Yuanqing Wang
- Materials Chemistry
- Molecular Biology
- Computational Theory and Mathematics top 5%
- Atomic and Molecular Physics, and Optics
- Physical and Theoretical Chemistry
- Co-authors
- John D. ChoderaPavan Kumar BeharaJoshua T. HortonYuezhi MaoJohn E. HerrDavid DotsonThomas E. MarklandGianni De Fabritiis
- Topics
- Protein Structure and Dynamics (6 papers)Machine Learning in Materials Science (6 papers)Computational Drug Discovery Methods (5 papers)
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Yuanqing Wang
12 papers receiving 253 citations
Hit Papers
Peers
Comparison fields: 5 of 58
- Materials Chemistry 160
- Molecular Biology 119
- Computational Theory and Mathematics 106
- Atomic and Molecular Physics, and Optics 21
- Physical and Theoretical Chemistry 20
Countries citing papers authored by Yuanqing Wang
This map shows the geographic impact of Yuanqing Wang'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 Yuanqing Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuanqing Wang more than expected).
Fields of papers citing papers by Yuanqing Wang
This network shows the impact of papers produced by Yuanqing Wang. 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 Yuanqing Wang. The network helps show where Yuanqing Wang may publish in the future.
Co-authorship network of co-authors of Yuanqing Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Yuanqing Wang. A scholar is included among the top collaborators of Yuanqing Wang 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 Yuanqing Wang. Yuanqing Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 10 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 2 | |
| 5 | 2 | |
| 6 | 0 | |
| 7 | 19 | |
| 8 | 22 | |
| 9 | 15 | |
| 10 | 8 | |
| 11 | SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentialsbreakdown → | 104 |
| 12 | 46 | |
| 13 | 10 | |
| 14 | 17 | |
| 15 | 0 |
About Yuanqing Wang
Yuanqing Wang is a scholar working on Computational Theory and Mathematics, Sensory Systems and Materials Chemistry, having authored 15 papers that have together received 256 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (6 papers), Machine Learning in Materials Science (6 papers) and Computational Drug Discovery Methods (5 papers). The work is most often cited by research in Computational Theory and Mathematics (106 citations), Materials Chemistry (160 citations) and Physical and Theoretical Chemistry (20 citations). Yuanqing Wang has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include John D. Chodera, Pavan Kumar Behara, Joshua T. Horton, Yuezhi Mao, John E. Herr, David Dotson, Thomas E. Markland, Gianni De Fabritiis, Raimondas Galvelis and Peter Eastman. Their work appears in journals such as The Journal of Physical Chemistry B, The Journal of Physical Chemistry A and Chemical Science.
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.