Yanfei Guan
- Materials Chemistry top 10%
- Organic Chemistry top 5%
- Computational Theory and Mathematics top 2%
- Molecular Biology
- Inorganic Chemistry top 5%
- Co-authors
- Steven E. WheelerAnalise C. DoneyRobert S. PatonTrevor J. SeguinPeter C. St. JohnSeonah KimYeonjoon KimBenjamin J. Rooks
- Topics
- Machine Learning in Materials Science (7 papers)Computational Drug Discovery Methods (6 papers)Asymmetric Hydrogenation and Catalysis (4 papers)
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Yanfei Guan
24 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 105
- Materials Chemistry 546
- Organic Chemistry 497
- Computational Theory and Mathematics 333
- Molecular Biology 259
- Inorganic Chemistry 230
Countries citing papers authored by Yanfei Guan
This map shows the geographic impact of Yanfei Guan'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 Yanfei Guan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yanfei Guan more than expected).
Fields of papers citing papers by Yanfei Guan
This network shows the impact of papers produced by Yanfei Guan. 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 Yanfei Guan. The network helps show where Yanfei Guan may publish in the future.
Co-authorship network of co-authors of Yanfei Guan
This figure shows the co-authorship network connecting the top 25 collaborators of Yanfei Guan. A scholar is included among the top collaborators of Yanfei Guan 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 Yanfei Guan. Yanfei Guan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 3 | |
| 3 | 14 | |
| 4 | 27 | |
| 5 | 1 | |
| 6 | 31 | |
| 7 | 2 | |
| 8 | 96 | |
| 9 | 56 | |
| 10 | 45 | |
| 11 | 113 | |
| 12 | Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational costbreakdown → | 238 |
| 13 | 96 | |
| 14 | 15 | |
| 15 | 9 | |
| 16 | 15 | |
| 17 | 41 | |
| 18 | Noncovalent Interactions in Organocatalysis and the Prospect of Computational Catalyst Designbreakdown → | 330 |
| 19 | 60 | |
| 20 | 17 |
About Yanfei Guan
Yanfei Guan is a scholar working on Inorganic Chemistry, Catalysis and Organic Chemistry, having authored 24 papers that have together received 1.4k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (7 papers), Computational Drug Discovery Methods (6 papers) and Asymmetric Hydrogenation and Catalysis (4 papers). The work is most often cited by research in Computational Theory and Mathematics (333 citations), Organic Chemistry (497 citations) and Inorganic Chemistry (230 citations). Yanfei Guan has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Steven E. Wheeler, Analise C. Doney, Robert S. Paton, Trevor J. Seguin, Peter C. St. John, Seonah Kim, Yeonjoon Kim, Benjamin J. Rooks, William H. Green and Chang‐Ming Dong. Their work appears in journals such as Angewandte Chemie International Edition, Nature Communications and Accounts of Chemical Research.
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.