmaceutics.
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.