Edward Kim
- Materials Chemistry top 5%
- Computational Theory and Mathematics top 2%
- Artificial Intelligence top 5%
- Molecular Biology
- Electrical and Electronic Engineering
- Co-authors
- Elsa OlivettiKevin HuangGerbrand CederAndrew McCallumAdam M. SaundersDuncan C. MacLarenSteven ClarkeJonathan D. Lowenson
- Topics
- Machine Learning in Materials Science (12 papers)X-ray Diffraction in Crystallography (5 papers)Computational Drug Discovery Methods (5 papers)
- Journals
- Proceedings of the National Academy of SciencesSHILAP Revista de lepidopterologíaChemistry of Materials
- Partner nations
- United StatesCanadaIndia
In The Last Decade
Edward Kim
26 papers receiving 1.5k citations
Peers
Comparison fields: 5 of 135
- Materials Chemistry 1.0k
- Computational Theory and Mathematics 299
- Artificial Intelligence 252
- Molecular Biology 243
- Electrical and Electronic Engineering 191
Countries citing papers authored by Edward Kim
This map shows the geographic impact of Edward Kim'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 Edward Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Edward Kim more than expected).
Fields of papers citing papers by Edward Kim
This network shows the impact of papers produced by Edward Kim. 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 Edward Kim. The network helps show where Edward Kim may publish in the future.
Co-authorship network of co-authors of Edward Kim
This figure shows the co-authorship network connecting the top 25 collaborators of Edward Kim. A scholar is included among the top collaborators of Edward Kim 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 Edward Kim. Edward Kim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 6 | |
| 4 | 2 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 0 | |
| 8 | 24 | |
| 9 | 198 | |
| 10 | 104 | |
| 11 | 3 | |
| 12 | 206 | |
| 13 | 30 | |
| 14 | 126 | |
| 15 | 146 | |
| 16 | 27 | |
| 17 | 4 | |
| 18 | 7 | |
| 19 | 2 | |
| 20 | 2 |
About Edward Kim
Edward Kim is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Catalysis, having authored 29 papers that have together received 1.5k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (12 papers), X-ray Diffraction in Crystallography (5 papers) and Computational Drug Discovery Methods (5 papers). The work is most often cited by research in Materials Chemistry (1.0k citations), Computational Theory and Mathematics (299 citations) and Catalysis (111 citations). Edward Kim has collaborated with scholars based in United States, Canada and India. Frequent co-authors include Elsa Olivetti, Kevin Huang, Gerbrand Ceder, Andrew McCallum, Adam M. Saunders, Duncan C. MacLaren, Steven Clarke, Jonathan D. Lowenson, Stephen G. Young and Stefanie Jegelka. Their work appears in journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and Chemistry of Materials.
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.