Yunsie Chung
- Materials Chemistry
- Computational Theory and Mathematics top 2%
- Molecular Biology
- Biomedical Engineering
- Spectroscopy top 10%
- Co-authors
- William H. GreenFlorence H. VermeireHaoyang WuKevin P. GreenmanDavid GraffShih‐Cheng LiEsther HeidCharles J. McGill
- Topics
- Machine Learning in Materials Science (7 papers)Computational Drug Discovery Methods (6 papers)Chemical Thermodynamics and Molecular Structure (4 papers)
- Journals
- Journal of the American Chemical SocietyChemical Engineering JournalThe Journal of Physical Chemistry A
- Partner nations
- United StatesAustriaUnited Kingdom
In The Last Decade
Yunsie Chung
11 papers receiving 598 citations
Hit Papers
Peers
Comparison fields: 5 of 84
- Materials Chemistry 331
- Computational Theory and Mathematics 311
- Molecular Biology 137
- Biomedical Engineering 111
- Spectroscopy 103
Countries citing papers authored by Yunsie Chung
This map shows the geographic impact of Yunsie Chung'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 Yunsie Chung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yunsie Chung more than expected).
Fields of papers citing papers by Yunsie Chung
This network shows the impact of papers produced by Yunsie Chung. 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 Yunsie Chung. The network helps show where Yunsie Chung may publish in the future.
Co-authorship network of co-authors of Yunsie Chung
This figure shows the co-authorship network connecting the top 25 collaborators of Yunsie Chung. A scholar is included among the top collaborators of Yunsie Chung 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 Yunsie Chung. Yunsie Chung is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 0 | |
| 3 | 15 | |
| 4 | 2 | |
| 5 | 13 | |
| 6 | 18 | |
| 7 | Chemprop: A Machine Learning Package for Chemical Property Predictionbreakdown → | 250 |
| 8 | 130 | |
| 9 | 88 | |
| 10 | 66 | |
| 11 | 23 | |
| 12 | 1 |
About Yunsie Chung
Yunsie Chung is a scholar working on Computational Theory and Mathematics, Physical and Theoretical Chemistry and Fluid Flow and Transfer Processes, having authored 12 papers that have together received 613 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (7 papers), Computational Drug Discovery Methods (6 papers) and Chemical Thermodynamics and Molecular Structure (4 papers). The work is most often cited by research in Computational Theory and Mathematics (311 citations), Materials Chemistry (331 citations) and Spectroscopy (103 citations). Yunsie Chung has collaborated with scholars based in United States, Austria and United Kingdom. Frequent co-authors include William H. Green, Florence H. Vermeire, Haoyang Wu, Kevin P. Greenman, David Graff, Shih‐Cheng Li, Esther Heid, Charles J. McGill, Pierre J. Walker and Michael H. Abraham. Their work appears in journals such as Journal of the American Chemical Society, Chemical Engineering Journal and The Journal of Physical Chemistry A.
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.